TIW DCL

import dask_jobqueue

if "client" in locals():
    client.close()
if "cluster" in locals():
    cluster.close()

env = {"OMP_NUM_THREADS": "3", "NUMBA_NUM_THREADS": "3"}

# cluster = distributed.LocalCluster(
#    n_workers=8,
#    threads_per_worker=1,
#    env=env
# )

if "cluster" in locals():
    del cluster

cluster = dask_jobqueue.SLURMCluster(
    cores=1,
    processes=1,
    memory="25GB",
    walltime="08:00:00",
    project="NCGD0046",
    scheduler_options=dict(dashboard_address=":9797"),
)
# cluster = dask_jobqueue.PBSCluster(
#    cores=9, processes=9, memory="108GB", walltime="02:00:00", project="NCGD0043",
#    env_extra=env,
# )

cluster.scale(6)

# %load_ext autoreload
# %autoreload 2

%matplotlib inline

import dask
import distributed
import holoviews as hv
import hvplot.xarray
import matplotlib as mpl
import matplotlib.dates as mdates
import matplotlib.pyplot as plt
import matplotlib.units as munits
import numpy as np
import pandas as pd
import seawater as sw
import xarray as xr
from holoviews import opts

import cf_xarray
import dcpy
import pump

# import hvplot.xarray


# import facetgrid

mpl.rcParams["savefig.dpi"] = 300
mpl.rcParams["savefig.bbox"] = "tight"
mpl.rcParams["figure.dpi"] = 140

munits.registry[np.datetime64] = mdates.ConciseDateConverter()

xr.set_options(keep_attrs=True)


hv.extension("bokeh")
# hv.opts.defaults(opts.Image(fontscale=1.5), opts.Curve(fontscale=1.5))


print(dask.__version__)
print(distributed.__version__)
print(xr.__version__)


def unpack(d):
    val = []
    for k, v in d.items():
        if isinstance(v, dict):
            val.extend(unpack(v))
        else:
            val.append(v)
    return val


xr.DataArray([1.0])

2.23.0
2.23.0
0.16.0

<xarray.DataArray (dim_0: 1)>
array([1.])
Dimensions without coordinates: dim_0

client = distributed.Client(cluster)
client

Client Scheduler: tcp://10.12.205.13:36839 Dashboard: https://jupyterhub.ucar.edu/dav/user/dcherian/proxy/9797/status

Cluster Workers: 6 Cores: 6 Memory: 150.00 GB

from dask.cache import Cache

cache = Cache(5e9, 0.4)  # Leverage two gigabytes of memory
cache.register()  # Turn cache on globally

gcm1 = pump.Model(
    "/glade/u/home/dcherian/pump/TPOS_MITgcm_1_hb/HOLD_NC_LINKS/",
    name="gcm1",
    full=False,
    budget=False,
)
# gcm1.tao.load()
# gcm1.tao = xr.merge([gcm1.tao, pump.calc.get_tiw_phase(gcm1.tao.v)])
# gcm1.surface["zeta"] = xr.open_dataset(gcm1.dirname + "obs_subset/surface_zeta.nc").zeta
# gcm1.surface = gcm1.surface.chunk({"time": 1})
# gcm1.surface["zeta"] = gcm1.surface.v.differentiate("longitude") - gcm1.surface.u.differentiate("latitude")

calc uz
calc vz
calc S2
calc N2
calc shred2
Calc Ri

gcm1.tao["mld"] = pump.calc.get_mld(gcm1.tao.dens)
gcm1.tao["dcl"] = pump.calc.get_dcl_base_Ri(gcm1.tao)

read data

model = gcm1
periods = np.arange(1, 9)
longitudes = [-110]  # -140, -155]
tslice = slice(None, "1996-03-01")
subset_fields = [
    "Jq",
    "theta",
    "salt",
    "dens",
    "u",
    "v",
    "w",
    "KPP_diffusivity",
    "sst",
    "Ri",
]
anomalize_fields = ["u", "v", "w", "theta", "salt", "sst"]
anomalize_over = ["time"]

import xfilter

surf = (
    model.surface.sel(longitude=longitudes, method="nearest")
    .assign_coords(longitude=longitudes)
    .drop("depth")
)
tao = model.tao.sel(longitude=longitudes, latitude=0).chunk({"longitude": 1})
# tao["time"] = model.full.time

sst = surf.theta.sel(longitude=longitudes).chunk({"longitude": 1, "time": -1})
sstfilt = pump.calc.tiw_avg_filter_sst(sst, "bandpass")  # .persist()

# filter out daily cycle
subdaily_sst = xfilter.lowpass(
    surf.theta.chunk({"time": -1, "latitude": 10}),
    coord="time",
    freq=2,
    cycles_per="D",
).reindex(time=tao.time)
subdaily_sst.attrs["long_name"] = "2 day filtered SST"
subdaily_sst.attrs["units"] = "C"

# surf["theta"] = (
#    sst.pipe(xfilter.lowpass, coord="time", freq=1/7, cycles_per="D", num_discard=0, method="pad")
# )

Tx = model.surface.theta.differentiate("longitude")
Ty = model.surface.theta.differentiate("latitude")
gradT = (
    np.hypot(Tx, Ty)
    .sel(longitude=longitudes, method="nearest")
    .assign_coords(longitude=longitudes)
    .drop_vars("depth")
)

# with performance_report("profiles/phase-period.html"):
tiw_phase, period, tiw_ptp = dask.compute(
    pump.calc.get_tiw_phase_sst(
        sstfilt.chunk({"longitude": 1}), gradT.chunk({"longitude": 1, "time": -1})
    ),
)[0]

full = pump.utils.read_gcm1_zarr_subset(gcm1)

# needed for apply_ufunc later
valid_subset_fields = list(set(subset_fields).intersection(set(full.data_vars)))
full_subset = full[valid_subset_fields]

full_subset = pump.calc_reduced_shear(
    full_subset.sel(depth=slice(0, -200))
    .sel(longitude=longitudes)
    .sel(time=tslice)
    .chunk({"longitude": 1, "latitude": -1, "time": 36})
    .assign_coords(
        period=period,
        tiw_phase=tiw_phase,
    )
)
# full_subset["uz"] = full_subset.u.differentiate("depth")
# full_subset["vz"] = full_subset.v.differentiate("depth")
# full_subset["S2"] = full_subset.uz ** 2 + full_subset.vz ** 2
# full_subset["N2"] = -9.81/1025 * full_subset.dens.differentiate("depth")
# full_subset["Ri"] = full_subset.N2 / full_subset.S2
full_subset["sst"] = surf.theta.sel(
    time=full_subset.time
)  # full_subset.theta.isel(depth=0)
# if "zeta" in surf:
#    full["ssvor"] = surf.zeta

euc_max = tao.euc_max.reset_coords(drop=True)
mld = pump.calc.get_mld(full_subset.dens)
dcl_base = pump.calc.get_dcl_base_Ri(full_subset, mld, euc_max)


full_subset = full_subset.assign_coords(mld=mld, dcl_base=dcl_base, eucmax=tao.euc_max)

single TIW period analysis

Paritioning mixing within KPP interior & KPP BL

Jq = xr.Dataset()
Jq["Total DCL"] = period4.Jq.where(period4.dcl_mask)
Jq["Within KPP BL"] = period4.Jq.where(
    period4.dcl_mask & (period4.depth > -period4.KPPhbl)
)
Jq["Below KPP BL"] = period4.Jq.where(
    period4.dcl_mask & (period4.depth <= -period4.KPPhbl)
)
Jq = Jq.isel(longitude=1).to_array().load()

(
    Jq.mean("depth").plot(
        col="variable", robust=True, x="time", **pump.plot.cmaps["Jq"], ylim=(-2, 5)
    )
)

<xarray.plot.facetgrid.FacetGrid at 0x2b559b58c820>

f, ax = plt.subplots(1, 1, constrained_layout=1)
(
    Jq.fillna(0)
    .integrate("time", datetime_unit="s")
    .integrate("depth")
    .plot.line(
        hue="variable",
        y="latitude",
        ylim=(-3, 5),
        xlim=(0, None),
        add_legend=False,
        ax=ax,
    )
)

ax.legend(Jq["variable"].values, loc="upper right")
ax.grid(True, which="both")
ax.xaxis.set_minor_locator(mpl.ticker.AutoMinorLocator(3))
ax.set_xlabel("$∫dt∫dz$ $J_q$ [J/m]")
ax.set_title("Parameterized heat flux \nin low Ri layer at 110°W")

f.set_size_inches((dcpy.plots.pub_fig_width("jpo", "single column"), 4))

f.savefig("images/kpp-deep-cycle-bl-int-heat-flux.png")

Reduced shear analysis

pump.plot.plot_tiw_period_snapshots(
    full_subset,
    lon=-110,
    period=4,
    times=[
        "1995-11-18 00:00",
        # "1995-11-24 18:00",
        "1995-11-26 06:00",
        "1995-12-03 18:00",
        # "1995-12-09 18:00",
    ],
)

plt.gcf().savefig("images/sst-dcl-shred2.png", dpi=150)

/glade/u/home/dcherian/python/xarray/xarray/core/resample.py:176: FutureWarning: This DataArray contains multi-dimensional coordinates. In the future, the dimension order of these coordinates will be restored as well unless you specify restore_coord_dims=False.
  super().__init__(*args, **kwargs)

shred2 = (
    (full_subset.uz**2 + full_subset.vz**2 - 1 / 0.4 * full_subset.N2).sel(
        time="1995-12-03 20:00", longitude=-110
    )
).compute()

are we seeing a deep cycle off the equator? Yes!

Jq

sub = full_subset.sel(longitude=-110, latitude=3.5, method="nearest").sel(
    time=slice("1995-11-29", "1995-12-05")
)

sub.Jq.rolling(depth=3, center=True).mean().plot(
    y="depth",
    cmap=mpl.cm.Blues_r,
    vmax=0,
    vmin=-400,
)
sub.mld.plot(color="orange", x="time")
sub.dcl_base.plot(color="k", x="time")

[<matplotlib.lines.Line2D at 0x2ba424ec7860>]

deep cycle summary

Jq, N2, S2, Ri

ds = (
    full_subset[["Ri", "S2", "N2", "Jq"]]
    .sel(latitude=2, method="nearest")
    .sel(time=slice("1995-11-29", "1995-12-09"), depth=slice(-100))
    .assign_coords(latitude=2)
    .squeeze()
    .compute()
)
ds["Jq"] = ds.Jq.rolling(depth=2).mean()
del ds.time.attrs["long_name"]
ds.depth.attrs["units"] = "m"
ds.Jq.attrs["long_name"] = "$J^t_q$"
ds.Jq.attrs["units"] = "W/m²"
ds.S2.attrs["long_name"] = "$S²$"
ds.S2.attrs["units"] = "$s^{-2}$"
ds.Ri.attrs["long_name"] = "$Ri$"
ds.N2.attrs["long_name"] = "$N²$"
ds.N2.attrs["units"] = "$s^{-2}$"

f, axx = plt.subplots(4, 1, sharex=True, sharey=True, constrained_layout=True)
ax = dict(zip(["Ri", "S2", "N2", "Jq"], axx))

kwargs = {
    "S2": dict(cmap=mpl.cm.OrRd, norm=mpl.colors.LogNorm(1e-5, 1e-3)),
    "Jq": dict(
        cmap=mpl.cm.Blues_r,
        vmax=0,
        vmin=-400,
    ),
    "Ri": dict(cmap=mpl.cm.RdYlBu_r, center=0.5, norm=mpl.colors.LogNorm(0.25, 1)),
    "N2": dict(cmap=mpl.cm.GnBu, norm=mpl.colors.LogNorm(1e-6, 3e-4)),
}

for var in kwargs.keys():
    ds[var].plot(ax=ax[var], x="time", y="depth", **kwargs[var])


def plot(ax):
    ds.mld.plot(ax=ax, color="w", x="time", _labels=False, lw=2)
    ds.mld.plot(ax=ax, color="orange", x="time", _labels=False, lw=1)
    ds.dcl_base.plot(ax=ax, color="w", x="time", _labels=False, lw=2)
    ds.dcl_base.plot(ax=ax, color="k", x="time", _labels=False, lw=1)


[plot(aa) for aa in axx]
[aa.set_xlabel("") for aa in axx]
[aa.set_title("") for aa in axx[1:]]
[tt.set_rotation(0) for tt in axx[-1].get_xticklabels()]

f.set_size_inches((6, 6))

# f.savefig("images/110-deep-cycle-2.png")

Deep cycle forcing: shear, velocity & vorticity

subset = (
    full_subset[["u", "v"]].sel(
        longitude=-110,
        latitude=slice(-3, 6),
        time=(full_subset.period == 4).squeeze().reset_coords(drop=True),
    )
).compute()

ssta4 = (
    gcm1.surface.theta.sel(time=full_subset.time)
    .sel(
        longitude=slice(-140, -100),
        time=(full_subset.period == 4).squeeze().reset_coords(drop=True),
    )
    .pipe(lambda x: x - x.mean(["longitude", "time"]))
    .reset_coords(drop=True)
)

ssta4.isel(time=slice(None, None, 20)).plot(
    x="longitude", y="latitude", col="time", col_wrap=3, robust=True, aspect=2.5
)

<xarray.plot.facetgrid.FacetGrid at 0x2b3a768f65f8>

propagation velocity from hovmoeller plots

Using 0.5m/s barotropic shift in u works OK. This is inferred from SST, SSV plots.

SST

fg = (
    ssta4.sel(longitude=slice(-120, -100))
    .sel(latitude=[0, 1, 2, 2.5, 3, 3.5, 4], method="nearest")
    .sortby("latitude", ascending=False)
    .plot(
        x="longitude",
        y="time",
        col="latitude",
        col_wrap=2,
        robust=True,
        aspect=3,
        cbar_kwargs={"orientation": "horizontal", "shrink": 0.6, "aspect": 40},
    )
)


[plot_reference(ax) for ax in fg.axes.flat]
fg.fig.suptitle("SST hovmoeller plots with -0.6, -0.5, -0.4 m/s slope lines", y=1.01)

Text(0.5, 1.01, 'SST hovmoeller plots with -0.6, -0.5, -0.4 m/s slope lines')

SSV

ssv = (
    gcm1.surface.v.sel(time=full_subset.time)
    .sel(
        longitude=slice(-140, -100),
        time=(full_subset.period == 4).squeeze().reset_coords(drop=True),
    )
    .reset_coords(drop=True)
)

fg = (
    ssv.sel(longitude=slice(-120, -100))
    .sel(latitude=[0, 1, 2, 2.5, 3, 3.5, 4], method="nearest")
    .sortby("latitude", ascending=False)
    .plot(
        x="longitude",
        y="time",
        col="latitude",
        col_wrap=2,
        robust=True,
        aspect=3,
        cbar_kwargs={"orientation": "horizontal", "shrink": 0.6, "aspect": 40},
    )
)

[plot_reference(ax) for ax in fg.axes.flat]
fg.fig.suptitle("SSV hovmoeller plots with -0.6, -0.5, -0.4 m/s slope lines", y=1.01)

Text(0.5, 1.01, 'SSV hovmoeller plots with -0.6, -0.5, -0.4 m/s slope lines')

depth-averaged velocity?

time4 = full_subset.time.where((full_subset.period == 4).squeeze().reset_coords(drop=True), drop=True)

fg = (
    gcm1.full.v
    .sel(depth=slice(-60))
    .mean("depth")
    .sel(longitude=slice(-120, -100), time=time4)
    .sel(latitude=[4, 3.5, 3, 2.5, 2, 1, 0], method="nearest")
    .plot(x="longitude", y="time", col="latitude", col_wrap=2, robust=True, aspect=3, 
          cbar_kwargs={"orientation": "horizontal", "shrink": 0.6, "aspect": 40})

)

[plot_reference(ax) for ax in fg.axes.flat]
fg.fig.suptitle("[0-60m] mean V hovmoeller plots with -0.6, -0.5, -0.4 m/s slope lines", y=1.01);;;;;;;;

velocity in vortex-following frame

Kennan & Flament find 0.35 m/s north of eq and 0.8m/s at eq. so there’s definitely some latitudinal structure

fg = quiver(
    (subset.assign(u=lambda x: x["u"] + 0.5))
    .sel(depth=[0, -30, -60, -90], method="nearest")
    .isel(latitude=slice(None, None, 5), time=slice(None, None, 6))
    .reset_coords(drop=True),
    row="depth",
    x="time",
    y="latitude",
    u="u",
    v="v",
    scale=10,
)

fg.fig.suptitle("Velocity vectors in \n -0.5m/s reference frame", y=1.03);;;;;;;;

refvel = xr.concat(
    [
        subset.assign(u=lambda x: x["u"] + 0.4),
        subset.assign(u=lambda x: x["u"] + 0.5),
        subset.assign(u=lambda x: x["u"] + 0.6),
    ],
    dim=xr.Variable("uref", [-0.4, -0.5, -0.6]),
)
refvel

<xarray.Dataset>
Dimensions:    (depth: 200, latitude: 180, time: 143, uref: 3)
Coordinates:
    mld        (time, latitude) float32 -23.5 -24.5 -22.5 ... -35.5 -31.5 -28.5
    eucmax     (time) float64 -91.5 -88.5 -86.5 -85.5 ... -80.5 -78.5 -77.5
    tiw_phase  (time) float64 0.0 2.571 5.143 7.714 ... 350.3 352.7 355.1 357.6
    dcl_base   (time, latitude) float32 -48.5 -49.5 -47.5 ... -35.5 -31.5 -28.5
    longitude  int64 -110
    period     (time) float64 4.0 4.0 4.0 4.0 4.0 4.0 ... 4.0 4.0 4.0 4.0 4.0
  * time       (time) datetime64[ns] 1995-11-16T16:00:00 ... 1995-12-10T08:00:00
  * latitude   (latitude) float32 -2.981211 -2.9311066 ... 5.9373693 5.987474
  * depth      (depth) float32 -0.5 -1.5 -2.5 -3.5 ... -197.5 -198.5 -199.5
  * uref       (uref) float64 -0.4 -0.5 -0.6
Data variables:
    u          (uref, time, depth, latitude) float32 0.1987586 ... 0.64383435
    v          (uref, time, depth, latitude) float32 -0.06828315 ... -0.027467424
Attributes:
    easting:            longitude
    field_julian_date:  400296
    julian_day_unit:    days since 1950-01-01 00:00:00
    northing:           latitude
    title:              daily snapshot from 1/20 degree Equatorial Pacific MI...

fg = quiver(
    refvel.sel(depth=slice(-60)).mean("depth")
    .isel(latitude=slice(None, None, 5), time=slice(None, None, 6))
    .reset_coords(drop=True),
    col="uref",
    x="time",
    y="latitude",
    u="u",
    v="v",
    scale=10,
)

fg.fig.suptitle("[0-60m] avg. velocity vectors in reference frame", y=1.03);
[dcpy.plots.concise_date_formatter(aa) for aa in fg.axes.flat]
[[aa.set_xlabel("") for aa in fg.axes.flat]];;;;;;;;

[[Text(0.5, 5.811111111111111, ''),
  Text(0.5, 5.811111111111111, ''),
  Text(0.5, 5.811111111111111, '')]]

bandpass filtering

Not sure this does what i want.

Let’s try bandpass filtering in time. This will be wrong at the front but sensible behind it?

vel = (
    full_subset[["u", "v"]]
    .squeeze()
    .sel(depth=[0, -30, -60, -90], method="nearest")
    .chunk({"time": -1, "depth": 1})
)

tiw_filter = lambda x: xfilter.bandpass(
    x, coord="time", freq=[1 / 20.0, 1 / 40.0], cycles_per="D", num_discard=0
)
filtered = vel.map(tiw_filter)

/glade/u/home/dcherian/python/xfilter/xfilter/xfilter.py:152: FutureWarning: This DataArray contains multi-dimensional coordinates. In the future, these coordinates will be transposed as well unless you specify transpose_coords=False.
  data = data.copy().transpose(..., coord)
/glade/u/home/dcherian/miniconda3/envs/dcpy/lib/python3.6/site-packages/xarray/core/missing.py:410: FutureWarning: This DataArray contains multi-dimensional coordinates. In the future, these coordinates will be transposed as well unless you specify transpose_coords=False.
  ).transpose(*arr.dims)
/glade/u/home/dcherian/miniconda3/envs/dcpy/lib/python3.6/site-packages/xarray/core/missing.py:427: FutureWarning: This DataArray contains multi-dimensional coordinates. In the future, these coordinates will be transposed as well unless you specify transpose_coords=False.
  ).transpose(*arr.dims)
/glade/u/home/dcherian/miniconda3/envs/dcpy/lib/python3.6/site-packages/xarray/core/missing.py:346: FutureWarning: This DataArray contains multi-dimensional coordinates. In the future, these coordinates will be transposed as well unless you specify transpose_coords=False.
  ).transpose(*self.dims)
/glade/u/home/dcherian/python/xfilter/xfilter/xfilter.py:152: FutureWarning: This DataArray contains multi-dimensional coordinates. In the future, these coordinates will be transposed as well unless you specify transpose_coords=False.
  data = data.copy().transpose(..., coord)
/glade/u/home/dcherian/miniconda3/envs/dcpy/lib/python3.6/site-packages/xarray/core/missing.py:410: FutureWarning: This DataArray contains multi-dimensional coordinates. In the future, these coordinates will be transposed as well unless you specify transpose_coords=False.
  ).transpose(*arr.dims)
/glade/u/home/dcherian/miniconda3/envs/dcpy/lib/python3.6/site-packages/xarray/core/missing.py:427: FutureWarning: This DataArray contains multi-dimensional coordinates. In the future, these coordinates will be transposed as well unless you specify transpose_coords=False.
  ).transpose(*arr.dims)
/glade/u/home/dcherian/miniconda3/envs/dcpy/lib/python3.6/site-packages/xarray/core/missing.py:346: FutureWarning: This DataArray contains multi-dimensional coordinates. In the future, these coordinates will be transposed as well unless you specify transpose_coords=False.
  ).transpose(*self.dims)

merged = xr.concat(
    [vel, filtered.reset_coords(drop=True)],
    dim=xr.Variable("kind", ["unfiltered", "filtered"]),
    coords="minimal",
)
merged_subset = (
    merged.sel(
        time=merged.time.where(filtered.period == 4, drop=True), latitude=slice(-3, 6)
    )
    .isel(latitude=slice(None, None, 5), time=slice(None, None, 5))
    .compute()
)

ERROR:root:Internal Python error in the inspect module.
Below is the traceback from this internal error.

Traceback (most recent call last):
  File "/glade/u/home/dcherian/miniconda3/envs/dcpy/lib/python3.6/site-packages/IPython/core/interactiveshell.py", line 3331, in run_code
    exec(code_obj, self.user_global_ns, self.user_ns)
  File "<ipython-input-171-5c50a822dbc5>", line 8, in <module>
    .sel(time=merged.time.where(filtered.period == 4, drop=True), latitude=slice(-3, 6))
  File "/glade/u/home/dcherian/miniconda3/envs/dcpy/lib/python3.6/site-packages/xarray/core/common.py", line 1141, in where
    nonzeros = zip(clipcond.dims, np.nonzero(clipcond.values))
  File "/glade/u/home/dcherian/miniconda3/envs/dcpy/lib/python3.6/site-packages/xarray/core/dataarray.py", line 557, in values
    return self.variable.values
  File "/glade/u/home/dcherian/miniconda3/envs/dcpy/lib/python3.6/site-packages/xarray/core/variable.py", line 451, in values
    return _as_array_or_item(self._data)
  File "/glade/u/home/dcherian/miniconda3/envs/dcpy/lib/python3.6/site-packages/xarray/core/variable.py", line 254, in _as_array_or_item
    data = np.asarray(data)
  File "/glade/u/home/dcherian/miniconda3/envs/dcpy/lib/python3.6/site-packages/numpy/core/_asarray.py", line 85, in asarray
    return array(a, dtype, copy=False, order=order)
  File "/glade/u/home/dcherian/miniconda3/envs/dcpy/lib/python3.6/site-packages/dask/array/core.py", line 1342, in __array__
    x = self.compute()
  File "/glade/u/home/dcherian/miniconda3/envs/dcpy/lib/python3.6/site-packages/dask/base.py", line 166, in compute
    (result,) = compute(self, traverse=False, **kwargs)
  File "/glade/u/home/dcherian/miniconda3/envs/dcpy/lib/python3.6/site-packages/dask/base.py", line 437, in compute
    results = schedule(dsk, keys, **kwargs)
  File "/glade/u/home/dcherian/miniconda3/envs/dcpy/lib/python3.6/site-packages/distributed/client.py", line 2595, in get
    results = self.gather(packed, asynchronous=asynchronous, direct=direct)
  File "/glade/u/home/dcherian/miniconda3/envs/dcpy/lib/python3.6/site-packages/distributed/client.py", line 1893, in gather
    asynchronous=asynchronous,
  File "/glade/u/home/dcherian/miniconda3/envs/dcpy/lib/python3.6/site-packages/distributed/client.py", line 780, in sync
    self.loop, func, *args, callback_timeout=callback_timeout, **kwargs
  File "/glade/u/home/dcherian/miniconda3/envs/dcpy/lib/python3.6/site-packages/distributed/utils.py", line 345, in sync
    e.wait(10)
  File "/glade/u/home/dcherian/miniconda3/envs/dcpy/lib/python3.6/threading.py", line 551, in wait
    signaled = self._cond.wait(timeout)
  File "/glade/u/home/dcherian/miniconda3/envs/dcpy/lib/python3.6/threading.py", line 299, in wait
    gotit = waiter.acquire(True, timeout)
KeyboardInterrupt

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/glade/u/home/dcherian/miniconda3/envs/dcpy/lib/python3.6/site-packages/IPython/core/interactiveshell.py", line 2044, in showtraceback
    stb = value._render_traceback_()
AttributeError: 'KeyboardInterrupt' object has no attribute '_render_traceback_'

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/glade/u/home/dcherian/miniconda3/envs/dcpy/lib/python3.6/site-packages/IPython/core/ultratb.py", line 1148, in get_records
    return _fixed_getinnerframes(etb, number_of_lines_of_context, tb_offset)
  File "/glade/u/home/dcherian/miniconda3/envs/dcpy/lib/python3.6/site-packages/IPython/core/ultratb.py", line 316, in wrapped
    return f(*args, **kwargs)
  File "/glade/u/home/dcherian/miniconda3/envs/dcpy/lib/python3.6/site-packages/IPython/core/ultratb.py", line 350, in _fixed_getinnerframes
    records = fix_frame_records_filenames(inspect.getinnerframes(etb, context))
  File "/glade/u/home/dcherian/miniconda3/envs/dcpy/lib/python3.6/inspect.py", line 1490, in getinnerframes
    frameinfo = (tb.tb_frame,) + getframeinfo(tb, context)
  File "/glade/u/home/dcherian/miniconda3/envs/dcpy/lib/python3.6/inspect.py", line 1452, in getframeinfo
    lines, lnum = findsource(frame)
  File "/glade/u/home/dcherian/miniconda3/envs/dcpy/lib/python3.6/site-packages/IPython/core/ultratb.py", line 182, in findsource
    lines = linecache.getlines(file, globals_dict)
  File "/glade/u/home/dcherian/miniconda3/envs/dcpy/lib/python3.6/linecache.py", line 47, in getlines
    return updatecache(filename, module_globals)
  File "/glade/u/home/dcherian/miniconda3/envs/dcpy/lib/python3.6/linecache.py", line 136, in updatecache
    with tokenize.open(fullname) as fp:
  File "/glade/u/home/dcherian/miniconda3/envs/dcpy/lib/python3.6/tokenize.py", line 452, in open
    buffer = _builtin_open(filename, 'rb')
KeyboardInterrupt

---------------------------------------------------------------------------

quiver(
    merged_subset,
    x="time",
    y="latitude",
    u="u",
    v="v",
    row="depth",
    col="kind",
    scale=8,
)

quiver(
    subset.sel(depth=[0, -30, -60, -90], method="nearest")
    .isel(latitude=slice(None, None, 5), time=slice(None, None, 6))
    .reset_coords(drop=True),
    row="depth",
    x="time",
    y="latitude",
    u="u",
    v="v",
)

dcl = (
    (subset.mld - subset.dcl_base)
    .resample(time="D")
    .mean()
    .rolling(latitude=6)
    .mean()
    .compute()
)

/glade/u/home/dcherian/miniconda3/envs/dcpy/lib/python3.6/site-packages/xarray/core/resample.py:176: FutureWarning: This DataArray contains multi-dimensional coordinates. In the future, the dimension order of these coordinates will be restored as well unless you specify restore_coord_dims=False.
  super().__init__(*args, **kwargs)

shears = subset.differentiate("depth").to_array("shear").reset_coords(drop=True)
shears.name = "shear"
shears.attrs["units"] = "1/s"

fg = shears.sel(depth=[-30, -45, -60, -75, -90], method="nearest").plot(
    col="depth",
    row="shear",
    x="time",
    y="latitude",
    robust=True,
    cbar_kwargs={"orientation": "horizontal", "pad": 0.2, "shrink": 0.8, "aspect": 40},
)


def plot():
    dcl.plot.contour(
        levels=7, colors="k", robust=True, x="time", add_labels=False, linewidths=1
    )


fg.map(plot)
fg.set_xlabels("")
fg.fig.set_size_inches((10, 8))

times = pd.to_datetime(["1995-11-18 00:00", "1995-11-26 04:00", "1995-12-03 16:00"])

shears.sel(time=times).sel(depth=slice(-100)).plot(col="time", row="shear", robust=True)
full_subset[["u", "v"]].to_array("vel").sel(
    time=times, latitude=slice(-3, 6), depth=slice(-100)
).plot(col="time", row="vel", robust=True)

<xarray.plot.facetgrid.FacetGrid at 0x2abce7ac7128>

fg = shears.sel(depth=slice(-60)).mean("depth").plot(col="shear", x="time", robust=True)


def plot():
    dcl.plot.contour(
        levels=7, colors="k", robust=True, x="time", add_labels=False, linewidths=1
    )


fg.map(plot)

<xarray.plot.facetgrid.FacetGrid at 0x2abc7cd550f0>

shear evolution

\begin{align} D_t u_z &= u_z v_y + (f - u_y) v_z - b_x + ∂_z F_x \ D_t v_z &= v_z u_x - (f + v_x) u_z - b_y + ∂_z F_y \end{align}

ilon = gcmfull.full.indexes["longitude"].get_loc(-110, method="nearest")
time_subset = gcmfull.time.where(full_subset.period.squeeze() == 4, drop=True).values
ds = gcmfull.full.isel(longitude=[ilon - 1, ilon, ilon + 1]).sel(
    time=time_subset, depth=slice(-200)
)
# ds = dcpy.dask.map_copy(ds)

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-5-d89d001597c4> in <module>
----> 1 ilon = gcmfull.full.indexes["longitude"].get_loc(-110, method="nearest")
      2 time_subset = gcmfull.time.where(full_subset.period.squeeze() == 4, drop=True).values
      3 ds = gcmfull.full.isel(longitude=[ilon-1, ilon, ilon+1]).sel(time=time_subset, depth=slice(-200))
      4 # ds = dcpy.dask.map_copy(ds)

NameError: name 'gcmfull' is not defined

ds.to_netcdf("/glade/scratch/dcherian/TPOS_MITgcm_1_hb/110w-period-4.nc")

from pump.calc import ddx, ddy
from pump.plot import plot_shear_terms

ds = xr.open_dataset("/glade/scratch/dcherian/TPOS_MITgcm_1_hb/110w-period-4.nc")

duzdt, dvzdt = dask.compute(pump.calc.estimate_shear_evolution_terms(ds))

Tilting of the vertical vortex tube is what changes both components of shear so lets look at that

f = dcpy.oceans.coriolis(ds.latitude)

vvort = xr.Dataset()
vvort["zeta"] = ddx(ds.v) - ddy(ds.u)
vvort["vx"] = ddx(ds.v)
vvort["uy"] = ddy(ds.u)
vvort["xcomp"] = f - vvort.uy
vvort["ycomp"] = f + vvort.vx
vvort = vvort.isel(longitude=1).load()

(vvort[["vx", "uy"]].to_array("vort") / np.abs(f)).sel(
    latitude=slice(-3, 6), depth=slice(-60)
).mean("depth").plot(
    col="vort",
    x="time",
    vmin=-1,
    vmax=1,
    cmap=mpl.cm.RdBu_r,
    cbar_kwargs={"label": "vx/|f|; uy/|f|"},
)

<xarray.plot.facetgrid.FacetGrid at 0x2ba563a03cf8>

(f + vvort.zeta).isel(depth=1).sel(latitude=slice(-3, 6)).plot(x="time", robust=True)

<matplotlib.collections.QuadMesh at 0x2b16b6c32fd0>

vvort[["xcomp", "ycomp"]].sel(depth=slice(-60), latitude=slice(-3, 6)).mean(
    "depth"
).to_array("comp").plot(col="comp", robust=True)

<xarray.plot.facetgrid.FacetGrid at 0x2b3cb8ec62e8>

depth slices shear evolution terms

large advection at the surface

plot_shear_terms(duzdt.sel(depth=[-20, -40, -60], method="nearest"), dcl)
plot_shear_terms(dvzdt.sel(depth=[-20, -40, -60], method="nearest"), dcl)

depth averaged shear evolution terms

plot_shear_terms(duzdt.sel(depth=slice(-60)).mean("depth", keep_attrs=True), dcl)
plot_shear_terms(dvzdt.sel(depth=slice(-60)).mean("depth", keep_attrs=True), dcl)

plot_shear_terms(duzdt.sel(depth=slice(-90)).mean("depth", keep_attrs=True))
plot_shear_terms(dvzdt.sel(depth=slice(-90)).mean("depth", keep_attrs=True))

vertical and horizontal vorticity

\[ \hat{ω} = [(w_y - v_z), (u_z - w_x), (v_x - u_y)] \]

vort = xr.Dataset()
vort["x"] = ddy(ds.w) - ds.v.differentiate("depth")
vort["y"] = ds.u.differentiate("depth") - ddx(ds.w)
vort["z"] = ddx(ds.v) - ddy(ds.u)
vort["vx"] = ddx(ds.v)
vort["uy"] = ddy(ds.u)
vort = vort.isel(longitude=1).load()

subset = (
    vort.sel(depth=(-20, -40, -60), method="nearest")
    .sel(latitude=slice(-2, 5))
    .isel(latitude=slice(None, None, 5), time=slice(None, None, 5))
)

fg = quiver(
    subset,
    u="x",
    v="y",
    x="time",
    y="latitude",
    col="depth",
    scale=0.4,
)


uvsub = (
    ds.isel(longitude=1)[["u", "v"]]
    # .differentiate("depth")
    .sel(latitude=slice(-2, 5)).isel(
        latitude=slice(None, None, 6), time=slice(None, None, 6)
    )
).load()

for loc, ax in zip(fg.name_dicts.flat, fg.axes.flat):
    quiver(
        uvsub.sel(loc).load(),
        u="u",
        v="v",
        x="time",
        y="latitude",
        scale=10,
        ax=ax,
        color="r",
    )

subset = vort.sel(depth=(-20, -40, -60), method="nearest").sel(latitude=slice(-2, 5))

fg = subset.z.sel(depth=(-20, -40, -60), method="nearest").plot(
    x="time", col="depth", vmax=2.5e-5, cbar_kwargs={"label": "vertical vorticity"}
)
subset.time.attrs["name"] = ""

for loc, ax in zip(fg.name_dicts.flat, fg.axes.flat):
    quiver(
        subset.isel(latitude=slice(None, None, 5), time=slice(None, None, 5))
        .sel(loc)
        .load(),
        u="x",
        v="y",
        x="time",
        y="latitude",
        scale=0.3,
        ax=ax,
        color="k",
    )

fg.set_xlabels("")
fg.fig.suptitle(
    "Horizontal vortex vectors[$ω_xi + ω_yj$] over vertical vorticity [color]", y=1.03
)
plt.gcf().savefig("images/110-period-4-horizontal-vertical-vorticity-depth.png")

subset = vort.sel(latitude=slice(-2, 6), depth=slice(0, -60)).mean("depth")
f0 = f.reindex_like(subset.z)

subset.z.attrs["long_name"] = "vert vorticity"
((f0 + subset.vx - subset.uy)).plot(x="time", vmax=3e-5)
(
    ds.theta.isel(depth=0, longitude=1)
    .resample(time="D")
    .mean()
    .plot.contour(x="time", colors="k", levels=[23.8], zorder=2, add_labels=False)
)
quiver(
    subset.isel(latitude=slice(None, None, 4), time=slice(None, None, 4)),
    u="x",
    v="y",
    x="time",
    y="latitude",
    scale=0.3,
)

quiver(
    ds.sel(depth=-30, method="nearest").isel(
        longitude=1, latitude=slice(None, None, 4), time=slice(None, None, 6)
    ),
    u="u",
    v="v",
    x="time",
    y="latitude",
    color="darkgreen",
    scale=12,
)


plt.gcf().suptitle(
    "Horizontal vorticity vectors over ($f+v_x - u_y$) [color].\n Depth average to 60m",
    y=1.05,
)

# plt.gcf().savefig("images/110-period-4-horizontal-vertical-vorticity.png")

Text(0.5, 1.05, 'Horizontal vorticity vectors over ($f+v_x - u_y$) [color].\n Depth average to 60m')

quiver(subset, x="time", y="latitude", u="u", v="v", scale=10)

subset = (
    ds.isel(longitude=1)
    .sel(
        latitude=slice(
            -2,
            6,
        ),
        depth=slice(-20, -60),
    )
    .isel(latitude=slice(0, -1, 2))
    .mean("depth")
    .resample(time="D")
    .mean()
)

t = (subset.time - subset.time[0]).values.astype("timedelta64[s]").astype("float32")

x = t * 1200e3 / 25 / 86400

y = subset.latitude  # np.linspace(-2, 6, 100)

start_points = [x[-4] * np.ones_like(y), y]
start_points = np.stack(start_points).T

plt.streamplot(
    x,
    y,
    subset.u.transpose().values,
    subset.v.transpose().values,
    integration_direction="forward",
    start_points=start_points,
)

older plots

plot_shear_terms(duzdt.sel(depth=[-25, -50, -75], method="nearest"))

plot_shear_terms(dvzdt.sel(depth=[-25, -50, -75], method="nearest"))

particle tracking

from datetime import timedelta as delta

import numpy as np
import xarray as xr

from parcels import (
    AdvectionRK4,
    FieldSet,
    JITParticle,
    ParticleSet,
    plotTrajectoriesFile,
)

variables = {"U": "u", "V": "v"}
dimensions = {"lon": "longitude", "lat": "latitude", "time": "time"}
fset = FieldSet.from_xarray_dataset(gcm1.surface, variables, dimensions)
# fset.add_periodic_halo(zonal=True, meridional=False, halosize=2)

fset

WARNING: Casting depth data to np.float32
WARNING:parcels.tools.loggers:Casting depth data to np.float32

<parcels.fieldset.FieldSet at 0x2b3a5b619b70>

lons, lats = np.meshgrid(np.arange(-120.0, -105.0, 0.2), np.arange(-2, 8, 0.5))
pset = ParticleSet(fset, JITParticle, lon=lons, lat=lats)
ofile = pset.ParticleFile("particles.nc", outputdt=delta(hours=1))

kernel =

def beach(particle, fieldset, time):
    if particle.lon > -96:
        particle.lon = -96

gcm1.surface

<xarray.Dataset>
Dimensions:          (latitude: 480, longitude: 1500, time: 2947)
Coordinates:
  * latitude         (latitude) float32 -12.0 -11.949896 ... 11.949896 12.0
    depth            float32 ...
  * longitude        (longitude) float32 -170.0 -169.94997 ... -95.05003 -95.0
  * time             (time) datetime64[ns] 1995-09-01 ... 1997-01-04T12:00:00
Data variables:
    theta            (time, latitude, longitude) float32 dask.array<chunksize=(227, 480, 116), meta=np.ndarray>
    u                (time, latitude, longitude) float32 dask.array<chunksize=(227, 480, 116), meta=np.ndarray>
    v                (time, latitude, longitude) float32 dask.array<chunksize=(227, 480, 116), meta=np.ndarray>
    w                (time, latitude, longitude) float32 dask.array<chunksize=(227, 480, 116), meta=np.ndarray>
    salt             (time, latitude, longitude) float32 dask.array<chunksize=(227, 480, 116), meta=np.ndarray>
    KPP_diffusivity  (time, latitude, longitude) float32 dask.array<chunksize=(227, 480, 116), meta=np.ndarray>
    zeta             (time, latitude, longitude) float32 ...
Attributes:
    title:              daily snapshot from 1/20 degree Equatorial Pacific MI...
    easting:            longitude
    northing:           latitude
    field_julian_date:  400296
    julian_day_unit:    days since 1950-01-01 00:00:00

# %%time

# Advect ParticleSet with RK4 and periodic Boundary conditions
pset.execute(
    pset.Kernel(AdvectionRK4) + pset.Kernel(beach),
    runtime=delta(days=60),
    dt=delta(minutes=30),
    output_file=ofile,
)

INFO: Compiled JITParticleAdvectionRK4beach ==> /glade/scratch/dcherian/parcels-25721/3d4cb8c8bf1b028586a64d8cc3f66cce_0.so
INFO:parcels.tools.loggers:Compiled JITParticleAdvectionRK4beach ==> /glade/scratch/dcherian/parcels-25721/3d4cb8c8bf1b028586a64d8cc3f66cce_0.so
INFO: Temporary output files are stored in out-NQNDSGCQ.
INFO:parcels.tools.loggers:Temporary output files are stored in out-NQNDSGCQ.
INFO: You can use "parcels_convert_npydir_to_netcdf out-NQNDSGCQ" to convert these to a NetCDF file during the run.
INFO:parcels.tools.loggers:You can use "parcels_convert_npydir_to_netcdf out-NQNDSGCQ" to convert these to a NetCDF file during the run.
100% (5184000.0 of 5184000.0) |##########| Elapsed Time: 0:01:12 Time:  0:01:12

ds = xr.open_dataset("particles.nc").set_coords(["time"])
ds["time"] = ds.time.isel(traj=0, drop=True)
ds = ds.rename({"obs": "time"})
ds["trajectory"] = ds.trajectory.isel(time=0)
ds = ds.rename({"trajectory": "traj"})
ds = ds.set_coords("traj")
ds

<xarray.Dataset>
Dimensions:  (time: 241, traj: 2000)
Coordinates:
  * traj     (traj) float64 0.0 1.0 2.0 3.0 ... 1.997e+03 1.998e+03 1.999e+03
  * time     (time) datetime64[ns] 1995-09-01 1995-09-01T01:00:00 ... 1995-09-11
Data variables:
    lat      (traj, time) float32 ...
    lon      (traj, time) float32 ...
    z        (traj, time) float32 ...
Attributes:
    feature_type:           trajectory
    Conventions:            CF-1.6/CF-1.7
    ncei_template_version:  NCEI_NetCDF_Trajectory_Template_v2.0
    parcels_version:        2.1.4
    parcels_mesh:           spherical

ds.sel(traj=slice(None, None, 10)).plot.scatter(x="lon", y="lat", s=0.05)

<matplotlib.collections.PathCollection at 0x2b3a52b15588>

Read new hourly output

dirname = "/glade/campaign/cgd/oce/people/dcherian/TPOS_MITgcm_1_hb/HOLD"
metrics = pump.model.read_metrics(f"{dirname}/../")
ds = xr.open_mfdataset(f"{dirname}/File_*.nc", parallel=True)
# RAC = xr.align(metrics.RAC, ds, join="right")[0].chunk(
#    {k: v for k, v in ds.chunks.items() if k in ["latitude", "longitude"]}
# )
# ds["Jq"] = 1035 * 3994 * (ds.DFrI_TH + ds.KPPg_TH) / RAC
ds

<xarray.Dataset>
Dimensions:    (depth: 280, latitude: 400, longitude: 1420, time: 779)
Coordinates:
  * latitude   (latitude) float32 -10.0 -9.95 -9.9 -9.85 ... 9.85 9.9 9.95 10.0
  * longitude  (longitude) float32 -168.0 -167.9 -167.9 ... -97.1 -97.05 -97.0
  * depth      (depth) float32 -0.5 -1.5 -2.5 -3.5 ... -378.4 -391.7 -406.3
  * time       (time) datetime64[ns] 1995-11-15 ... 1995-12-17T10:00:00
Data variables:
    DFrI_TH    (time, depth, latitude, longitude) float32 dask.array<chunksize=(1, 280, 400, 1420), meta=np.ndarray>
    VISrI_Um   (time, depth, latitude, longitude) float32 dask.array<chunksize=(1, 280, 400, 1420), meta=np.ndarray>
    VISrI_Vm   (time, depth, latitude, longitude) float32 dask.array<chunksize=(1, 280, 400, 1420), meta=np.ndarray>
    Um_Diss    (time, depth, latitude, longitude) float32 dask.array<chunksize=(1, 280, 400, 1420), meta=np.ndarray>
    Vm_Diss    (time, depth, latitude, longitude) float32 dask.array<chunksize=(1, 280, 400, 1420), meta=np.ndarray>
    ETAN       (time, latitude, longitude) float32 dask.array<chunksize=(1, 400, 1420), meta=np.ndarray>
    KPPviscA   (time, depth, latitude, longitude) float32 dask.array<chunksize=(1, 280, 400, 1420), meta=np.ndarray>
    KPPdiffT   (time, depth, latitude, longitude) float32 dask.array<chunksize=(1, 280, 400, 1420), meta=np.ndarray>
    KPPRi      (time, depth, latitude, longitude) float32 dask.array<chunksize=(1, 280, 400, 1420), meta=np.ndarray>
    KPPg_TH    (time, depth, latitude, longitude) float32 dask.array<chunksize=(1, 280, 400, 1420), meta=np.ndarray>
    KPPhbl     (time, latitude, longitude) float32 dask.array<chunksize=(1, 400, 1420), meta=np.ndarray>
    KPPbo      (time, latitude, longitude) float32 dask.array<chunksize=(1, 400, 1420), meta=np.ndarray>
    theta      (time, depth, latitude, longitude) float32 dask.array<chunksize=(1, 280, 400, 1420), meta=np.ndarray>
    u          (time, depth, latitude, longitude) float32 dask.array<chunksize=(1, 280, 400, 1420), meta=np.ndarray>
    v          (time, depth, latitude, longitude) float32 dask.array<chunksize=(1, 280, 400, 1420), meta=np.ndarray>
    w          (time, depth, latitude, longitude) float32 dask.array<chunksize=(1, 280, 400, 1420), meta=np.ndarray>
    salt       (time, depth, latitude, longitude) float32 dask.array<chunksize=(1, 280, 400, 1420), meta=np.ndarray>
Attributes:
    title:              daily snapshot from 1/20 degree Equatorial Pacific MI...
    easting:            longitude
    northing:           latitude
    field_julian_date:  400296
    julian_day_unit:    days since 1950-01-01 00:00:00

idx = ds.indexes["longitude"].get_loc(-110, method="nearest")
period4 = dcpy.dask.map_copy(
    ds.isel(longitude=[idx - 1, idx, idx + 1]).sel(depth=slice(-200))
)

period4.to_zarr(
    "/glade/work/dcherian/pump/zarrs/110w-period-4-3.zarr",
    consolidated=True,
    mode="w",
)

<xarray.backends.zarr.ZarrStore at 0x2b629541f040>

RAC = metrics.RAC.reindex(
    longitude=period4.longitude, latitude=period4.latitude, method="nearest"
)  # .isel(longitude=[idx - 1, idx, idx + 1])

period4 = xr.open_zarr(
    "/glade/work/dcherian/pump/zarrs/110w-period-4-3.zarr", consolidated=True
)
period4["Jq"] = 1035 * 3994 * (period4.DFrI_TH + period4.KPPg_TH.fillna(0)) / RAC
period4["time"] = period4["time"] - pd.Timedelta("7h")
period4["dens"] = pump.mdjwf.dens(period4.salt, period4.theta, np.array([0.0]))
period4 = pump.calc.calc_reduced_shear(period4)
period4["mld"] = pump.calc.get_mld(period4.dens)
period4["dcl_base"] = pump.calc.get_dcl_base_Ri(period4)
period4["dcl"] = period4["mld"] - period4["dcl_base"]
period4["sst"] = period4.theta.isel(depth=0)
period4["eucmax"] = pump.calc.get_euc_max(
    period4.u.sel(latitude=0, method="nearest", drop=True)
)
period4.depth.attrs["units"] = "m"
period4.Jq.attrs["long_name"] = "$J^t_q$"
period4.Jq.attrs["units"] = "W/m²"
period4.S2.attrs["long_name"] = "$S²$"
period4.S2.attrs["units"] = "$s^{-2}$"
period4.Ri.attrs["long_name"] = "$Ri$"
period4.N2.attrs["long_name"] = "$N²$"
period4.N2.attrs["units"] = "$s^{-2}$"
period4

calc uz
calc vz
calc S2
calc N2
calc shred2
Calc Ri

<xarray.Dataset>
Dimensions:    (depth: 200, latitude: 400, longitude: 3, time: 779)
Coordinates:
  * depth      (depth) float32 -0.5 -1.5 -2.5 -3.5 ... -197.5 -198.5 -199.5
  * latitude   (latitude) float32 -10.0 -9.95 -9.9 -9.85 ... 9.85 9.9 9.95 10.0
  * longitude  (longitude) float64 -110.1 -110.0 -110.0
  * time       (time) datetime64[ns] 1995-11-14T17:00:00 ... 1995-12-17T03:00:00
Data variables:
    DFrI_TH    (time, depth, latitude, longitude) float32 dask.array<chunksize=(1, 200, 400, 3), meta=np.ndarray>
    ETAN       (time, latitude, longitude) float32 dask.array<chunksize=(1, 400, 3), meta=np.ndarray>
    KPPRi      (time, depth, latitude, longitude) float32 dask.array<chunksize=(1, 200, 400, 3), meta=np.ndarray>
    KPPbo      (time, latitude, longitude) float32 dask.array<chunksize=(1, 400, 3), meta=np.ndarray>
    KPPdiffT   (time, depth, latitude, longitude) float32 dask.array<chunksize=(1, 200, 400, 3), meta=np.ndarray>
    KPPg_TH    (time, depth, latitude, longitude) float32 dask.array<chunksize=(1, 200, 400, 3), meta=np.ndarray>
    KPPhbl     (time, latitude, longitude) float32 dask.array<chunksize=(1, 400, 3), meta=np.ndarray>
    KPPviscA   (time, depth, latitude, longitude) float32 dask.array<chunksize=(1, 200, 400, 3), meta=np.ndarray>
    Um_Diss    (time, depth, latitude, longitude) float32 dask.array<chunksize=(1, 200, 400, 3), meta=np.ndarray>
    VISrI_Um   (time, depth, latitude, longitude) float32 dask.array<chunksize=(1, 200, 400, 3), meta=np.ndarray>
    VISrI_Vm   (time, depth, latitude, longitude) float32 dask.array<chunksize=(1, 200, 400, 3), meta=np.ndarray>
    Vm_Diss    (time, depth, latitude, longitude) float32 dask.array<chunksize=(1, 200, 400, 3), meta=np.ndarray>
    salt       (time, depth, latitude, longitude) float32 dask.array<chunksize=(1, 200, 400, 3), meta=np.ndarray>
    theta      (time, depth, latitude, longitude) float32 dask.array<chunksize=(1, 200, 400, 3), meta=np.ndarray>
    u          (time, depth, latitude, longitude) float32 dask.array<chunksize=(1, 200, 400, 3), meta=np.ndarray>
    v          (time, depth, latitude, longitude) float32 dask.array<chunksize=(1, 200, 400, 3), meta=np.ndarray>
    w          (time, depth, latitude, longitude) float32 dask.array<chunksize=(1, 200, 400, 3), meta=np.ndarray>
    Jq         (time, depth, latitude, longitude) float64 dask.array<chunksize=(1, 200, 400, 3), meta=np.ndarray>
    dens       (time, depth, latitude, longitude) float64 dask.array<chunksize=(1, 200, 400, 3), meta=np.ndarray>
    uz         (time, depth, latitude, longitude) float32 dask.array<chunksize=(1, 200, 400, 3), meta=np.ndarray>
    vz         (time, depth, latitude, longitude) float32 dask.array<chunksize=(1, 200, 400, 3), meta=np.ndarray>
    S2         (time, depth, latitude, longitude) float32 dask.array<chunksize=(1, 200, 400, 3), meta=np.ndarray>
    shear      (time, depth, latitude, longitude) float32 dask.array<chunksize=(1, 200, 400, 3), meta=np.ndarray>
    N2         (time, depth, latitude, longitude) float64 dask.array<chunksize=(1, 200, 400, 3), meta=np.ndarray>
    shred2     (time, depth, latitude, longitude) float64 dask.array<chunksize=(1, 200, 400, 3), meta=np.ndarray>
    Ri         (time, depth, latitude, longitude) float64 dask.array<chunksize=(1, 200, 400, 3), meta=np.ndarray>
    mld        (time, latitude, longitude) float32 dask.array<chunksize=(1, 400, 3), meta=np.ndarray>
    dcl_base   (time, latitude, longitude) float32 dask.array<chunksize=(1, 400, 3), meta=np.ndarray>
    dcl        (time, latitude, longitude) float32 dask.array<chunksize=(1, 400, 3), meta=np.ndarray>
    sst        (time, latitude, longitude) float32 dask.array<chunksize=(1, 400, 3), meta=np.ndarray>
    eucmax     (time, longitude) float64 -96.5 -97.5 -97.5 ... -99.5 -99.5 -99.5
Attributes:
    easting:            longitude
    field_julian_date:  400296
    julian_day_unit:    days since 1950-01-01 00:00:00
    northing:           latitude
    title:              daily snapshot from 1/20 degree Equatorial Pacific MI...

period4.to_zarr(
    "/glade/work/dcherian/pump/zarrs/110w-period-4-4.zarr",
    consolidated=True,
    mode="w",
)

period4.Jq.isel(longitude=1).sel(latitude=0, method="nearest").plot(
    x="time", robust=True
)

<matplotlib.collections.QuadMesh at 0x2b5715c4e810>

Time series of shear

subset = (
    period4.isel(longitude=1)
    .sel(latitude=3.5, method="nearest")
    .sel(depth=slice(-60))
    .mean("depth")
)


subset.uz.plot()
subset.vz.plot()
subset.sst.plot(ax=plt.gca().twinx(), color="C2")
plt.figure()
subset.N2.plot()

[<matplotlib.lines.Line2D at 0x2ae91f212310>]

older attempts

oni = pump.obs.process_oni().sel(time=slice("1996", None))
oni.plot()
oni = oni.where(oni.time.dt.month.isin([9, 10, 11, 12]))
yearly = oni.resample(time="Y", loffset="-6M").mean()
yearly.plot()
x
subset = yearly.where((yearly < -0.25) & (yearly > (-0.65)), drop=True)
subset.plot(marker="o", ls="none")
print(subset.time.dt.year)

neutral_years = subset.time.dt.year.values
ny = list(set(neutral_years) - set([2008]))

/glade/u/home/dcherian/pump/pump/obs.py:354: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support skipfooter; you can avoid this warning by specifying engine='python'.
  )
/glade/u/home/dcherian/miniconda3/envs/dcpy/lib/python3.6/site-packages/xarray/core/nanops.py:142: RuntimeWarning: Mean of empty slice
  return np.nanmean(a, axis=axis, dtype=dtype)

<xarray.DataArray 'year' (time: 5)>
array([1996, 2001, 2005, 2008, 2013])
Coordinates:
  * time     (time) datetime64[ns] 1996-06-30 2001-06-30 ... 2013-06-30

tao.sel(longitude=-110, time="1996").T.plot(x="time")

<matplotlib.collections.QuadMesh at 0x2b2a8ba57ed0>

# meantao = tao.sel(longitude=-110, time=slice("1990", "2000")).resample(time="D").mean()
# meantao = meantao.where(meantao.time.dt.year.isin([1996, 2001, 2013]), drop=True).resample(time="Y").mean()
meantao = (
    tao.sel(longitude=-110, time="1996")
    # .where(tao.time.dt.year.isin([1996]), drop=True)
    # .groupby("time.year")
    # .mean()
    .mean("time")
)
meantao

<xarray.Dataset>
Dimensions:    (depth: 101)
Coordinates:
  * depth      (depth) float64 -500.0 -495.0 -490.0 -485.0 ... -10.0 -5.0 0.0
    latitude   float32 ...
    longitude  float32 -110.0
Data variables:
    T          (depth) float64 dask.array<chunksize=(101,), meta=np.ndarray>
    u          (depth) float32 dask.array<chunksize=(101,), meta=np.ndarray>
    v          (depth) float32 dask.array<chunksize=(101,), meta=np.ndarray>
    dens       (depth) float64 dask.array<chunksize=(101,), meta=np.ndarray>

import xrft

daily = (
    tao.sel(longitude=-140)
    .resample(time="D")
    .mean()
    .where(
        tao.time.dt.year.isin(ny) & tao.time.dt.month.isin([9, 10, 11, 12]), drop=True
    )
)
daily

xarray.Dataset

• Dimensions:
  • depth: 101
  • time: 488
• Coordinates: (4)
  • time
    (time)
    datetime64[ns]
    1996-09-01 ... 2013-12-31

    array(['1996-09-01T00:00:00.000000000', '1996-09-02T00:00:00.000000000',
           '1996-09-03T00:00:00.000000000', ..., '2013-12-29T00:00:00.000000000',
           '2013-12-30T00:00:00.000000000', '2013-12-31T00:00:00.000000000'],
          dtype='datetime64[ns]')

  • longitude
    ()
    float32
    -140.0

    FORTRAN_format :

    epic_code :

    502

    type :

    EVEN

    units :

    degree_east

    array(-140., dtype=float32)

  • latitude
    ()
    float32
    0.0

    FORTRAN_format :

    epic_code :

    500

    type :

    EVEN

    units :

    degree_north

    array(0., dtype=float32)

  • depth
    (depth)
    float64
    -500.0 -495.0 -490.0 ... -5.0 0.0

    array([-500., -495., -490., -485., -480., -475., -470., -465., -460., -455.,
           -450., -445., -440., -435., -430., -425., -420., -415., -410., -405.,
           -400., -395., -390., -385., -380., -375., -370., -365., -360., -355.,
           -350., -345., -340., -335., -330., -325., -320., -315., -310., -305.,
           -300., -295., -290., -285., -280., -275., -270., -265., -260., -255.,
           -250., -245., -240., -235., -230., -225., -220., -215., -210., -205.,
           -200., -195., -190., -185., -180., -175., -170., -165., -160., -155.,
           -150., -145., -140., -135., -130., -125., -120., -115., -110., -105.,
           -100.,  -95.,  -90.,  -85.,  -80.,  -75.,  -70.,  -65.,  -60.,  -55.,
            -50.,  -45.,  -40.,  -35.,  -30.,  -25.,  -20.,  -15.,  -10.,   -5.,
              0.])

• Data variables: (4)
  • T
    (time, depth)
    float64
    dask.array<chunksize=(1, 101), meta=np.ndarray>

    
    
    
    
      
    
        
     
     Array 
     Chunk 
      
      
    
        
     Bytes 
     394.30 kB 
     
     808 B 
        
     Shape 
     (488, 101) 
     
     (1, 101) 
        
     Count 
     76730 Tasks 
     488 Chunks 
        
     Type 
     float64 
     numpy.ndarray 
      
    
    
    
    
    
    
    

  • u
    (time, depth)
    float32
    dask.array<chunksize=(1, 101), meta=np.ndarray>

    
    
    
    
      
    
        
     
     Array 
     Chunk 
      
      
    
        
     Bytes 
     197.15 kB 
     
     404 B 
        
     Shape 
     (488, 101) 
     
     (1, 101) 
        
     Count 
     76730 Tasks 
     488 Chunks 
        
     Type 
     float32 
     numpy.ndarray 
      
    
    
    
    
    
    
    

  • v
    (time, depth)
    float32
    dask.array<chunksize=(1, 101), meta=np.ndarray>

    
    
    
    
      
    
        
     
     Array 
     Chunk 
      
      
    
        
     Bytes 
     197.15 kB 
     
     404 B 
        
     Shape 
     (488, 101) 
     
     (1, 101) 
        
     Count 
     76730 Tasks 
     488 Chunks 
        
     Type 
     float32 
     numpy.ndarray 
      
    
    
    
    
    
    
    

  • dens
    (time, depth)
    float64
    dask.array<chunksize=(1, 101), meta=np.ndarray>

    
    
    
    
      
    
        
     
     Array 
     Chunk 
      
      
    
        
     Bytes 
     394.30 kB 
     
     808 B 
        
     Shape 
     (488, 101) 
     
     (1, 101) 
        
     Count 
     89182 Tasks 
     488 Chunks 
        
     Type 
     float64 
     numpy.ndarray 
      
    
    
    
    
    
    
    

• Attributes: (0)

ind = pd.MultiIndex.from_arrays([daily.time.dt.year, daily.time.dt.dayofyear])
daily = daily.assign_coords(time=ind)
reshaped = daily.unstack("time")
reshaped

xarray.Dataset

• Dimensions:
  • dayofyear: 123
  • depth: 101
  • year: 4
• Coordinates: (5)
  • longitude
    ()
    float32
    -140.0

    FORTRAN_format :

    epic_code :

    502

    type :

    EVEN

    units :

    degree_east

    array(-140., dtype=float32)

  • latitude
    ()
    float32
    0.0

    FORTRAN_format :

    epic_code :

    500

    type :

    EVEN

    units :

    degree_north

    array(0., dtype=float32)

  • depth
    (depth)
    float64
    -500.0 -495.0 -490.0 ... -5.0 0.0

    array([-500., -495., -490., -485., -480., -475., -470., -465., -460., -455.,
           -450., -445., -440., -435., -430., -425., -420., -415., -410., -405.,
           -400., -395., -390., -385., -380., -375., -370., -365., -360., -355.,
           -350., -345., -340., -335., -330., -325., -320., -315., -310., -305.,
           -300., -295., -290., -285., -280., -275., -270., -265., -260., -255.,
           -250., -245., -240., -235., -230., -225., -220., -215., -210., -205.,
           -200., -195., -190., -185., -180., -175., -170., -165., -160., -155.,
           -150., -145., -140., -135., -130., -125., -120., -115., -110., -105.,
           -100.,  -95.,  -90.,  -85.,  -80.,  -75.,  -70.,  -65.,  -60.,  -55.,
            -50.,  -45.,  -40.,  -35.,  -30.,  -25.,  -20.,  -15.,  -10.,   -5.,
              0.])

  • year
    (year)
    int64
    1996 2001 2005 2013

    array([1996, 2001, 2005, 2013])

  • dayofyear
    (dayofyear)
    int64
    244 245 246 247 ... 363 364 365 366

    array([244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257,
           258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271,
           272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285,
           286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299,
           300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313,
           314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327,
           328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341,
           342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355,
           356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366])

• Data variables: (4)
  • T
    (depth, year, dayofyear)
    float64
    dask.array<chunksize=(101, 1, 123), meta=np.ndarray>

    
    
    
    
      
    
        
     
     Array 
     Chunk 
      
      
    
        
     Bytes 
     397.54 kB 
     
     99.38 kB 
        
     Shape 
     (101, 4, 123) 
     
     (101, 1, 123) 
        
     Count 
     80157 Tasks 
     4 Chunks 
        
     Type 
     float64 
     numpy.ndarray 
      
    
    
    
    
    
    
    

  • u
    (depth, year, dayofyear)
    float32
    dask.array<chunksize=(101, 1, 123), meta=np.ndarray>

    
    
    
    
      
    
        
     
     Array 
     Chunk 
      
      
    
        
     Bytes 
     198.77 kB 
     
     49.69 kB 
        
     Shape 
     (101, 4, 123) 
     
     (101, 1, 123) 
        
     Count 
     80157 Tasks 
     4 Chunks 
        
     Type 
     float32 
     numpy.ndarray 
      
    
    
    
    
    
    
    

  • v
    (depth, year, dayofyear)
    float32
    dask.array<chunksize=(101, 1, 123), meta=np.ndarray>

    
    
    
    
      
    
        
     
     Array 
     Chunk 
      
      
    
        
     Bytes 
     198.77 kB 
     
     49.69 kB 
        
     Shape 
     (101, 4, 123) 
     
     (101, 1, 123) 
        
     Count 
     80157 Tasks 
     4 Chunks 
        
     Type 
     float32 
     numpy.ndarray 
      
    
    
    
    
    
    
    

  • dens
    (depth, year, dayofyear)
    float64
    dask.array<chunksize=(101, 1, 123), meta=np.ndarray>

    
    
    
    
      
    
        
     
     Array 
     Chunk 
      
      
    
        
     Bytes 
     397.54 kB 
     
     99.38 kB 
        
     Shape 
     (101, 4, 123) 
     
     (101, 1, 123) 
        
     Count 
     92609 Tasks 
     4 Chunks 
        
     Type 
     float64 
     numpy.ndarray 
      
    
    
    
    
    
    
    

• Attributes: (0)

u = reshaped.u.sel(depth=slice(-200, -55)).sel(dayofyear=slice(365)).fillna(0)
u

xarray.DataArray

'u'

• depth: 30
• year: 4
• dayofyear: 122

• dask.array<chunksize=(30, 1, 122), meta=np.ndarray>

  
  
  
  
    
  
      
   
   Array 
   Chunk 
    
    
  
      
   Bytes 
   58.56 kB 
   
   14.64 kB 
      
   Shape 
   (30, 4, 122) 
   
   (30, 1, 122) 
      
   Count 
   80177 Tasks 
   4 Chunks 
      
   Type 
   float32 
   numpy.ndarray 
    
  
  
  
  
  
  
  

• Coordinates: (5)
  • longitude
    ()
    float32
    -140.0

    FORTRAN_format :

    epic_code :

    502

    type :

    EVEN

    units :

    degree_east

    array(-140., dtype=float32)

  • latitude
    ()
    float32
    0.0

    FORTRAN_format :

    epic_code :

    500

    type :

    EVEN

    units :

    degree_north

    array(0., dtype=float32)

  • depth
    (depth)
    float64
    -200.0 -195.0 ... -60.0 -55.0

    array([-200., -195., -190., -185., -180., -175., -170., -165., -160., -155.,
           -150., -145., -140., -135., -130., -125., -120., -115., -110., -105.,
           -100.,  -95.,  -90.,  -85.,  -80.,  -75.,  -70.,  -65.,  -60.,  -55.])

  • year
    (year)
    int64
    1996 2001 2005 2013

    array([1996, 2001, 2005, 2013])

  • dayofyear
    (dayofyear)
    int64
    244 245 246 247 ... 362 363 364 365

    array([244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257,
           258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271,
           272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285,
           286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299,
           300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313,
           314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327,
           328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341,
           342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355,
           356, 357, 358, 359, 360, 361, 362, 363, 364, 365])

• Attributes: (0)

variable = u.differentiate("depth")

fft = xrft.dft(variable, dim=["dayofyear"]).mean("year")
rms = 1.414 * np.abs(fft).sel(freq_dayofyear=slice(1 / 50, 1 / 5)).integrate(
    "freq_dayofyear"
)

mean = variable.mean(["year", "dayofyear"])
mean, rms = dask.compute(mean, rms)

mean.plot(y="depth")
(mean + rms).plot(y="depth")
(mean - rms).plot(y="depth")

[<matplotlib.lines.Line2D at 0x2ac3fabc32b0>]

This plot is junk because of sampling bias.

model = (
    gcm1.annual.sel(depth=slice(-250))
    .sel(latitude=0, longitude=-110, method="nearest")
    .rename({"theta": "T"})
)

f, axx = plt.subplots(1, 2, sharey=True, constrained_layout=True)

ax = dict(zip("uT", axx))

for var in ax:
    model[var].plot(ax=ax[var], y="depth")
    meantao[var].sel(depth=slice(-250, None)).plot(ax=ax[var], y="depth")

[aa.set_title("") for aa in axx]

[Text(0.5, 1.0, ''), Text(0.5, 1.0, '')]

levels = np.sort([0.2, 0.4, 0.6, 0.8, 1])
gcm1.annual.u.sel(depth=slice(-400)).sel(latitude=0, method="nearest").plot.contour(
    colors="w", levels=levels
)
johnson.u.sel(longitude=slice(-170, -95)).sel(
    latitude=0, method="nearest"
).plot.contour(colors="k", levels=levels)

<matplotlib.contour.QuadContourSet at 0x2ac327fe0d30>

125 W summer low Ri

subset = (
    sections.isel(longitude=1)
    .sel(depth=slice(-200), time=slice("1996-04-20", "1996-05-03"))
    .sel(latitude=3.5, method="nearest")
)

subset.Jq.plot(
    x="time",
    robust=True,
)
subset.mld.plot(x="time")

[<matplotlib.lines.Line2D at 0x2ac3f0fd2710>]

fg = (
    sections.Ri.sel(time="1996-04-30", depth=slice(-100))
    .isel(longitude=1)
    .plot(y="depth", row="time", vmin=0.1, vmax=0.7)
)
sections.mld.sel(time="1996-04-30").isel(longitude=1)

<xarray.DataArray 'mld' (time: 6, latitude: 480)>
dask.array<getitem, shape=(6, 480), dtype=float32, chunksize=(6, 138), chunktype=numpy.ndarray>
Coordinates:
  * time       (time) datetime64[ns] 1996-04-30 ... 1996-04-30T20:00:00
  * latitude   (latitude) float32 -12.0 -11.949896 -11.899791 ... 11.949896 12.0
    longitude  int64 -125
Attributes:
    long_name:    MLD
    units:        m
    description:  Interpolate density to 1m grid. Search for max depth where ...

diurnal trigger

subset = (
    period4.isel(longitude=1)
    .sel(latitude=3.5, method="nearest")
    .sel(depth=slice(-100))
    .transpose(..., "time")
)

f, axx = plt.subplots(3, 1, sharex=True, squeeze=False)
ax = axx.squeeze()
subset.S2.plot(
    ax=ax[0],
    robust=True,
    norm=mpl.colors.Normalize(vmin=8e-5, vmax=4e-4),
    cmap=mpl.cm.Spectral_r,
)
subset.N2.plot(
    ax=ax[1],
    robust=True,
    norm=mpl.colors.LogNorm(vmin=0.5e-5, vmax=2e-4),
    cmap=mpl.cm.Spectral_r,
)
subset.Jq.rolling(depth=2).mean().plot(
    ax=ax[2], vmax=0, robust=True, cmap=mpl.cm.BuPu_r
)


def plot(ax):
    if "KPPhbl" in subset:
        (-1 * subset.KPPhbl).plot(x="time", color="w", lw=2, ax=ax)
    subset.mld.plot(color="g", lw=4, ax=ax)
    subset.dcl_base.plot(color="k", lw=2, ax=ax)
    subset.Jq.rolling(depth=2).mean().plot.contour(
        levels=17,
        colors="k",
        linestyles="-",
        linewidths=0.5,
        x="time",
        robust=True,
        ax=ax,
    )


[plot(axx) for axx in ax]

dcpy.plots.clean_axes(axx)

f.set_size_inches((8, 8))

Presentation images

Summary lat-lon fields

subset = period4.isel(longitude=1).sel(depth=slice(-40, -60), latitude=slice(-2, 5))
# subset = subset.where((subset.depth < subset.mld)).mean("depth")
subset = subset.mean("depth")

kwargs = {
    "sst": dict(cmap=mpl.cm.RdYlBu_r),
    "uz": dict(vmax=1.5e-2),
    "vz": dict(vmax=1.5e-2),
    "N2": dict(cmap=mpl.cm.Greens, vmin=0, vmax=4e-4),
    "Jq": dict(cmap=mpl.cm.Blues_r, vmax=0, vmin=-200),
    "dcl": dict(cmap=mpl.cm.GnBu, vmin=10, vmax=50),
}

subset = subset[list(kwargs.keys())].compute()

xr.set_options(keep_attrs=True)
sstmean = subset.sst.resample(time="D").mean().compute()

kwargs = {
    "sst": dict(cmap=mpl.cm.RdYlBu_r),
    "uz": dict(vmax=1.5e-2),
    "vz": dict(vmax=1.5e-2),
    "N2": dict(cmap=mpl.cm.Greens, vmin=1e-5, vmax=2e-4),
    "Jq": dict(cmap=mpl.cm.Blues_r, vmax=0, vmin=-200),
    "dcl": dict(cmap=mpl.cm.GnBu, vmin=10, vmax=50),
}


f, axx = plt.subplots(2, 3, sharex=True, sharey=True, constrained_layout=True)

subset["sst"].attrs["long_name"] = "SST"
subset["uz"].attrs["long_name"] = "$u_z$"
subset["uz"].attrs["units"] = "1/s"
subset["vz"].attrs["long_name"] = "$v_z$"
subset["vz"].attrs["units"] = "1/s"
subset["N2"].attrs["long_name"] = "$N^2$"
subset["N2"].attrs["units"] = "1/s²"
subset["dcl"].attrs["long_name"] = "$z_{MLD} - z_{Ri}$"
subset["dcl"].attrs["units"] = "m"
subset["Jq"].attrs["long_name"] = "$J_q^t$"
subset["Jq"].attrs["units"] = "W/m²"

ax = dict(zip(["sst", "uz", "vz", "N2", "dcl", "Jq"], axx.flat))

for var, axis in ax.items():
    subset[var].plot(
        x="time",
        cbar_kwargs={"orientation": "horizontal", "shrink": 0.8},
        ax=axis,
        robust=True,
        **kwargs[var],
    )
    sstmean.plot.contour(
        x="time",
        ax=axis,
        levels=[24],
        colors="w",
        add_labels=False,
        linewidths=2,
    )
    sstmean.plot.contour(
        x="time",
        ax=axis,
        levels=[24],
        colors="k",
        add_labels=False,
        linewidths=0.5,
    )

[dcpy.plots.concise_date_formatter(aa) for aa in axx.flat]
[aa.set_xlabel("") for aa in axx.flat]
[aa.set_title("") for aa in axx.flat]

dcpy.plots.clean_axes(axx)
# f.savefig("images/period4-all-fields.png")

shear rotation sequence

pump.plot.vor_streamplot(
    period4,
    vort,
    stride=3,
    refspeed=0.4,
    uy=False,
    vec=False,
    stream=False,
)
plt.gcf().savefig("images/110-period4-vort-0.png")

pump.plot.vor_streamplot(
    period4, vort, stride=3, refspeed=0.4, uy=False, vec=False, stream=True
)
plt.gcf().savefig("images/110-period4-vort-1.png")

pump.plot.vor_streamplot(period4, vort, stride=3, refspeed=0.4, uy=False, stream=False)
plt.gcf().savefig("images/110-period4-vort-2.png")

pump.plot.vor_streamplot(
    period4, vort, stride=3, refspeed=0.4, vy=True, stream=False, colorbar=False
)
plt.gca().text(0.8, 0.9, "$+v_y$", fontsize="xx-large", transform=plt.gca().transAxes)
plt.gcf().savefig("images/110-period4-vort-3.png")

pump.plot.vor_streamplot(
    period4, vort, stride=3, refspeed=0.4, stream=False, colorbar=False
)
plt.gca().text(0.8, 0.9, "$-u_y$", fontsize="xx-large", transform=plt.gca().transAxes)
plt.gcf().savefig("images/110-period4-vort-4.png")

pump.plot.vor_streamplot(
    period4, vort, stride=3, refspeed=0.4, uy=False, colorbar=False
)
plt.gcf().savefig("images/110-period4-vort-4.png")

pump.plot.vor_streamplot(
    period4, vort, stride=3, refspeed=0.4, uy=False, colorbar=False
)
plt.gcf().savefig("images/110-period4-vort-4.png")

pump.plot.vor_streamplot(
    period4, vort, stride=3, refspeed=0.4, uy=False, colorbar=False
)
plt.gcf().savefig("images/110-period4-vort-4.png")

KPP deep cycle 1999 nov

stationnov = (
    xr.open_zarr("nov-1999-station.zarr", consolidated=True)
    .sel(depth=slice(-50))
    .load()
)

stationnov

<xarray.Dataset>
Dimensions:        (depth: 20, time: 49)
Coordinates:
  * depth          (depth) float32 -1.25 -3.75 -6.25 ... -43.75 -46.25 -48.75
    latitude       float64 3.525
    longitude      float32 -110.025
  * time           (time) datetime64[ns] 1999-11-21 ... 1999-11-23
Data variables:
    Cd             (time) float64 0.001047 0.001046 ... 0.001075 0.00107
    Cdn_10         (time) float64 0.9759 0.9749 0.9742 ... 1.008 0.9959 0.988
    Ce             (time) float64 0.001237 0.001237 ... 0.001244 0.001253
    Cen_10         (time) float64 1.108 1.108 1.108 1.109 ... 1.105 1.106 1.107
    Ch             (time) float64 0.001237 0.001237 ... 0.001244 0.001253
    Chn_10         (time) float64 1.108 1.108 1.108 1.109 ... 1.105 1.106 1.107
    DFrI_TH        (depth, time) float32 0.0 0.0 0.0 ... -763.1658 -812.03156
    Evap           (time) float64 0.1404 0.1398 0.1391 ... 0.1514 0.1474 0.1456
    Jq             (depth, time) float64 0.0 0.0 0.0 ... -95.27 -102.3 -108.8
    KPPdiffKzT     (depth, time) float32 0.0 0.0 ... 0.0007682296 0.00079970685
    KPPg_TH        (depth, time) float32 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0
    KPPhbl         (time) float32 22.682652 22.976309 ... 22.382105 22.323658
    KPPviscAz      (depth, time) float32 0.0 0.0 ... 0.0007772296 0.00080870686
    L              (time) float64 -50.09 -50.04 -50.2 ... -48.49 -44.78 -41.55
    Le             (time) float64 2.5e+06 2.5e+06 2.5e+06 ... 2.5e+06 2.5e+06
    Q10            (time) float64 12.94 12.95 12.96 12.93 ... 13.38 13.4 13.41
    Qrf            (time) float64 12.94 12.95 12.96 12.93 ... 13.38 13.4 13.41
    Qs             (time) float64 0.01747 0.01747 0.01746 ... 0.01792 0.01793
    RF             (time) float64 1.601e-10 9.915e-11 ... 1.131e-10 1.209e-10
    RH10           (time) float64 75.81 75.84 75.88 75.48 ... 78.1 78.3 78.46
    RHrf           (time) float64 75.81 75.84 75.88 75.48 ... 78.1 78.3 78.46
    Rnl            (time) float64 23.84 28.76 33.68 35.42 ... 31.87 30.67 31.06
    SSH            (time) float32 0.36676002 0.36429515 ... 0.37923476
    T10            (time) float64 22.65 22.65 22.64 22.69 ... 22.7 22.69 22.65
    Trf            (time) float64 22.65 22.65 22.64 22.69 ... 22.7 22.69 22.65
    U10            (time) float64 5.76 5.745 5.736 5.713 ... 6.195 6.022 5.902
    U10N           (time) float64 5.966 5.95 5.941 5.912 ... 6.425 6.256 6.143
    UN             (time) float64 5.966 5.95 5.941 5.912 ... 6.425 6.256 6.143
    Urf            (time) float64 5.76 5.745 5.736 5.713 ... 6.195 6.022 5.902
    UrfN           (time) float64 5.966 5.95 5.941 5.912 ... 6.425 6.256 6.143
    VISrI_Um       (depth, time) float32 0.0 0.0 0.0 ... 190.43164 193.63968
    VISrI_Vm       (depth, time) float32 0.0 0.0 0.0 ... -298.01572 -318.4322
    dens           (depth, time) float32 1023.3128 1023.317 ... 1023.55115
    dqer           (time) float64 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0
    dter           (time) float64 0.1908 0.1978 0.2047 ... 0.2018 0.2022 0.2055
    hbb            (time) float64 12.05 11.94 11.84 11.21 ... 16.35 16.06 16.21
    hlb            (time) float64 97.53 97.06 96.61 96.27 ... 105.1 102.3 101.1
    hlwebb         (time) float64 2.531 2.516 2.5 2.423 ... 3.198 3.135 3.137
    hsb            (time) float64 4.97 4.904 4.828 4.225 ... 8.717 8.634 8.871
    hsbb           (time) float64 10.89 10.79 10.69 10.06 ... 15.09 14.84 15.01
    huss           (time) float64 0.01294 0.01295 0.01296 ... 0.0134 0.01341
    lat            (time) float64 -97.53 -97.06 -96.61 ... -105.1 -102.3 -101.1
    long           (time) float64 -35.87 -40.65 -45.41 ... -43.73 -42.57 -42.95
    netflux        (time) float64 -138.4 -142.6 -146.9 ... -157.6 -153.5 -153.0
    nonlocal_flux  (depth, time) float64 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0
    prra           (time) float64 1.427e-07 8.867e-08 ... 1.004e-07 1.07e-07
    psl            (time) float64 1.014e+05 1.014e+05 ... 1.014e+05 1.013e+05
    qsr            (time) float64 -0.1732 -0.1729 -0.1724 ... -0.1714 -0.1731
    rhoa           (time) float64 1.2 1.2 1.2 1.2 1.2 ... 1.2 1.2 1.2 1.2 1.2
    rlds           (time) float64 401.1 396.1 391.1 389.3 ... 395.5 396.7 396.3
    rsds           (time) float64 0.0 0.0 0.0 29.76 59.52 ... 0.0 0.0 0.0 0.0
    salt           (depth, time) float32 34.304375 34.30507 ... 34.393257
    sens           (time) float64 -4.97 -4.904 -4.828 ... -8.717 -8.634 -8.871
    short          (time) float64 0.0 0.0 0.0 26.78 53.57 ... 0.0 0.0 0.0 0.0
    stress         (time) float64 0.04198 0.04172 0.04156 ... 0.04716 0.04512
    tas            (time) float64 295.8 295.8 295.8 295.8 ... 295.8 295.8 295.8
    tau            (time) float64 0.04198 0.04172 0.04156 ... 0.04716 0.04512
    taux           (time) float64 -0.0117 -0.01035 ... -0.01367 -0.01311
    tauy           (time) float64 0.04032 0.04042 0.04057 ... 0.04513 0.04317
    theta          (depth, time) float32 23.32442 23.311815 ... 22.730448
    tkt            (time) float64 0.0009061 0.0009081 ... 0.0008566 0.0008741
    tsr            (time) float64 -0.02195 -0.02173 ... -0.03597 -0.03777
    u              (depth, time) float32 -0.29970324 -0.30042994 ... -0.17791136
    uas            (time) float64 -1.606 -1.426 -1.246 ... -1.768 -1.746 -1.715
    usr            (time) float64 0.1877 0.1872 0.1868 ... 0.2056 0.199 0.1947
    v              (depth, time) float32 0.689187 0.6815233 ... -0.060036022
    vas            (time) float64 5.532 5.565 5.599 5.612 ... 5.938 5.763 5.647
    w              (depth, time) float32 -7.249771e-06 ... 4.057019e-05
    zet            (time) float64 -0.1997 -0.1998 -0.1992 ... -0.2233 -0.2407
    zoq            (time) float64 0.0001268 0.0001276 ... 0.0001107 0.0001166
    zot            (time) float64 0.0001268 0.0001276 ... 0.0001107 0.0001166
Attributes:
    easting:   longitude
    northing:  latitude
    title:     Station profile, index (i,j)=(1200,240)

stationnov.KPPdiffKzT.attrs["long_name"] = "$K_T$"
stationnov.KPPdiffKzT.attrs["units"] = "$m²/s²$"
stationnov.stress.attrs["units"] = "$N/m²$"
stationnov.netflux.attrs["long_name"] = "$Q_{net}$"
stationnov.netflux.attrs["units"] = "$W/m²$"

f, ax = pump.plot.plot_dcl(stationnov)
ax[0, 0].set_title("(3.5°N, 110°W)")
dcpy.plots.pub_fig_width("jpo", "medium 2")
f.savefig("../images/kpp-deep-cycle-35.png")

calc uz
calc vz
calc S2
calc N2
calc shred2
Calc Ri

/glade/u/home/dcherian/miniconda3/envs/dcpy2/lib/python3.7/site-packages/matplotlib/colors.py:1171: RuntimeWarning: invalid value encountered in less_equal
  mask |= resdat <= 0
/glade/u/home/dcherian/miniconda3/envs/dcpy2/lib/python3.7/site-packages/IPython/core/pylabtools.py:132: UserWarning: Creating legend with loc="best" can be slow with large amounts of data.
  fig.canvas.print_figure(bytes_io, **kw)

dcl flux vs surface flux

gcm1.budget["time"] = gcm1.budget.time - pd.Timedelta("7h")

qnet = gcm1.budget.oceQnet.sel(longitude=-110, method="nearest")
# qnet = qnet.isel(time=slice(10)).mean("latitude")
# qnet = qnet.copy(data=qnet.data.map_blocks(np.copy))

period4.sst.isel(longitude=1).sel(latitude=slice(-1, 5)).plot(x="time")

<matplotlib.collections.QuadMesh at 0x2b1332eaf9e8>

period4["qnet"] = qnet.sel(time=period4.time).expand_dims("longitude")

def visualize(da, nblocks=5, **kwargs):
    kwargs.setdefault("color", "order")
    kwargs.setdefault("optimize_graph", True)
    kwargs.setdefault("rankdir", "LR")
    kwargs.setdefault("cmap", "autumn")
    da.data.blocks[slice(nblocks)].visualize(**kwargs)

period4.dcl.load()

xarray.DataArray

'dcl'

• time: 210
• latitude: 480
• longitude: 3

• 0.0 0.0 0.0 0.0 0.0 0.0 2.0 0.0 ... 9.0 11.0 11.0 11.0 0.0 0.0 0.0

  array([[[ 0.,  0.,  0.],
          [ 0.,  0.,  0.],
          [ 2.,  0.,  0.],
          ...,
          [ 0.,  0.,  0.],
          [ 0.,  0.,  0.],
          [ 0.,  0.,  0.]],
  
         [[ 0.,  0.,  0.],
          [ 0.,  0.,  0.],
          [ 0.,  0.,  0.],
          ...,
          [ 0.,  0.,  0.],
          [ 0.,  0.,  0.],
          [ 0.,  0.,  0.]],
  
         [[ 0.,  0.,  0.],
          [ 0.,  0.,  0.],
          [ 0.,  0.,  0.],
          ...,
          [ 0.,  0.,  0.],
          [ 0.,  0.,  0.],
          [ 0.,  0.,  0.]],
  
         ...,
  
         [[ 0.,  0.,  0.],
          [ 0.,  0.,  0.],
          [ 0.,  0.,  0.],
          ...,
          [ 0.,  0.,  0.],
          [ 0.,  0.,  0.],
          [ 0.,  0.,  0.]],
  
         [[ 0.,  0.,  0.],
          [ 0.,  0.,  0.],
          [ 0.,  0.,  0.],
          ...,
          [ 0.,  0.,  0.],
          [ 0.,  0.,  0.],
          [ 0.,  0.,  0.]],
  
         [[ 0.,  0.,  0.],
          [ 8.,  8.,  8.],
          [ 8.,  7.,  8.],
          ...,
          [ 9., 10.,  9.],
          [11., 11., 11.],
          [ 0.,  0.,  0.]]], dtype=float32)

• Coordinates: (3)
  • longitude
    (longitude)
    float64
    -110.1 -110.0 -110.0

    array([-110.060043, -110.01001 , -109.959976])

  • latitude
    (latitude)
    float32
    -12.0 -11.949896 ... 11.949896 12.0

    array([-12.      , -11.949896, -11.899791, ...,  11.899791,  11.949896,
            12.      ], dtype=float32)

  • time
    (time)
    datetime64[ns]
    1995-11-10T17:00:00 ... 1995-12-15T13:00:00

    array(['1995-11-10T17:00:00.000000000', '1995-11-10T21:00:00.000000000',
           '1995-11-11T01:00:00.000000000', ..., '1995-12-15T05:00:00.000000000',
           '1995-12-15T09:00:00.000000000', '1995-12-15T13:00:00.000000000'],
          dtype='datetime64[ns]')

• Attributes: (0)

period4["mld"], period4["dcl_base"] = dask.compute(period4.mld, period4.dcl_base)

period4 = period4.sel(time=slice("1995-11-11 00:00", "1995-12-15 00:00"))
latslice = slice(None, None)
sstmask = (period4.sst.isel(longitude=1).sel(latitude=latslice) < 24.3).compute()
period4.sst.isel(longitude=1).where(sstmask).plot(levels=11, x="time")

<matplotlib.collections.QuadMesh at 0x2b12fdddbc18>

masked = period4.isel(longitude=1).where(sstmask)

dcmask = ((masked.depth > masked.dcl_base) & (masked.depth < masked.mld)).compute()

dcl_jq = masked.Jq.where(sstmask & dcmask)

masked.dcl.where(dcmask).plot.hist(bins=30)

(array([4.70000e+03, 7.83600e+03, 1.29980e+04, 1.52560e+04, 1.78160e+04,
        1.13470e+04, 1.10620e+04, 3.17100e+03, 1.66364e+05, 3.42010e+04,
        2.79190e+04, 1.96840e+04, 3.16700e+04, 3.10220e+04, 2.97680e+04,
        2.74370e+04, 2.47780e+04, 1.82610e+04, 2.73120e+04, 2.48090e+04,
        2.26800e+04, 1.91240e+04, 1.08830e+04, 5.12700e+03, 4.89400e+03,
        4.30700e+03, 1.89900e+03, 7.07000e+02, 2.46000e+02, 8.60000e+01]),
 array([ 2.       ,  4.8333335,  7.6666665, 10.5      , 13.333333 ,
        16.166666 , 19.       , 21.833334 , 24.666666 , 27.5      ,
        30.333334 , 33.166668 , 36.       , 38.833332 , 41.666668 ,
        44.5      , 47.333332 , 50.166668 , 53.       , 55.833332 ,
        58.666668 , 61.5      , 64.333336 , 67.166664 , 70.       ,
        72.833336 , 75.666664 , 78.5      , 81.333336 , 84.166664 ,
        87.       ], dtype=float32),
 <a list of 30 Patch objects>)

period4.dcl.where(period4.dcl > 0).isel(longitude=1).plot()

<matplotlib.collections.QuadMesh at 0x2b1387a68f28>

masked.qnet.sum("time").plot(y="latitude")
(-1 * dcl_jq.sum(["depth", "time"]) / 50).plot(y="latitude")

qnet = (period4.isel(longitude=1).qnet).load()
mean_dcl_jq = (-1 * dcl_jq.sum("depth") / 50).fillna(0).mean(["time"]).load()
uprof = (
    period4.u.sel(depth=slice(-70, -120))
    .isel(longitude=1)
    .mean(["depth", "time"])
    .load()
)

daily_qnet = qnet.resample(time="D").mean()

daily_qnet.where(daily_qnet > 0).mean("time").plot(y="latitude")
mean_dcl_jq.plot(y="latitude")

ax1 = plt.gca()
ax1.set_facecolor("None")
ax2 = ax1.twiny()

ax2.fill_betweenx(uprof.latitude, uprof.values, color="gray", alpha=0.5)
ax2.set_xlim([-0.5, 1.5])
ax2.set_ylim([-5, 5])
ax2.set_zorder(-1)
ax1.set_xlim([0, 220])
plt.gcf().set_size_inches((3, 5))

period4.isel(longitude=1).qnet.resample(time="D").mean().plot(x="time")
masked.sst.plot.contour(levels=11, x="time")
dcpy.plots.liney([2.5, 4.25], zorder=10)

Check 1_hb jq calculation

tao = xr.open_dataset(
    "~/pump/glade/TPOS_MITgcm_1_hb/HOLD/obs_subset/tao-budget-extract.nc"
).load()
metrics = read_metrics("../glade/TPOS_MITgcm_1_hb/")

metrics.dRF.max().compute()

xarray.DataArray

'dRF'

• -2.5

  array(-2.5)

• Coordinates: (0)
• Attributes: (0)

(
    (
        3994
        * 1035
        * tao.sel(longitude=-110, latitude=0).DFrI_TH.rolling(depth=2).mean()
        / metrics.RAC.sel(longitude=-110, method="nearest", latitude=0)
    )
    .sel(time=slice("1996-05-19", "1996-05-23"))
    .plot(x="time", robust=True)
)

<matplotlib.collections.QuadMesh at 0x2b325d2fba20>

Get surface fluxes for station

station = ds

jrafull = xr.concat(
    [
        pump.obs.read_jra(
            "/glade/p/nsc/ncgd0043/make_TPOS_MITgcm/JRA_FORCING/1999/JRA55DO*[0-9].nc",
            chunks={"time": 1200},
        ),
        pump.obs.read_jra(
            "/glade/p/nsc/ncgd0043/make_TPOS_MITgcm/JRA_FORCING/200[0-2]/JRA55DO*[0-9].nc",
            chunks={"time": 1200},
        ),
    ],
    dim="time",
)
jrafull["time"] = jrafull.time - pd.Timedelta("7h")
jra = jrafull.interp(longitude=-110.009, latitude=0.02506, method="linear").sel(
    time=slice(station.time[0].values, station.time[-1].values)
)
jra["time"] = jra.time.dt.floor("h")
for var in jra.variables:
    if isinstance(jra[var].data, dask.array.Array):
        jra[var] = jra[var].copy(data=jra[var].data.map_blocks(np.copy))
jra

xarray.Dataset

• Dimensions:
  • time: 8000
• Coordinates: (3)
  • time
    (time)
    datetime64[ns]
    1998-12-31T18:00:00 ... 2000-05-14T17:00:00

    array(['1998-12-31T18:00:00.000000000', '1998-12-31T20:00:00.000000000',
           '1998-12-31T21:00:00.000000000', ..., '2000-05-14T14:00:00.000000000',
           '2000-05-14T15:00:00.000000000', '2000-05-14T17:00:00.000000000'],
          dtype='datetime64[ns]')

  • longitude
    ()
    float64
    -110.0

    array(-110.009)

  • latitude
    ()
    float64
    0.02506

    array(0.02506)

• Data variables: (8)
  • huss
    (time)
    float32
    dask.array<chunksize=(2399,), meta=np.ndarray>

    actual_range :

    [1.9999999e-05 2.0845825e-02]

    
    
    
    
      
    
        
     
     Array 
     Chunk 
      
      
    
        
     Bytes 
     32.00 kB 
     
     9.60 kB 
        
     Shape 
     (8000,) 
     
     (2400,) 
        
     Count 
     183 Tasks 
     4 Chunks 
        
     Type 
     float32 
     numpy.ndarray 
      
    
    
    
    
    
    
    

  • prra
    (time)
    float32
    dask.array<chunksize=(2400,), meta=np.ndarray>

    actual_range :

    [0. 0.00155959]

    
    
    
    
      
    
        
     
     Array 
     Chunk 
      
      
    
        
     Bytes 
     32.00 kB 
     
     9.60 kB 
        
     Shape 
     (8000,) 
     
     (2400,) 
        
     Count 
     183 Tasks 
     4 Chunks 
        
     Type 
     float32 
     numpy.ndarray 
      
    
    
    
    
    
    
    

  • psl
    (time)
    float32
    dask.array<chunksize=(2399,), meta=np.ndarray>

    actual_range :

    [ 94958.15 104364.42]

    
    
    
    
      
    
        
     
     Array 
     Chunk 
      
      
    
        
     Bytes 
     32.00 kB 
     
     9.60 kB 
        
     Shape 
     (8000,) 
     
     (2400,) 
        
     Count 
     183 Tasks 
     4 Chunks 
        
     Type 
     float32 
     numpy.ndarray 
      
    
    
    
    
    
    
    

  • rlds
    (time)
    float32
    dask.array<chunksize=(2400,), meta=np.ndarray>

    actual_range :

    [126.48046 442.72263]

    
    
    
    
      
    
        
     
     Array 
     Chunk 
      
      
    
        
     Bytes 
     32.00 kB 
     
     9.60 kB 
        
     Shape 
     (8000,) 
     
     (2400,) 
        
     Count 
     183 Tasks 
     4 Chunks 
        
     Type 
     float32 
     numpy.ndarray 
      
    
    
    
    
    
    
    

  • rsds
    (time)
    float32
    dask.array<chunksize=(2400,), meta=np.ndarray>

    actual_range :

    [ 0. 1149.1526]

    
    
    
    
      
    
        
     
     Array 
     Chunk 
      
      
    
        
     Bytes 
     32.00 kB 
     
     9.60 kB 
        
     Shape 
     (8000,) 
     
     (2400,) 
        
     Count 
     183 Tasks 
     4 Chunks 
        
     Type 
     float32 
     numpy.ndarray 
      
    
    
    
    
    
    
    

  • tas
    (time)
    float32
    dask.array<chunksize=(2399,), meta=np.ndarray>

    actual_range :

    [235.69489 304.40918]

    
    
    
    
      
    
        
     
     Array 
     Chunk 
      
      
    
        
     Bytes 
     32.00 kB 
     
     9.60 kB 
        
     Shape 
     (8000,) 
     
     (2400,) 
        
     Count 
     183 Tasks 
     4 Chunks 
        
     Type 
     float32 
     numpy.ndarray 
      
    
    
    
    
    
    
    

  • uas
    (time)
    float32
    dask.array<chunksize=(2399,), meta=np.ndarray>

    actual_range :

    [-26.107578 28.0601 ]

    
    
    
    
      
    
        
     
     Array 
     Chunk 
      
      
    
        
     Bytes 
     32.00 kB 
     
     9.60 kB 
        
     Shape 
     (8000,) 
     
     (2400,) 
        
     Count 
     183 Tasks 
     4 Chunks 
        
     Type 
     float32 
     numpy.ndarray 
      
    
    
    
    
    
    
    

  • vas
    (time)
    float32
    dask.array<chunksize=(2399,), meta=np.ndarray>

    actual_range :

    [-26.301264 20.78898 ]

    
    
    
    
      
    
        
     
     Array 
     Chunk 
      
      
    
        
     Bytes 
     32.00 kB 
     
     9.60 kB 
        
     Shape 
     (8000,) 
     
     (2400,) 
        
     Count 
     183 Tasks 
     4 Chunks 
        
     Type 
     float32 
     numpy.ndarray 
      
    
    
    
    
    
    
    

• Attributes: (1)

  About :

  Created with SOSIE interpolation environement => https://github.com/brodeau/sosie/

jrai = jra.compute().interpolate_na("time").interp(time=station.time)
fluxes = pump.calc.coare_fluxes_jra(station.sel(latitude=0, method="nearest"), jrai)

fluxes.sel(time="1999-05").to_array().plot(x="time", hue="variable")

[<matplotlib.lines.Line2D at 0x2ac8544011d0>,
 <matplotlib.lines.Line2D at 0x2ac81a4ca390>,
 <matplotlib.lines.Line2D at 0x2ac80b617b70>,
 <matplotlib.lines.Line2D at 0x2ac8511004a8>,
 <matplotlib.lines.Line2D at 0x2ac7d3a9a0b8>,
 <matplotlib.lines.Line2D at 0x2ac802430390>]

expected = xr.open_mfdataset(
    "/glade/campaign/cgd/oce/people/bachman/TPOS_MITgcm_fix3/*00[0-9][0-9]*surf.nc",
    parallel=True,
    chunks={"time": 1},
)
# expected["time"] = expected.time - pd.Timedelta("7h")

/glade/u/home/dcherian/miniconda3/envs/dcpy/lib/python3.6/site-packages/ipykernel_launcher.py:2: FutureWarning: In xarray version 0.15 the default behaviour of `open_mfdataset`
will change. To retain the existing behavior, pass
combine='nested'. To use future default behavior, pass
combine='by_coords'. See
http://xarray.pydata.org/en/stable/combining.html#combining-multi

  
/glade/u/home/dcherian/miniconda3/envs/dcpy/lib/python3.6/site-packages/xarray/backends/api.py:941: FutureWarning: The datasets supplied have global dimension coordinates. You may want
to use the new `combine_by_coords` function (or the
`combine='by_coords'` option to `open_mfdataset`) to order the datasets
before concatenation. Alternatively, to continue concatenating based
on the order the datasets are supplied in future, please use the new
`combine_nested` function (or the `combine='nested'` option to
open_mfdataset).
  from_openmfds=True,

Things look good! I could take the daily error and distribute that in time uniformly in my hourly net flux time series

actual = fluxes.sel(time=slice(expected.time[0], expected.time[-1]))
# output has means calculated in GMT
# so shift time back to GMT and then calculate daily means.
# first and last points will be weird.
actual["time"] = actual.time + pd.Timedelta("7h")
# actual.total.plot()
(
    actual.total.resample(time="D").mean().hvplot()
    * expected.TFLUX.sel(latitude=0, longitude=-110, method="nearest").hvplot(x="time")
)

debug eucmax

yp, y0, ym = dask.compute(*get_euc_bounds(u))
yp.plot(hue="longitude")
sections.eucmax.plot(hue="longitude")
ym.plot(hue="longitude")

[<matplotlib.lines.Line2D at 0x2ac3af343050>,
 <matplotlib.lines.Line2D at 0x2ac3e40111d0>,
 <matplotlib.lines.Line2D at 0x2ac3ed8dae50>,
 <matplotlib.lines.Line2D at 0x2ac3e8886910>]

KPP’s deep cycle

1. The descending shear layer coincides with a descending PBL. The mixed layer descends later

20 year run stations

jrafull = pump.obs.read_jra_20()
station = pump.model.model.read_stations_20("../TPOS_MITgcm_fix3/", "*110.000*.nc")
station

xarray.Dataset

• Dimensions:
  • depth: 185
  • latitude: 111
  • time: 24000
• Coordinates: (4)
  • depth
    (depth)
    float32
    -1.25 -3.75 ... -5758.0986

    array([-1.250000e+00, -3.750000e+00, -6.250000e+00, -8.750000e+00,
           -1.125000e+01, -1.375000e+01, -1.625000e+01, -1.875000e+01,
           -2.125000e+01, -2.375000e+01, -2.625000e+01, -2.875000e+01,
           -3.125000e+01, -3.375000e+01, -3.625000e+01, -3.875000e+01,
           -4.125000e+01, -4.375000e+01, -4.625000e+01, -4.875000e+01,
           -5.125000e+01, -5.375000e+01, -5.625000e+01, -5.875000e+01,
           -6.125000e+01, -6.375000e+01, -6.625000e+01, -6.875000e+01,
           -7.125000e+01, -7.375000e+01, -7.625000e+01, -7.875000e+01,
           -8.125000e+01, -8.375000e+01, -8.625000e+01, -8.875000e+01,
           -9.125000e+01, -9.375000e+01, -9.625000e+01, -9.875000e+01,
           -1.012500e+02, -1.037500e+02, -1.062500e+02, -1.087500e+02,
           -1.112500e+02, -1.137500e+02, -1.162500e+02, -1.187500e+02,
           -1.212500e+02, -1.237500e+02, -1.262500e+02, -1.287500e+02,
           -1.312500e+02, -1.337500e+02, -1.362500e+02, -1.387500e+02,
           -1.412500e+02, -1.437500e+02, -1.462500e+02, -1.487500e+02,
           -1.512500e+02, -1.537500e+02, -1.562500e+02, -1.587500e+02,
           -1.612500e+02, -1.637500e+02, -1.662500e+02, -1.687500e+02,
           -1.712500e+02, -1.737500e+02, -1.762500e+02, -1.787500e+02,
           -1.812500e+02, -1.837500e+02, -1.862500e+02, -1.887500e+02,
           -1.912500e+02, -1.937500e+02, -1.962500e+02, -1.987500e+02,
           -2.012500e+02, -2.037500e+02, -2.062500e+02, -2.087500e+02,
           -2.112500e+02, -2.137500e+02, -2.162500e+02, -2.187500e+02,
           -2.212500e+02, -2.237500e+02, -2.262500e+02, -2.287500e+02,
           -2.312500e+02, -2.337500e+02, -2.362500e+02, -2.387500e+02,
           -2.412500e+02, -2.437500e+02, -2.462500e+02, -2.487500e+02,
           -2.513687e+02, -2.542363e+02, -2.573763e+02, -2.608145e+02,
           -2.645794e+02, -2.687019e+02, -2.732162e+02, -2.781592e+02,
           -2.835718e+02, -2.894987e+02, -2.959886e+02, -3.030949e+02,
           -3.108764e+02, -3.193972e+02, -3.287274e+02, -3.389439e+02,
           -3.501312e+02, -3.623811e+02, -3.757947e+02, -3.904827e+02,
           -4.065660e+02, -4.241773e+02, -4.434617e+02, -4.645781e+02,
           -4.877004e+02, -5.130195e+02, -5.407438e+02, -5.711019e+02,
           -6.043441e+02, -6.407443e+02, -6.806025e+02, -7.242472e+02,
           -7.720381e+02, -8.243693e+02, -8.816718e+02, -9.444181e+02,
           -1.013125e+03, -1.088360e+03, -1.170741e+03, -1.260949e+03,
           -1.358099e+03, -1.458099e+03, -1.558099e+03, -1.658099e+03,
           -1.758099e+03, -1.858099e+03, -1.958099e+03, -2.058098e+03,
           -2.158098e+03, -2.258098e+03, -2.358098e+03, -2.458098e+03,
           -2.558098e+03, -2.658098e+03, -2.758098e+03, -2.858098e+03,
           -2.958098e+03, -3.058098e+03, -3.158098e+03, -3.258098e+03,
           -3.358098e+03, -3.458098e+03, -3.558098e+03, -3.658098e+03,
           -3.758098e+03, -3.858098e+03, -3.958098e+03, -4.058098e+03,
           -4.158099e+03, -4.258099e+03, -4.358099e+03, -4.458099e+03,
           -4.558099e+03, -4.658099e+03, -4.758099e+03, -4.858099e+03,
           -4.958099e+03, -5.058099e+03, -5.158099e+03, -5.258099e+03,
           -5.358099e+03, -5.458099e+03, -5.558099e+03, -5.658099e+03,
           -5.758099e+03], dtype=float32)

  • longitude
    ()
    float32
    ...

    array(-110.025, dtype=float32)

  • latitude
    (latitude)
    float64
    -3.075 -3.025 ... 5.975 6.025

    array([-3.075, -3.025, -2.975, -2.825, -2.775, -2.725, -2.575, -2.525, -2.475,
           -2.325, -2.275, -2.225, -2.075, -2.025, -1.975, -1.825, -1.775, -1.725,
           -1.575, -1.525, -1.475, -1.325, -1.275, -1.225, -1.075, -1.025, -0.975,
           -0.825, -0.775, -0.725, -0.575, -0.525, -0.475, -0.325, -0.275, -0.225,
           -0.075, -0.025,  0.025,  0.175,  0.225,  0.275,  0.425,  0.475,  0.525,
            0.675,  0.725,  0.775,  0.925,  0.975,  1.025,  1.175,  1.225,  1.275,
            1.425,  1.475,  1.525,  1.675,  1.725,  1.775,  1.925,  1.975,  2.025,
            2.175,  2.225,  2.275,  2.425,  2.475,  2.525,  2.675,  2.725,  2.775,
            2.925,  2.975,  3.025,  3.175,  3.225,  3.275,  3.425,  3.475,  3.525,
            3.675,  3.725,  3.775,  3.925,  3.975,  4.025,  4.175,  4.225,  4.275,
            4.425,  4.475,  4.525,  4.675,  4.725,  4.775,  4.925,  4.975,  5.025,
            5.175,  5.225,  5.275,  5.425,  5.475,  5.525,  5.675,  5.725,  5.775,
            5.925,  5.975,  6.025])

  • time
    (time)
    datetime64[ns]
    1998-12-31T18:00:00 ... 2001-09-26T17:00:00

    array(['1998-12-31T18:00:00.000000000', '1998-12-31T19:00:00.000000000',
           '1998-12-31T20:00:00.000000000', ..., '2001-09-26T15:00:00.000000000',
           '2001-09-26T16:00:00.000000000', '2001-09-26T17:00:00.000000000'],
          dtype='datetime64[ns]')

• Data variables: (16)
  • SSH
    (time, latitude)
    float32
    dask.array<chunksize=(6000, 1), meta=np.ndarray>

    
    
    
    
      
    
        
     
     Array 
     Chunk 
      
      
    
        
     Bytes 
     10.66 MB 
     
     24.00 kB 
        
     Shape 
     (24000, 111) 
     
     (6000, 1) 
        
     Count 
     2220 Tasks 
     444 Chunks 
        
     Type 
     float32 
     numpy.ndarray 
      
    
    
    
    
    
    
    

  • KPPhbl
    (time, latitude)
    float32
    dask.array<chunksize=(6000, 1), meta=np.ndarray>

    
    
    
    
      
    
        
     
     Array 
     Chunk 
      
      
    
        
     Bytes 
     10.66 MB 
     
     24.00 kB 
        
     Shape 
     (24000, 111) 
     
     (6000, 1) 
        
     Count 
     2220 Tasks 
     444 Chunks 
        
     Type 
     float32 
     numpy.ndarray 
      
    
    
    
    
    
    
    

  • u
    (depth, time, latitude)
    float32
    dask.array<chunksize=(185, 6000, 1), meta=np.ndarray>

    
    
    
    
      
    
        
     
     Array 
     Chunk 
      
      
    
        
     Bytes 
     1.97 GB 
     
     4.44 MB 
        
     Shape 
     (185, 24000, 111) 
     
     (185, 6000, 1) 
        
     Count 
     2220 Tasks 
     444 Chunks 
        
     Type 
     float32 
     numpy.ndarray 
      
    
    
    
    
    
    
    

  • v
    (depth, time, latitude)
    float32
    dask.array<chunksize=(185, 6000, 1), meta=np.ndarray>

    
    
    
    
      
    
        
     
     Array 
     Chunk 
      
      
    
        
     Bytes 
     1.97 GB 
     
     4.44 MB 
        
     Shape 
     (185, 24000, 111) 
     
     (185, 6000, 1) 
        
     Count 
     2220 Tasks 
     444 Chunks 
        
     Type 
     float32 
     numpy.ndarray 
      
    
    
    
    
    
    
    

  • w
    (depth, time, latitude)
    float32
    dask.array<chunksize=(185, 6000, 1), meta=np.ndarray>

    
    
    
    
      
    
        
     
     Array 
     Chunk 
      
      
    
        
     Bytes 
     1.97 GB 
     
     4.44 MB 
        
     Shape 
     (185, 24000, 111) 
     
     (185, 6000, 1) 
        
     Count 
     2220 Tasks 
     444 Chunks 
        
     Type 
     float32 
     numpy.ndarray 
      
    
    
    
    
    
    
    

  • theta
    (depth, time, latitude)
    float32
    dask.array<chunksize=(185, 6000, 1), meta=np.ndarray>

    
    
    
    
      
    
        
     
     Array 
     Chunk 
      
      
    
        
     Bytes 
     1.97 GB 
     
     4.44 MB 
        
     Shape 
     (185, 24000, 111) 
     
     (185, 6000, 1) 
        
     Count 
     2220 Tasks 
     444 Chunks 
        
     Type 
     float32 
     numpy.ndarray 
      
    
    
    
    
    
    
    

  • salt
    (depth, time, latitude)
    float32
    dask.array<chunksize=(185, 6000, 1), meta=np.ndarray>

    
    
    
    
      
    
        
     
     Array 
     Chunk 
      
      
    
        
     Bytes 
     1.97 GB 
     
     4.44 MB 
        
     Shape 
     (185, 24000, 111) 
     
     (185, 6000, 1) 
        
     Count 
     2220 Tasks 
     444 Chunks 
        
     Type 
     float32 
     numpy.ndarray 
      
    
    
    
    
    
    
    

  • DFrI_TH
    (depth, time, latitude)
    float32
    dask.array<chunksize=(185, 6000, 1), meta=np.ndarray>

    
    
    
    
      
    
        
     
     Array 
     Chunk 
      
      
    
        
     Bytes 
     1.97 GB 
     
     4.44 MB 
        
     Shape 
     (185, 24000, 111) 
     
     (185, 6000, 1) 
        
     Count 
     2220 Tasks 
     444 Chunks 
        
     Type 
     float32 
     numpy.ndarray 
      
    
    
    
    
    
    
    

  • VISrI_Um
    (depth, time, latitude)
    float32
    dask.array<chunksize=(185, 6000, 1), meta=np.ndarray>

    
    
    
    
      
    
        
     
     Array 
     Chunk 
      
      
    
        
     Bytes 
     1.97 GB 
     
     4.44 MB 
        
     Shape 
     (185, 24000, 111) 
     
     (185, 6000, 1) 
        
     Count 
     2220 Tasks 
     444 Chunks 
        
     Type 
     float32 
     numpy.ndarray 
      
    
    
    
    
    
    
    

  • VISrI_Vm
    (depth, time, latitude)
    float32
    dask.array<chunksize=(185, 6000, 1), meta=np.ndarray>

    
    
    
    
      
    
        
     
     Array 
     Chunk 
      
      
    
        
     Bytes 
     1.97 GB 
     
     4.44 MB 
        
     Shape 
     (185, 24000, 111) 
     
     (185, 6000, 1) 
        
     Count 
     2220 Tasks 
     444 Chunks 
        
     Type 
     float32 
     numpy.ndarray 
      
    
    
    
    
    
    
    

  • KPPdiffKzT
    (depth, time, latitude)
    float32
    dask.array<chunksize=(185, 6000, 1), meta=np.ndarray>

    
    
    
    
      
    
        
     
     Array 
     Chunk 
      
      
    
        
     Bytes 
     1.97 GB 
     
     4.44 MB 
        
     Shape 
     (185, 24000, 111) 
     
     (185, 6000, 1) 
        
     Count 
     2220 Tasks 
     444 Chunks 
        
     Type 
     float32 
     numpy.ndarray 
      
    
    
    
    
    
    
    

  • KPPviscAz
    (depth, time, latitude)
    float32
    dask.array<chunksize=(185, 6000, 1), meta=np.ndarray>

    
    
    
    
      
    
        
     
     Array 
     Chunk 
      
      
    
        
     Bytes 
     1.97 GB 
     
     4.44 MB 
        
     Shape 
     (185, 24000, 111) 
     
     (185, 6000, 1) 
        
     Count 
     2220 Tasks 
     444 Chunks 
        
     Type 
     float32 
     numpy.ndarray 
      
    
    
    
    
    
    
    

  • KPPg_TH
    (depth, time, latitude)
    float32
    dask.array<chunksize=(185, 6000, 1), meta=np.ndarray>

    
    
    
    
      
    
        
     
     Array 
     Chunk 
      
      
    
        
     Bytes 
     1.97 GB 
     
     4.44 MB 
        
     Shape 
     (185, 24000, 111) 
     
     (185, 6000, 1) 
        
     Count 
     2220 Tasks 
     444 Chunks 
        
     Type 
     float32 
     numpy.ndarray 
      
    
    
    
    
    
    
    

  • Jq
    (depth, time, latitude)
    float64
    dask.array<chunksize=(185, 6000, 1), meta=np.ndarray>

    
    
    
    
      
    
        
     
     Array 
     Chunk 
      
      
    
        
     Bytes 
     3.94 GB 
     
     8.88 MB 
        
     Shape 
     (185, 24000, 111) 
     
     (185, 6000, 1) 
        
     Count 
     6002 Tasks 
     444 Chunks 
        
     Type 
     float64 
     numpy.ndarray 
      
    
    
    
    
    
    
    

  • nonlocal_flux
    (depth, time, latitude)
    float64
    dask.array<chunksize=(185, 6000, 1), meta=np.ndarray>

    
    
    
    
      
    
        
     
     Array 
     Chunk 
      
      
    
        
     Bytes 
     3.94 GB 
     
     8.88 MB 
        
     Shape 
     (185, 24000, 111) 
     
     (185, 6000, 1) 
        
     Count 
     3338 Tasks 
     444 Chunks 
        
     Type 
     float64 
     numpy.ndarray 
      
    
    
    
    
    
    
    

  • dens
    (depth, time, latitude)
    float32
    dask.array<chunksize=(185, 6000, 1), meta=np.ndarray>

    
    
    
    
      
    
        
     
     Array 
     Chunk 
      
      
    
        
     Bytes 
     1.97 GB 
     
     4.44 MB 
        
     Shape 
     (185, 24000, 111) 
     
     (185, 6000, 1) 
        
     Count 
     4885 Tasks 
     444 Chunks 
        
     Type 
     float32 
     numpy.ndarray 
      
    
    
    
    
    
    
    

• Attributes: (3)

  title :

  Station profile, index (i,j)=(1200,240)

  easting :

  longitude

  northing :

  latitude

frictional shear torque

state = xr.open_mfdataset(
    "/glade/campaign/cgd/oce/people/bachman/TPOS_MITgcm_fix3/File_0[0-4]*buoy.nc",
    parallel=True,
)
budget = xr.open_mfdataset(
    "/glade/campaign/cgd/oce/people/bachman/TPOS_MITgcm_fix3/File_0[0-4]*[u,v]b.nc",
    parallel=True,
)

/glade/u/home/dcherian/miniconda3/envs/dcpy/lib/python3.6/site-packages/ipykernel_launcher.py:1: FutureWarning: In xarray version 0.15 the default behaviour of `open_mfdataset`
will change. To retain the existing behavior, pass
combine='nested'. To use future default behavior, pass
combine='by_coords'. See
http://xarray.pydata.org/en/stable/combining.html#combining-multi

  """Entry point for launching an IPython kernel.
/glade/u/home/dcherian/miniconda3/envs/dcpy/lib/python3.6/site-packages/xarray/backends/api.py:941: FutureWarning: The datasets supplied have global dimension coordinates. You may want
to use the new `combine_by_coords` function (or the
`combine='by_coords'` option to `open_mfdataset`) to order the datasets
before concatenation. Alternatively, to continue concatenating based
on the order the datasets are supplied in future, please use the new
`combine_nested` function (or the `combine='nested'` option to
open_mfdataset).
  from_openmfds=True,
/glade/u/home/dcherian/miniconda3/envs/dcpy/lib/python3.6/site-packages/ipykernel_launcher.py:2: FutureWarning: In xarray version 0.15 the default behaviour of `open_mfdataset`
will change. To retain the existing behavior, pass
combine='nested'. To use future default behavior, pass
combine='by_coords'. See
http://xarray.pydata.org/en/stable/combining.html#combining-multi

  
/glade/u/home/dcherian/miniconda3/envs/dcpy/lib/python3.6/site-packages/xarray/backends/api.py:941: FutureWarning: The datasets supplied have global dimension coordinates. You may want
to use the new `combine_by_coords` function (or the
`combine='by_coords'` option to `open_mfdataset`) to order the datasets
before concatenation. Alternatively, to continue concatenating based
on the order the datasets are supplied in future, please use the new
`combine_nested` function (or the `combine='nested'` option to
open_mfdataset).The datasets supplied require both concatenation and merging. From
xarray version 0.15 this will operation will require either using the
new `combine_nested` function (or the `combine='nested'` option to
open_mfdataset), with a nested list structure such that you can combine
along the dimensions None. Alternatively if your datasets have global
dimension coordinates then you can use the new `combine_by_coords`
function.
  from_openmfds=True,

metrics = pump.model.model.read_metrics(
    "/glade/campaign/cgd/oce/people/bachman/TPOS_MITgcm_fix3/"
)
metrics = (
    metrics.isel(
        longitude=slice(40, -40), latitude=slice(40, -40), depth=slice(136)
    ).assign_coords(
        longitude=state.longitude, latitude=state.latitude, depth=state.depth
    )
).persist()

VISrI_Um = xr.concat(
    [
        budget.VISrI_Um,
        xr.zeros_like(budget.VISrI_Um.isel(depth=0).drop("depth")).expand_dims(
            depth=[-5865.922]
        ),
    ],
    dim="depth",
)

VISrI_Vm = xr.concat(
    [
        budget.VISrI_Vm,
        xr.zeros_like(budget.VISrI_Vm.isel(depth=0).drop("depth")).expand_dims(
            depth=[-5865.922]
        ),
    ],
    dim="depth",
)
# from http://mailman.mitgcm.org/pipermail/mitgcm-support/2010-December/006921.html
budget["Um_Impl"] = VISrI_Um.diff("depth", label="lower") / (
    metrics.rAw * metrics.drF * metrics.hFacW
)
# from http://mailman.mitgcm.org/pipermail/mitgcm-support/2010-December/006921.html
budget["Vm_Impl"] = VISrI_Vm.diff("depth", label="lower") / (
    metrics.rAw * metrics.drF * metrics.hFacW
)

state.theta.isel(depth=0).sel(longitude=-110, method="nearest").sel(
    latitude=slice(-2, 6), time=slice("1999-Jul", "1999-Nov")
).plot(x="time")

<matplotlib.collections.QuadMesh at 0x2b03bdf443c8>

def subset(ds):
    return (
        ds.sel(time=slice("1999-12-15", "1999-10-15"))
        .sel(latitude=slice(-2, 6), depth=slice(-100))
        .sel(longitude=-110, method="nearest")
        .pipe(dcpy.dask.map_copy)
    )

subset(state.v.differentiate("depth")).sel(depth=slice(-60)).mean("depth").plot(
    x="time", robust=True
)
subset(state.theta).isel(depth=0).plot.contour(colors="k", linewidths=1, x="time")

<matplotlib.contour.QuadContourSet at 0x2b03bdf21d30>

state["dens"] = pump.mdjwf.dens(state.salt, state.theta, np.array([0.0]))
period = (
    state.merge(budget[["Um_Diss", "Um_Impl", "Vm_Diss", "Vm_Impl"]])
    .sel(time=slice("1999-10-15", "1999-11-15"))
    .sel(latitude=slice(-2, 6), depth=slice(-100))
    .pipe(dcpy.dask.map_copy)
)
idx = period.indexes["longitude"].get_loc(-110, method="nearest")
period = period.isel(longitude=[idx - 1, idx, idx + 1])
duzdt, dvzdt = dask.compute(*pump.calc.estimate_shear_evolution_terms(period))

period = pump.calc_reduced_shear(period)
period["mld"] = pump.calc.get_mld(period.dens)
period["dcl_base"] = pump.calc.get_dcl_base_Ri(period)
period["dcl"] = period.mld - period.dcl_base

calc uz
calc vz
calc S2
calc N2
calc shred2
Calc Ri

period = period.persist()

f, ax = pump.plot.plot_shear_terms(period, duzdt, dvzdt, mask_dcl=False)
# ax["u"]["shear"].set_xlim((None, "1999-12-06"))

f, ax = pump.plot.plot_shear_terms(period, duzdt, dvzdt, mask_dcl=False)
ax["u"]["shear"].set_xlim((None, "1999-12-06"))

(730072.5, 730094.0)

state.v.sel(
    latitude=slice(-2, 2),
    depth=slice(-80),
    longitude=slice(-125, -95),
    time=slice("1999-11-09", "2000-01-01"),
).mean(["depth", "latitude"]).plot(x="longitude", robust=True)
pump.plot.plot_reference_speed(plt.gca())

pump.plot.vor_streamplot(period, pump.calc.vorticity(period), stride=2, refspeed=0.3)

(subset(budget.Vm_Impl)).differentiate("depth").sel(depth=slice(-60)).mean(
    "depth"
).plot(x="time", vmin=-5e-8, cmap=mpl.cm.RdBu_r)
subset(state.v.differentiate("depth")).sel(depth=slice(-60)).mean("depth").plot.contour(
    colors="k", levels=15, x="time", robust=True
)

<matplotlib.contour.QuadContourSet at 0x2b7f9adf5ed0>

subset(state).theta.isel(depth=0).plot.contourf(levels=21, x="time", robust=True)

<matplotlib.contour.QuadContourSet at 0x2b6e1e7b4190>

subset(ubvb).VISrI_Vm.sel(latitude=0, method="nearest").sel(depth=slice(-100)).plot(
    y="depth", robust=True
)

<matplotlib.collections.QuadMesh at 0x2b6e62631090>

subset(ubvb).Vm_Diss.differentiate("depth").sel(depth=slice(-60)).mean("depth").plot(
    x="time", robust=True
)

<matplotlib.collections.QuadMesh at 0x2b6e2c134c90>

forcing = pump.obs.interp_jra_to_station(jrafull, station)
fluxes, coare = pump.calc.coare_fluxes_jra(station, forcing)
station = station.merge(fluxes, compat="override")
station = station.merge(coare, compat="override")
station = station.merge(forcing, compat="override")

/glade/u/home/dcherian/python/xcoare/xcoare/coare35vn.py:466: RuntimeWarning: invalid value encountered in greater
  k = u10 > umax
/glade/u/home/dcherian/python/xcoare/xcoare/coare35vn.py:480: RuntimeWarning: invalid value encountered in greater
  charn = np.where(ut > 0, 0.011 + (ut - 10) / (18 - 10) * (0.018 - 0.011), 0.011)
/glade/u/home/dcherian/python/xcoare/xcoare/coare35vn.py:481: RuntimeWarning: invalid value encountered in greater
  charn[ut > 18] = 0.018

fluxes.netflux.sel(latitude=0, method="nearest").isel(time=slice(1000)).plot(x="time")

[<matplotlib.lines.Line2D at 0x2b0a34394400>]

deep cycle plots

def hvplot_dcl(subset):
    flx = subset.netflux.hvplot()
    S2 = subset.S2.hvplot(cmap="fire")
    N2 = subset.N2.hvplot()
    jq = subset.Jq.hvplot.image(x="time")

    lines = (
        subset.mld.hvplot(color="darkorange", line_style="--", line_width=3)
        * (-1 * subset.KPPhbl).hvplot(x="time", color="w", line_width=4)
        * (-1 * subset.KPPhbl).hvplot(x="time", color="k", line_width=2)
        * subset.dcl_base.hvplot(color="k", line_width=2)
        * (-1 * subset.Jq).hvplot.contour(
            levels=17, colormap=["black"], line_width=0.5, x="time"
        )
    )

    layout = flx + S2 * lines + N2 * lines + jq * lines

    return (layout).opts(shared_axes=True, transpose=True)


hvplot_dcl(station)

WARNING:param.Image03702: Image dimension latitude is not evenly sampled to relative tolerance of 0.001. Please use the QuadMesh element for irregularly sampled data or set a higher tolerance on hv.config.image_rtol or the rtol parameter in the Image constructor.
WARNING:param.Image03702: Image dimension latitude is not evenly sampled to relative tolerance of 0.001. Please use the QuadMesh element for irregularly sampled data or set a higher tolerance on hv.config.image_rtol or the rtol parameter in the Image constructor.
WARNING:param.Image03702: Image dimension latitude is not evenly sampled to relative tolerance of 0.001. Please use the QuadMesh element for irregularly sampled data or set a higher tolerance on hv.config.image_rtol or the rtol parameter in the Image constructor.

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-217-d4f5524c1d25> in <module>
     21 
     22 
---> 23 hvplot_dcl(station)

<ipython-input-217-d4f5524c1d25> in hvplot_dcl(subset)
      2 
      3     flx = subset.netflux.hvplot()
----> 4     S2 = subset.S2.hvplot(cmap="fire")
      5     N2 = subset.N2.hvplot()
      6     jq = subset.Jq.hvplot.image(x="time")

~/miniconda3/envs/dcpy2/lib/python3.7/site-packages/xarray/core/common.py in __getattr__(self, name)
    231                     return source[name]
    232         raise AttributeError(
--> 233             "{!r} object has no attribute {!r}".format(type(self).__name__, name)
    234         )
    235 

AttributeError: 'Dataset' object has no attribute 'S2'

subset = station.sel(latitude=0, method="nearest").sel(
    depth=slice(-60), time=slice("1999-01-10", "1999-01-11")
)
pump.plot.plot_dcl(subset)

calc uz
calc vz
calc S2
calc N2
calc shred2
Calc Ri

(<Figure size 1120x1120 with 8 Axes>,
 array([[<matplotlib.axes._subplots.AxesSubplot object at 0x2b82520a0a90>],
        [<matplotlib.axes._subplots.AxesSubplot object at 0x2b825301af50>],
        [<matplotlib.axes._subplots.AxesSubplot object at 0x2b8251bb0c10>],
        [<matplotlib.axes._subplots.AxesSubplot object at 0x2b82520d88d0>]],
       dtype=object))

/glade/u/home/dcherian/miniconda3/envs/dcpy2/lib/python3.7/site-packages/matplotlib/colors.py:1171: RuntimeWarning: invalid value encountered in less_equal
  mask |= resdat <= 0

subset = station.sel(latitude=0, method="nearest").sel(
    depth=slice(-60), time=slice("1999-10-13", "1999-10-15")
)

plot_dcl(subset)

calc uz
calc vz
calc S2
calc N2
calc shred2
Calc Ri

(<Figure size 1120x1120 with 8 Axes>,
 array([[<matplotlib.axes._subplots.AxesSubplot object at 0x2b213b692dd8>],
        [<matplotlib.axes._subplots.AxesSubplot object at 0x2b21c0401c50>],
        [<matplotlib.axes._subplots.AxesSubplot object at 0x2b2182f40518>],
        [<matplotlib.axes._subplots.AxesSubplot object at 0x2b213c663240>]],
       dtype=object))

subset = station.sel(latitude=0, method="nearest").sel(
    depth=slice(-80), time=slice("1999-03-21 12:00", "1999-03-23 12:00")
)

plot_dcl(subset)

calc uz
calc vz
calc S2
calc N2
calc shred2
Calc Ri

station

<xarray.Dataset>
Dimensions:        (depth: 185, latitude: 111, time: 24000)
Coordinates:
  * latitude       (latitude) float64 -3.075 -3.025 -2.975 ... 5.925 5.975 6.025
  * depth          (depth) float32 -1.25 -3.75 -6.25 ... -5658.0986 -5758.0986
    longitude      float32 -110.025
  * time           (time) datetime64[ns] 1998-12-31T18:00:00 ... 2001-09-26T17:00:00
Data variables:
    SSH            (time, latitude) float32 dask.array<chunksize=(6000, 1), meta=np.ndarray>
    KPPhbl         (time, latitude) float32 dask.array<chunksize=(6000, 1), meta=np.ndarray>
    u              (depth, time, latitude) float32 dask.array<chunksize=(185, 6000, 1), meta=np.ndarray>
    v              (depth, time, latitude) float32 dask.array<chunksize=(185, 6000, 1), meta=np.ndarray>
    w              (depth, time, latitude) float32 dask.array<chunksize=(185, 6000, 1), meta=np.ndarray>
    theta          (depth, time, latitude) float32 dask.array<chunksize=(185, 6000, 1), meta=np.ndarray>
    salt           (depth, time, latitude) float32 dask.array<chunksize=(185, 6000, 1), meta=np.ndarray>
    DFrI_TH        (depth, time, latitude) float32 dask.array<chunksize=(185, 6000, 1), meta=np.ndarray>
    VISrI_Um       (depth, time, latitude) float32 dask.array<chunksize=(185, 6000, 1), meta=np.ndarray>
    VISrI_Vm       (depth, time, latitude) float32 dask.array<chunksize=(185, 6000, 1), meta=np.ndarray>
    KPPdiffKzT     (depth, time, latitude) float32 dask.array<chunksize=(185, 6000, 1), meta=np.ndarray>
    KPPviscAz      (depth, time, latitude) float32 dask.array<chunksize=(185, 6000, 1), meta=np.ndarray>
    KPPg_TH        (depth, time, latitude) float32 dask.array<chunksize=(185, 6000, 1), meta=np.ndarray>
    Jq             (depth, time, latitude) float64 dask.array<chunksize=(185, 6000, 1), meta=np.ndarray>
    nonlocal_flux  (depth, time, latitude) float64 dask.array<chunksize=(185, 6000, 1), meta=np.ndarray>
    dens           (depth, time, latitude) float32 dask.array<chunksize=(185, 6000, 1), meta=np.ndarray>
    long           (time, latitude) float64 dask.array<chunksize=(6000, 1), meta=np.ndarray>
    short          (time, latitude) float64 nan nan nan nan ... nan nan nan nan
    sens           (time, latitude) float64 dask.array<chunksize=(6000, 1), meta=np.ndarray>
    lat            (time, latitude) float64 dask.array<chunksize=(6000, 1), meta=np.ndarray>
    netflux        (time, latitude) float64 dask.array<chunksize=(6000, 1), meta=np.ndarray>
    stress         (time, latitude) float64 dask.array<chunksize=(6000, 1), meta=np.ndarray>
    taux           (time, latitude) float64 dask.array<chunksize=(6000, 1), meta=np.ndarray>
    tauy           (time, latitude) float64 dask.array<chunksize=(6000, 1), meta=np.ndarray>
    usr            (time, latitude) float64 dask.array<chunksize=(6000, 1), meta=np.ndarray>
    tau            (time, latitude) float64 dask.array<chunksize=(6000, 1), meta=np.ndarray>
    hsb            (time, latitude) float64 dask.array<chunksize=(6000, 1), meta=np.ndarray>
    hlb            (time, latitude) float64 dask.array<chunksize=(6000, 1), meta=np.ndarray>
    hbb            (time, latitude) float64 dask.array<chunksize=(6000, 1), meta=np.ndarray>
    hsbb           (time, latitude) float64 dask.array<chunksize=(6000, 1), meta=np.ndarray>
    hlwebb         (time, latitude) float64 dask.array<chunksize=(6000, 1), meta=np.ndarray>
    tsr            (time, latitude) float64 dask.array<chunksize=(6000, 1), meta=np.ndarray>
    qsr            (time, latitude) float64 dask.array<chunksize=(6000, 1), meta=np.ndarray>
    zot            (time, latitude) float64 dask.array<chunksize=(6000, 1), meta=np.ndarray>
    zoq            (time, latitude) float64 dask.array<chunksize=(6000, 1), meta=np.ndarray>
    Cd             (time, latitude) float64 dask.array<chunksize=(6000, 1), meta=np.ndarray>
    Ch             (time, latitude) float64 dask.array<chunksize=(6000, 1), meta=np.ndarray>
    Ce             (time, latitude) float64 dask.array<chunksize=(6000, 1), meta=np.ndarray>
    L              (time, latitude) float64 dask.array<chunksize=(6000, 1), meta=np.ndarray>
    zet            (time, latitude) float64 dask.array<chunksize=(6000, 1), meta=np.ndarray>
    dter           (time, latitude) float64 dask.array<chunksize=(6000, 1), meta=np.ndarray>
    dqer           (time, latitude) float64 dask.array<chunksize=(6000, 1), meta=np.ndarray>
    tkt            (time, latitude) float64 dask.array<chunksize=(6000, 1), meta=np.ndarray>
    Urf            (time, latitude) float64 dask.array<chunksize=(6000, 1), meta=np.ndarray>
    Trf            (time, latitude) float64 dask.array<chunksize=(6000, 1), meta=np.ndarray>
    Qrf            (time, latitude) float64 dask.array<chunksize=(6000, 1), meta=np.ndarray>
    RHrf           (time, latitude) float64 dask.array<chunksize=(6000, 1), meta=np.ndarray>
    UrfN           (time, latitude) float64 dask.array<chunksize=(6000, 1), meta=np.ndarray>
    Rnl            (time, latitude) float64 dask.array<chunksize=(6000, 1), meta=np.ndarray>
    Le             (time, latitude) float64 dask.array<chunksize=(6000, 1), meta=np.ndarray>
    rhoa           (time, latitude) float64 dask.array<chunksize=(6000, 1), meta=np.ndarray>
    UN             (time, latitude) float64 dask.array<chunksize=(6000, 1), meta=np.ndarray>
    U10            (time, latitude) float64 dask.array<chunksize=(6000, 1), meta=np.ndarray>
    U10N           (time, latitude) float64 dask.array<chunksize=(6000, 1), meta=np.ndarray>
    Cdn_10         (time, latitude) float64 dask.array<chunksize=(6000, 1), meta=np.ndarray>
    Chn_10         (time, latitude) float64 dask.array<chunksize=(6000, 1), meta=np.ndarray>
    Cen_10         (time, latitude) float64 dask.array<chunksize=(6000, 1), meta=np.ndarray>
    RF             (time, latitude) float64 dask.array<chunksize=(6000, 1), meta=np.ndarray>
    Qs             (time, latitude) float64 dask.array<chunksize=(6000, 1), meta=np.ndarray>
    Evap           (time, latitude) float64 dask.array<chunksize=(6000, 1), meta=np.ndarray>
    T10            (time, latitude) float64 dask.array<chunksize=(6000, 1), meta=np.ndarray>
    Q10            (time, latitude) float64 dask.array<chunksize=(6000, 1), meta=np.ndarray>
    RH10           (time, latitude) float64 dask.array<chunksize=(6000, 1), meta=np.ndarray>
    huss           (time, latitude) float64 nan nan nan nan ... nan nan nan nan
    psl            (time, latitude) float64 nan nan nan nan ... nan nan nan nan
    tas            (time, latitude) float64 nan nan nan nan ... nan nan nan nan
    uas            (time, latitude) float64 nan nan nan nan ... nan nan nan nan
    vas            (time, latitude) float64 nan nan nan nan ... nan nan nan nan
    prra           (time, latitude) float64 nan nan nan nan ... nan nan nan nan
    rlds           (time, latitude) float64 nan nan nan nan ... nan nan nan nan
    rsds           (time, latitude) float64 nan nan nan nan ... nan nan nan nan
Attributes:
    title:     Station profile, index (i,j)=(1200,240)
    easting:   longitude
    northing:  latitude

subset = station.sel(latitude=0, method="nearest").sel(
    depth=slice(-50), time=slice("1999-05-19 00:00", "1999-05-22 00:00")
)

pump.plot.plot_dcl(subset)

calc uz
calc vz
calc S2
calc N2
calc shred2
Calc Ri

(<Figure size 1120x1400 with 10 Axes>,
 array([[<matplotlib.axes._subplots.AxesSubplot object at 0x2b0a48953e80>],
        [<matplotlib.axes._subplots.AxesSubplot object at 0x2b0a4959ceb8>],
        [<matplotlib.axes._subplots.AxesSubplot object at 0x2b0a4a728b70>],
        [<matplotlib.axes._subplots.AxesSubplot object at 0x2b0a4d50d550>],
        [<matplotlib.axes._subplots.AxesSubplot object at 0x2b0a4bde5ef0>]],
       dtype=object))

/glade/u/home/dcherian/miniconda3/envs/dcpy/lib/python3.6/site-packages/matplotlib/colors.py:1171: RuntimeWarning: invalid value encountered in less_equal
  mask |= resdat <= 0

station.sel(latitude=3.5, method="nearest").sel(
    depth=slice(-200), time=slice("1999-11-21 00:00", "1999-11-23 00:00")
).to_zarr("nov-1999-station.zarr", consolidated=True)

subset = station.sel(latitude=3.5, method="nearest").sel(
    depth=slice(-50), time=slice("1999-11-21 00:00", "1999-11-23 00:00")
)

f, ax = pump.plot.plot_dcl(subset)
ax[0, 0].set_title("(3.5°N, -110°W)")

calc uz
calc vz
calc S2
calc N2
calc shred2
Calc Ri

(<Figure size 1120x1400 with 10 Axes>,
 array([[<matplotlib.axes._subplots.AxesSubplot object at 0x2b82ac447410>],
        [<matplotlib.axes._subplots.AxesSubplot object at 0x2b82ac711310>],
        [<matplotlib.axes._subplots.AxesSubplot object at 0x2b82ae67c950>],
        [<matplotlib.axes._subplots.AxesSubplot object at 0x2b82ac715390>],
        [<matplotlib.axes._subplots.AxesSubplot object at 0x2b82ac6fec90>]],
       dtype=object))

/glade/u/home/dcherian/miniconda3/envs/dcpy2/lib/python3.7/site-packages/matplotlib/colors.py:1171: RuntimeWarning: invalid value encountered in less_equal
  mask |= resdat <= 0

subset = station.sel(latitude=0, method="nearest").sel(
    depth=slice(-60), time=slice("1999-05-14 12:00", "1999-05-19 12:00")
)

plot_dcl(subset)

calc uz
calc vz
calc S2
calc N2
calc shred2
Calc Ri

subset = station.sel(latitude=3.5, method="nearest").sel(
    depth=slice(-80), time=slice("1999-10-14", "1999-11-14")
)

plot_dcl(subset, shear_max=False, zeros_flux=False, lw=1)
plt.gcf().set_size_inches((10, 8))

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-137-49c0a699a7bf> in <module>
      1 subset = station.sel(latitude=3.5, method="nearest").sel(depth=slice(-80), time=slice("1999-10-14", "1999-11-14"))
      2 
----> 3 plot_dcl(subset, shear_max=False, zeros_flux=False, lw=1)
      4 plt.gcf().set_size_inches((10,8))

TypeError: plot_dcl() got an unexpected keyword argument 'lw'

station.theta.isel(depth=0).sel(latitude=3.5, method="nearest").sel(
    time=slice("1999-12-14", "2000-01-14")
).plot(
    x="time",
)

[<matplotlib.lines.Line2D at 0x2b2188d085c0>]

subset = station.sel(latitude=3.5, method="nearest").sel(
    depth=slice(-80), time=slice("1999-10-14", "1999-11-14")
)

f, axx = plot_dcl(subset, shear_max=False, lw=1, zeros_flux=False)
f.set_size_inches((11, 8))

calc uz
calc vz
calc S2
calc N2
calc shred2
Calc Ri

subset = station.sel(latitude=0, method="nearest").sel(
    depth=slice(-40), time=slice("1999-10-13 12:00", "1999-10-15 12:00")
)

pump.plot.plot_dcl(subset, shear_max=False)

calc uz
calc vz
calc S2
calc N2
calc shred2
Calc Ri

(<Figure size 1120x1120 with 8 Axes>,
 array([[<matplotlib.axes._subplots.AxesSubplot object at 0x2b826d6cf210>],
        [<matplotlib.axes._subplots.AxesSubplot object at 0x2b826e5ccfd0>],
        [<matplotlib.axes._subplots.AxesSubplot object at 0x2b826e5d7b10>],
        [<matplotlib.axes._subplots.AxesSubplot object at 0x2b826e5f3a50>]],
       dtype=object))

/glade/u/home/dcherian/miniconda3/envs/dcpy2/lib/python3.7/site-packages/matplotlib/colors.py:1171: RuntimeWarning: invalid value encountered in less_equal
  mask |= resdat <= 0

subset = station.sel(
    depth=slice(-40), time=slice("1999-05-14 12:00", "1999-05-17 12:00")
)

pump.plot.plot_dcl(subset, shear_max=False)

calc uz
calc vz
calc S2
calc N2
calc shred2
Calc Ri

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-53-3feb20bb07c2> in <module>
      3 )
      4 
----> 5 pump.plot.plot_dcl(subset, shear_max=False)

~/pump/pump/plot.py in plot_dcl(subset, shear_max, zeros_flux, lw, kpp_terms)
    690 
    691     if kpp_terms:
--> 692         kpp = calc_kpp_hbl(sub)
    693 
    694     f, axx = plt.subplots(

NameError: name 'sub' is not defined

subset = station.sel(latitude=3.5, method="nearest").sel(
    depth=slice(-40), time=slice("1999-11-21", "1999-11-22")
)
subset

xarray.Dataset

• Dimensions:
  • depth: 16
  • time: 48
• Coordinates: (4)
  • depth
    (depth)
    float32
    -1.25 -3.75 -6.25 ... -36.25 -38.75

    array([ -1.25,  -3.75,  -6.25,  -8.75, -11.25, -13.75, -16.25, -18.75, -21.25,
           -23.75, -26.25, -28.75, -31.25, -33.75, -36.25, -38.75], dtype=float32)

  • longitude
    ()
    float32
    ...

    array(-110.025, dtype=float32)

  • latitude
    ()
    float64
    3.525

    array(3.5250001)

  • time
    (time)
    datetime64[ns]
    1999-11-21 ... 1999-11-22T23:00:00

    array(['1999-11-21T00:00:00.000000000', '1999-11-21T01:00:00.000000000',
           '1999-11-21T02:00:00.000000000', '1999-11-21T03:00:00.000000000',
           '1999-11-21T04:00:00.000000000', '1999-11-21T05:00:00.000000000',
           '1999-11-21T06:00:00.000000000', '1999-11-21T07:00:00.000000000',
           '1999-11-21T08:00:00.000000000', '1999-11-21T09:00:00.000000000',
           '1999-11-21T10:00:00.000000000', '1999-11-21T11:00:00.000000000',
           '1999-11-21T12:00:00.000000000', '1999-11-21T13:00:00.000000000',
           '1999-11-21T14:00:00.000000000', '1999-11-21T15:00:00.000000000',
           '1999-11-21T16:00:00.000000000', '1999-11-21T17:00:00.000000000',
           '1999-11-21T18:00:00.000000000', '1999-11-21T19:00:00.000000000',
           '1999-11-21T20:00:00.000000000', '1999-11-21T21:00:00.000000000',
           '1999-11-21T22:00:00.000000000', '1999-11-21T23:00:00.000000000',
           '1999-11-22T00:00:00.000000000', '1999-11-22T01:00:00.000000000',
           '1999-11-22T02:00:00.000000000', '1999-11-22T03:00:00.000000000',
           '1999-11-22T04:00:00.000000000', '1999-11-22T05:00:00.000000000',
           '1999-11-22T06:00:00.000000000', '1999-11-22T07:00:00.000000000',
           '1999-11-22T08:00:00.000000000', '1999-11-22T09:00:00.000000000',
           '1999-11-22T10:00:00.000000000', '1999-11-22T11:00:00.000000000',
           '1999-11-22T12:00:00.000000000', '1999-11-22T13:00:00.000000000',
           '1999-11-22T14:00:00.000000000', '1999-11-22T15:00:00.000000000',
           '1999-11-22T16:00:00.000000000', '1999-11-22T17:00:00.000000000',
           '1999-11-22T18:00:00.000000000', '1999-11-22T19:00:00.000000000',
           '1999-11-22T20:00:00.000000000', '1999-11-22T21:00:00.000000000',
           '1999-11-22T22:00:00.000000000', '1999-11-22T23:00:00.000000000'],
          dtype='datetime64[ns]')

• Data variables: (22)
  • SSH
    (time)
    float32
    dask.array<chunksize=(48,), meta=np.ndarray>

    
    
    
    
      
    
        
     
     Array 
     Chunk 
      
      
    
        
     Bytes 
     192 B 
     
     192 B 
        
     Shape 
     (48,) 
     
     (48,) 
        
     Count 
     2225 Tasks 
     1 Chunks 
        
     Type 
     float32 
     numpy.ndarray 
      
    
    
    
    
    
    
    

  • KPPhbl
    (time)
    float32
    dask.array<chunksize=(48,), meta=np.ndarray>

    
    
    
    
      
    
        
     
     Array 
     Chunk 
      
      
    
        
     Bytes 
     192 B 
     
     192 B 
        
     Shape 
     (48,) 
     
     (48,) 
        
     Count 
     2225 Tasks 
     1 Chunks 
        
     Type 
     float32 
     numpy.ndarray 
      
    
    
    
    
    
    
    

  • u
    (depth, time)
    float32
    dask.array<chunksize=(16, 48), meta=np.ndarray>

    
    
    
    
      
    
        
     
     Array 
     Chunk 
      
      
    
        
     Bytes 
     3.07 kB 
     
     3.07 kB 
        
     Shape 
     (16, 48) 
     
     (16, 48) 
        
     Count 
     2225 Tasks 
     1 Chunks 
        
     Type 
     float32 
     numpy.ndarray 
      
    
    
    
    
    
    
    

  • v
    (depth, time)
    float32
    dask.array<chunksize=(16, 48), meta=np.ndarray>

    
    
    
    
      
    
        
     
     Array 
     Chunk 
      
      
    
        
     Bytes 
     3.07 kB 
     
     3.07 kB 
        
     Shape 
     (16, 48) 
     
     (16, 48) 
        
     Count 
     2225 Tasks 
     1 Chunks 
        
     Type 
     float32 
     numpy.ndarray 
      
    
    
    
    
    
    
    

  • w
    (depth, time)
    float32
    dask.array<chunksize=(16, 48), meta=np.ndarray>

    
    
    
    
      
    
        
     
     Array 
     Chunk 
      
      
    
        
     Bytes 
     3.07 kB 
     
     3.07 kB 
        
     Shape 
     (16, 48) 
     
     (16, 48) 
        
     Count 
     2225 Tasks 
     1 Chunks 
        
     Type 
     float32 
     numpy.ndarray 
      
    
    
    
    
    
    
    

  • theta
    (depth, time)
    float32
    dask.array<chunksize=(16, 48), meta=np.ndarray>

    
    
    
    
      
    
        
     
     Array 
     Chunk 
      
      
    
        
     Bytes 
     3.07 kB 
     
     3.07 kB 
        
     Shape 
     (16, 48) 
     
     (16, 48) 
        
     Count 
     2225 Tasks 
     1 Chunks 
        
     Type 
     float32 
     numpy.ndarray 
      
    
    
    
    
    
    
    

  • salt
    (depth, time)
    float32
    dask.array<chunksize=(16, 48), meta=np.ndarray>

    
    
    
    
      
    
        
     
     Array 
     Chunk 
      
      
    
        
     Bytes 
     3.07 kB 
     
     3.07 kB 
        
     Shape 
     (16, 48) 
     
     (16, 48) 
        
     Count 
     2225 Tasks 
     1 Chunks 
        
     Type 
     float32 
     numpy.ndarray 
      
    
    
    
    
    
    
    

  • DFrI_TH
    (depth, time)
    float32
    dask.array<chunksize=(16, 48), meta=np.ndarray>

    
    
    
    
      
    
        
     
     Array 
     Chunk 
      
      
    
        
     Bytes 
     3.07 kB 
     
     3.07 kB 
        
     Shape 
     (16, 48) 
     
     (16, 48) 
        
     Count 
     2225 Tasks 
     1 Chunks 
        
     Type 
     float32 
     numpy.ndarray 
      
    
    
    
    
    
    
    

  • VISrI_Um
    (depth, time)
    float32
    dask.array<chunksize=(16, 48), meta=np.ndarray>

    
    
    
    
      
    
        
     
     Array 
     Chunk 
      
      
    
        
     Bytes 
     3.07 kB 
     
     3.07 kB 
        
     Shape 
     (16, 48) 
     
     (16, 48) 
        
     Count 
     2225 Tasks 
     1 Chunks 
        
     Type 
     float32 
     numpy.ndarray 
      
    
    
    
    
    
    
    

  • VISrI_Vm
    (depth, time)
    float32
    dask.array<chunksize=(16, 48), meta=np.ndarray>

    
    
    
    
      
    
        
     
     Array 
     Chunk 
      
      
    
        
     Bytes 
     3.07 kB 
     
     3.07 kB 
        
     Shape 
     (16, 48) 
     
     (16, 48) 
        
     Count 
     2225 Tasks 
     1 Chunks 
        
     Type 
     float32 
     numpy.ndarray 
      
    
    
    
    
    
    
    

  • KPPdiffKzT
    (depth, time)
    float32
    dask.array<chunksize=(16, 48), meta=np.ndarray>

    
    
    
    
      
    
        
     
     Array 
     Chunk 
      
      
    
        
     Bytes 
     3.07 kB 
     
     3.07 kB 
        
     Shape 
     (16, 48) 
     
     (16, 48) 
        
     Count 
     2225 Tasks 
     1 Chunks 
        
     Type 
     float32 
     numpy.ndarray 
      
    
    
    
    
    
    
    

  • KPPviscAz
    (depth, time)
    float32
    dask.array<chunksize=(16, 48), meta=np.ndarray>

    
    
    
    
      
    
        
     
     Array 
     Chunk 
      
      
    
        
     Bytes 
     3.07 kB 
     
     3.07 kB 
        
     Shape 
     (16, 48) 
     
     (16, 48) 
        
     Count 
     2225 Tasks 
     1 Chunks 
        
     Type 
     float32 
     numpy.ndarray 
      
    
    
    
    
    
    
    

  • KPPg_TH
    (depth, time)
    float32
    dask.array<chunksize=(16, 48), meta=np.ndarray>

    
    
    
    
      
    
        
     
     Array 
     Chunk 
      
      
    
        
     Bytes 
     3.07 kB 
     
     3.07 kB 
        
     Shape 
     (16, 48) 
     
     (16, 48) 
        
     Count 
     2225 Tasks 
     1 Chunks 
        
     Type 
     float32 
     numpy.ndarray 
      
    
    
    
    
    
    
    

  • Jq
    (depth, time)
    float64
    dask.array<chunksize=(16, 48), meta=np.ndarray>

    
    
    
    
      
    
        
     
     Array 
     Chunk 
      
      
    
        
     Bytes 
     6.14 kB 
     
     6.14 kB 
        
     Shape 
     (16, 48) 
     
     (16, 48) 
        
     Count 
     6007 Tasks 
     1 Chunks 
        
     Type 
     float64 
     numpy.ndarray 
      
    
    
    
    
    
    
    

  • nonlocal_flux
    (depth, time)
    float64
    dask.array<chunksize=(16, 48), meta=np.ndarray>

    
    
    
    
      
    
        
     
     Array 
     Chunk 
      
      
    
        
     Bytes 
     6.14 kB 
     
     6.14 kB 
        
     Shape 
     (16, 48) 
     
     (16, 48) 
        
     Count 
     3343 Tasks 
     1 Chunks 
        
     Type 
     float64 
     numpy.ndarray 
      
    
    
    
    
    
    
    

  • long
    (time)
    float64
    dask.array<chunksize=(48,), meta=np.ndarray>

    
    
    
    
      
    
        
     
     Array 
     Chunk 
      
      
    
        
     Bytes 
     384 B 
     
     384 B 
        
     Shape 
     (48,) 
     
     (48,) 
        
     Count 
     5778 Tasks 
     1 Chunks 
        
     Type 
     float64 
     numpy.ndarray 
      
    
    
    
    
    
    
    

  • short
    (time)
    float64
    0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0

    array([  0.        ,   0.        ,   0.        ,   0.        ,
            26.67723925,  53.3544785 ,  80.03171775, 234.07998805,
           388.12825834, 542.17652864, 637.58096106, 732.98539349,
           828.38982591, 705.72158814, 583.05335038, 460.38511261,
           313.98412565, 167.5831387 ,  21.18215175,  14.1214345 ,
             7.06071725,   0.        ,   0.        ,   0.        ,
             0.        ,   0.        ,   0.        ,   0.        ,
            29.58307234,  59.16614469,  88.74921703, 268.283195  ,
           447.81717296, 627.35115093, 693.29195576, 759.23276058,
           825.17356541, 705.6597719 , 586.1459784 , 466.6321849 ,
           318.92149786, 171.21081082,  23.50012379,  15.66674919,
             7.8333746 ,   0.        ,   0.        ,   0.        ])

  • sens
    (time)
    float64
    dask.array<chunksize=(48,), meta=np.ndarray>

    
    
    
    
      
    
        
     
     Array 
     Chunk 
      
      
    
        
     Bytes 
     384 B 
     
     384 B 
        
     Shape 
     (48,) 
     
     (48,) 
        
     Count 
     1093602 Tasks 
     1 Chunks 
        
     Type 
     float64 
     numpy.ndarray 
      
    
    
    
    
    
    
    

  • lat
    (time)
    float64
    dask.array<chunksize=(48,), meta=np.ndarray>

    
    
    
    
      
    
        
     
     Array 
     Chunk 
      
      
    
        
     Bytes 
     384 B 
     
     384 B 
        
     Shape 
     (48,) 
     
     (48,) 
        
     Count 
     1093602 Tasks 
     1 Chunks 
        
     Type 
     float64 
     numpy.ndarray 
      
    
    
    
    
    
    
    

  • random_offset
    ()
    float64
    -12.5

    array(-12.5)

  • netflux
    (time)
    float64
    dask.array<chunksize=(48,), meta=np.ndarray>

    
    
    
    
      
    
        
     
     Array 
     Chunk 
      
      
    
        
     Bytes 
     384 B 
     
     384 B 
        
     Shape 
     (48,) 
     
     (48,) 
        
     Count 
     1108256 Tasks 
     1 Chunks 
        
     Type 
     float64 
     numpy.ndarray 
      
    
    
    
    
    
    
    

  • stress
    (time)
    float64
    dask.array<chunksize=(48,), meta=np.ndarray>

    long_name :

    wind stress

    units :

    N/m^2

    
    
    
    
      
    
        
     
     Array 
     Chunk 
      
      
    
        
     Bytes 
     384 B 
     
     384 B 
        
     Shape 
     (48,) 
     
     (48,) 
        
     Count 
     1093158 Tasks 
     1 Chunks 
        
     Type 
     float64 
     numpy.ndarray 
      
    
    
    
    
    
    
    

• Attributes: (3)

  title :

  Station profile, index (i,j)=(1200,240)

  easting :

  longitude

  northing :

  latitude

subset = station.sel(latitude=3.5, method="nearest").sel(
    depth=slice(-40), time=slice("1999-11-21", "1999-11-22")
)

f, axx = plot_dcl(subset, shear_max=False, lw=1.5)

calc uz
calc vz
calc S2
calc N2
calc shred2
Calc Ri

/glade/u/home/dcherian/miniconda3/envs/dcpy2/lib/python3.7/site-packages/matplotlib/colors.py:1171: RuntimeWarning: invalid value encountered in less_equal
  mask |= resdat <= 0

station.sel(latitude=3.5, method="nearest").sel(
    time=slice("1999-10-21 11:00", "1999-10-21 18:00"), depth=slice(-100)
).v.plot()

<matplotlib.collections.QuadMesh at 0x2ba0c58020d0>

station.sel(latitude=3.5, method="nearest").sel(
    time=slice("1999-10-21 11:00", "1999-10-21 18:00"), depth=slice(-100)
).KPPdiffKzT.plot(x="time", robust=True, norm=mpl.colors.LogNorm(1e-5, 5e-3))

<matplotlib.collections.QuadMesh at 0x2ba0c335a3d0>

(
    station.sel(latitude=3.5, method="nearest")
    .sel(time=slice("1999-10-21 11:00", "1999-10-21 18:00"))
    .KPPdiffKzT.plot.line(
        hue="time", xscale="log", y="depth", ylim=(-100, 0), xlim=(1e-6, None)
    )
)

[<matplotlib.lines.Line2D at 0x2ba0c59ba790>,
 <matplotlib.lines.Line2D at 0x2ba0c354a110>,
 <matplotlib.lines.Line2D at 0x2ba0c2c365d0>,
 <matplotlib.lines.Line2D at 0x2ba0c3beee10>,
 <matplotlib.lines.Line2D at 0x2ba094f7b290>,
 <matplotlib.lines.Line2D at 0x2ba0c4766c90>,
 <matplotlib.lines.Line2D at 0x2ba0c4766e50>,
 <matplotlib.lines.Line2D at 0x2ba0c4766b90>]

subset = station.sel(latitude=3.5, method="nearest").sel(
    depth=slice(-80), time=slice("1999-11-21", "1999-11-22")
)

f, axx = plot_dcl(subset, shear_max=False, lw=1)
axx.flat[-1].set_ylim(-45, 0)

calc uz
calc vz
calc S2
calc N2
calc shred2
Calc Ri

(-45.0, 0.0)

/glade/u/home/dcherian/miniconda3/envs/dcpy2/lib/python3.7/site-packages/matplotlib/colors.py:1171: RuntimeWarning: invalid value encountered in less_equal
  mask |= resdat <= 0

subset.KPPdiffKzT.where(subset.KPPdiffKzT > 0).plot(
    x="time", vmin=1e-4, robust=True, norm=mpl.colors.LogNorm(), cmap=mpl.cm.Spectral_r
)

<matplotlib.collections.QuadMesh at 0x2ba4dc5bd910>

/glade/u/home/dcherian/miniconda3/envs/dcpy2/lib/python3.7/site-packages/matplotlib/colors.py:1171: RuntimeWarning: invalid value encountered in less_equal
  mask |= resdat <= 0

wind = forcing.sel(latitude=3.5, method="nearest").sel(time=subset.time)[
    ["uas", "vas"]
]  # .assign_coords(latitude=xr.full_like(subset.time, 3.5))


quiver(wind.expand_dims("latitude"), u="uas", v="vas", x="time", y="latitude")
quiver(
    subset[["u", "v"]].isel(depth=5).expand_dims("latitude").compute(),
    u="u",
    v="v",
    x="time",
    y="latitude",
    scale=6,
    color="r",
)

<matplotlib.quiver.Quiver at 0x2aef0f765690>

Inferring KPP hbl

offeq = (
    station.sel(latitude=3.5, method="nearest")
    .sel(depth=slice(-150), time=slice("1999-11-21", "1999-11-22"))
    .pipe(pump.calc_reduced_shear)
)

eq = (
    station.sel(latitude=0, method="nearest")
    .sel(depth=slice(-150), time=slice("1999-11-21", "1999-11-22"))
    .pipe(pump.calc_reduced_shear)
)

calc uz
calc vz
calc S2
calc N2
calc shred2
Calc Ri
calc uz
calc vz
calc S2
calc N2
calc shred2
Calc Ri

subset = eq[
    [
        "u",
        "v",
        "theta",
        "salt",
        "tau",
        "usr",
        "uas",
        "vas",
        "netflux",
        "short",
        "KPPhbl",
        "Jq",
        "KPPdiffKzT",
        "taux",
        "tauy",
    ]
].compute()

subset_o = offeq[
    [
        "u",
        "v",
        "theta",
        "salt",
        "tau",
        "usr",
        "uas",
        "vas",
        "netflux",
        "short",
        "KPPhbl",
        "Jq",
        "KPPdiffKzT",
        "taux",
        "tauy",
    ]
].load()

pump.plot.debug_kpp_plot(subset_o.isel(depth=slice(25), time=slice(240)))

/glade/u/home/dcherian/miniconda3/envs/dcpy2/lib/python3.7/site-packages/xarray/core/computation.py:604: RuntimeWarning: divide by zero encountered in log10
  result_data = func(*input_data)
/glade/u/home/dcherian/miniconda3/envs/dcpy2/lib/python3.7/site-packages/xarray/plot/plot.py:906: UserWarning: The following kwargs were not used by contour: 'label'
  primitive = ax.contour(x, y, z, **kwargs)

subset = (
    station.sel(latitude=0, method="nearest")
    .sel(time=slice("1999-05-19", "1999-05-21"))[
        [
            "u",
            "v",
            "theta",
            "salt",
            "tau",
            "usr",
            "uas",
            "vas",
            "netflux",
            "short",
            "KPPhbl",
            "Jq",
            "KPPdiffKzT",
            "taux",
            "tauy",
        ]
    ]
    .load()
)

pump.plot.debug_kpp_plot(subset.sel(depth=slice(-40)))

/glade/u/home/dcherian/miniconda3/envs/dcpy2/lib/python3.7/site-packages/xarray/core/computation.py:604: RuntimeWarning: divide by zero encountered in log10
  result_data = func(*input_data)
/glade/u/home/dcherian/miniconda3/envs/dcpy2/lib/python3.7/site-packages/xarray/plot/plot.py:906: UserWarning: The following kwargs were not used by contour: 'label'
  primitive = ax.contour(x, y, z, **kwargs)

pump.calc.calc_kpp_terms(subset_o.isel(time=slice(120)), debug=False)

21.562137693168097
hbl_top = 23.75, hbl_bottom = 21.25
21.508353306605443
hbl_top = 23.75, hbl_bottom = 21.25
21.645652719144437
hbl_top = 23.75, hbl_bottom = 21.25
21.71008658196208
hbl_top = 23.75, hbl_bottom = 21.25
22.01677270574992
hbl_top = 23.75, hbl_bottom = 21.25
21.948488957295535
hbl_top = 23.75, hbl_bottom = 21.25
22.08246661873677
hbl_top = 23.75, hbl_bottom = 21.25
22.145413632993883
hbl_top = 23.75, hbl_bottom = 21.25
22.195302295606304
hbl_top = 23.75, hbl_bottom = 21.25
22.066310006841196
hbl_top = 23.75, hbl_bottom = 21.25
22.24133188754695
hbl_top = 23.75, hbl_bottom = 21.25
22.323145858177032
hbl_top = 23.75, hbl_bottom = 21.25
22.054759928208135
hbl_top = 23.75, hbl_bottom = 21.25
21.74766525119885
hbl_top = 23.75, hbl_bottom = 21.25
21.96514183635163
hbl_top = 23.75, hbl_bottom = 21.25
22.06654327265203
hbl_top = 23.75, hbl_bottom = 21.25
21.947758413989142
hbl_top = 23.75, hbl_bottom = 21.25
21.63542539179921
hbl_top = 23.75, hbl_bottom = 21.25
21.819160014704995
hbl_top = 23.75, hbl_bottom = 21.25
21.905131451567286
hbl_top = 23.75, hbl_bottom = 21.25
21.675680029445495
hbl_top = 23.75, hbl_bottom = 21.25
21.399447172041846
hbl_top = 23.75, hbl_bottom = 21.25
21.548286334707633
hbl_top = 23.75, hbl_bottom = 21.25
21.61795460845824
hbl_top = 23.75, hbl_bottom = 21.25
20.37833714859105
hbl_top = 21.25, hbl_bottom = 18.75
19.140231830217452
hbl_top = 21.25, hbl_bottom = 18.75
19.096249865203298
hbl_top = 21.25, hbl_bottom = 18.75
23.382178043646125
hbl_top = 23.75, hbl_bottom = 21.25
23.50966158578035
hbl_top = 23.75, hbl_bottom = 21.25
23.585500953976588
hbl_top = 23.75, hbl_bottom = 21.25
23.175004482337314
hbl_top = 23.75, hbl_bottom = 21.25
23.384766310970008
hbl_top = 23.75, hbl_bottom = 21.25
23.528257604716416
hbl_top = 23.75, hbl_bottom = 21.25
22.072581679580615
hbl_top = 23.75, hbl_bottom = 21.25
22.34555184575829
hbl_top = 23.75, hbl_bottom = 21.25
22.547326046697705
hbl_top = 23.75, hbl_bottom = 21.25
20.566370443381764
hbl_top = 21.25, hbl_bottom = 18.75
20.885728731343622
hbl_top = 21.25, hbl_bottom = 18.75
21.135747235870983
hbl_top = 21.25, hbl_bottom = 18.75
17.714394601459023
hbl_top = 18.75, hbl_bottom = 16.25
18.10017036092813
hbl_top = 18.75, hbl_bottom = 16.25
18.414005608084878
hbl_top = 18.75, hbl_bottom = 16.25
15.924321361017425
hbl_top = 16.25, hbl_bottom = 13.75
16.223217566875334
hbl_top = 16.25, hbl_bottom = 13.75
16.467129401164126
hbl_top = 18.75, hbl_bottom = 16.25
19.072772221913507
hbl_top = 21.25, hbl_bottom = 18.75
19.274353175531903
hbl_top = 21.25, hbl_bottom = 18.75
19.40389903137693
hbl_top = 21.25, hbl_bottom = 18.75
23.00510599628578
hbl_top = 23.75, hbl_bottom = 21.25
23.081517310258004
hbl_top = 23.75, hbl_bottom = 21.25
25.29325671831957
hbl_top = 26.25, hbl_bottom = 23.75
23.0226086847083
hbl_top = 23.75, hbl_bottom = 21.25
20.552656354372083
hbl_top = 21.25, hbl_bottom = 18.75
18.28252336519945
hbl_top = 18.75, hbl_bottom = 16.25
18.779560889008007
hbl_top = 21.25, hbl_bottom = 18.75
18.008655349586295
hbl_top = 18.75, hbl_bottom = 16.25
19.280331854438167
hbl_top = 21.25, hbl_bottom = 18.75
19.309814493463044
hbl_top = 21.25, hbl_bottom = 18.75
19.41810992085268
hbl_top = 21.25, hbl_bottom = 18.75
18.85041107862146
hbl_top = 21.25, hbl_bottom = 18.75
18.960659903799932
hbl_top = 21.25, hbl_bottom = 18.75
19.089269173136927
hbl_top = 21.25, hbl_bottom = 18.75
17.199039569411184
hbl_top = 18.75, hbl_bottom = 16.25
17.34087264407167
hbl_top = 18.75, hbl_bottom = 16.25
17.46837905655693
hbl_top = 18.75, hbl_bottom = 16.25
16.576202668448467
hbl_top = 18.75, hbl_bottom = 16.25
16.72000617012054
hbl_top = 18.75, hbl_bottom = 16.25
16.82070902151038
hbl_top = 18.75, hbl_bottom = 16.25
14.311303502352393
hbl_top = 16.25, hbl_bottom = 13.75
14.449883474952593
hbl_top = 16.25, hbl_bottom = 13.75
12.97664444256843
hbl_top = 13.75, hbl_bottom = 11.25
12.308063213597052
hbl_top = 13.75, hbl_bottom = 11.25
11.629846090776118
hbl_top = 13.75, hbl_bottom = 11.25
11.302498463693176
hbl_top = 13.75, hbl_bottom = 11.25
11.689271294818639
hbl_top = 13.75, hbl_bottom = 11.25
12.551872559607364
hbl_top = 13.75, hbl_bottom = 11.25
12.899624972263439
hbl_top = 13.75, hbl_bottom = 11.25
15.320320872532465
hbl_top = 16.25, hbl_bottom = 13.75
13.266098910964843
hbl_top = 13.75, hbl_bottom = 11.25
20.185909412052297
hbl_top = 21.25, hbl_bottom = 18.75
20.263276129684527
hbl_top = 21.25, hbl_bottom = 18.75
21.451838066266646
hbl_top = 23.75, hbl_bottom = 21.25
21.572420735693957
hbl_top = 23.75, hbl_bottom = 21.25
21.646085937821635
hbl_top = 23.75, hbl_bottom = 21.25
22.099115445591085
hbl_top = 23.75, hbl_bottom = 21.25
22.229340914866313
hbl_top = 23.75, hbl_bottom = 21.25
22.312718087711616
hbl_top = 23.75, hbl_bottom = 21.25
22.356680388175935
hbl_top = 23.75, hbl_bottom = 21.25
22.497350973679385
hbl_top = 23.75, hbl_bottom = 21.25
22.591083279557186
hbl_top = 23.75, hbl_bottom = 21.25
21.9087378738579
hbl_top = 23.75, hbl_bottom = 21.25
22.06556794313892
hbl_top = 23.75, hbl_bottom = 21.25
22.173620796320574
hbl_top = 23.75, hbl_bottom = 21.25
22.669471057444387
hbl_top = 23.75, hbl_bottom = 21.25
22.799594958203578
hbl_top = 23.75, hbl_bottom = 21.25
22.8833885305723
hbl_top = 23.75, hbl_bottom = 21.25
24.208945276986924
hbl_top = 26.25, hbl_bottom = 23.75
24.299756917753438
hbl_top = 26.25, hbl_bottom = 23.75
24.353854355505064
hbl_top = 26.25, hbl_bottom = 23.75
25.84316562291642
hbl_top = 26.25, hbl_bottom = 23.75
25.87783520542498
hbl_top = 26.25, hbl_bottom = 23.75
26.962273062442545
hbl_top = 28.75, hbl_bottom = 26.25
25.19759209741487
hbl_top = 26.25, hbl_bottom = 23.75
25.151653710482968
hbl_top = 26.25, hbl_bottom = 23.75
25.130125445174507
hbl_top = 26.25, hbl_bottom = 23.75
21.668189094329772
hbl_top = 23.75, hbl_bottom = 21.25
19.98708492183505
hbl_top = 21.25, hbl_bottom = 18.75
20.045446275133962
hbl_top = 21.25, hbl_bottom = 18.75
19.427630219412183
hbl_top = 21.25, hbl_bottom = 18.75
18.75707976358253
hbl_top = 21.25, hbl_bottom = 18.75
18.9889746172224
hbl_top = 21.25, hbl_bottom = 18.75
19.599299529504425
hbl_top = 21.25, hbl_bottom = 18.75
19.266948848627543
hbl_top = 21.25, hbl_bottom = 18.75
19.47056143556383
hbl_top = 21.25, hbl_bottom = 18.75
21.684969131391153
hbl_top = 23.75, hbl_bottom = 21.25
21.659742256487426
hbl_top = 23.75, hbl_bottom = 21.25
21.808345541212656
hbl_top = 23.75, hbl_bottom = 21.25
21.877846769970848
hbl_top = 23.75, hbl_bottom = 21.25
22.023883452697486
hbl_top = 23.75, hbl_bottom = 21.25
22.034244267607754
hbl_top = 23.75, hbl_bottom = 21.25
22.157669929831194
hbl_top = 23.75, hbl_bottom = 21.25
22.215620204514106
hbl_top = 23.75, hbl_bottom = 21.25
21.831126611702437
hbl_top = 23.75, hbl_bottom = 21.25
21.86102003826511
hbl_top = 23.75, hbl_bottom = 21.25
21.993609792830966
hbl_top = 23.75, hbl_bottom = 21.25
22.05571209633549
hbl_top = 23.75, hbl_bottom = 21.25
21.572837583359252
hbl_top = 23.75, hbl_bottom = 21.25
21.627671765482592
hbl_top = 23.75, hbl_bottom = 21.25
21.772180410250826
hbl_top = 23.75, hbl_bottom = 21.25
21.83976707521497
hbl_top = 23.75, hbl_bottom = 21.25
20.797675805190554
hbl_top = 21.25, hbl_bottom = 18.75
20.703021585772692
hbl_top = 21.25, hbl_bottom = 18.75
20.989311927329435
hbl_top = 21.25, hbl_bottom = 18.75

xarray.Dataset

• Dimensions:
  • depth: 60
  • time: 48
• Coordinates: (4)
  • time
    (time)
    datetime64[ns]
    1999-11-21 ... 1999-11-22T23:00:00

    array(['1999-11-21T00:00:00.000000000', '1999-11-21T01:00:00.000000000',
           '1999-11-21T02:00:00.000000000', '1999-11-21T03:00:00.000000000',
           '1999-11-21T04:00:00.000000000', '1999-11-21T05:00:00.000000000',
           '1999-11-21T06:00:00.000000000', '1999-11-21T07:00:00.000000000',
           '1999-11-21T08:00:00.000000000', '1999-11-21T09:00:00.000000000',
           '1999-11-21T10:00:00.000000000', '1999-11-21T11:00:00.000000000',
           '1999-11-21T12:00:00.000000000', '1999-11-21T13:00:00.000000000',
           '1999-11-21T14:00:00.000000000', '1999-11-21T15:00:00.000000000',
           '1999-11-21T16:00:00.000000000', '1999-11-21T17:00:00.000000000',
           '1999-11-21T18:00:00.000000000', '1999-11-21T19:00:00.000000000',
           '1999-11-21T20:00:00.000000000', '1999-11-21T21:00:00.000000000',
           '1999-11-21T22:00:00.000000000', '1999-11-21T23:00:00.000000000',
           '1999-11-22T00:00:00.000000000', '1999-11-22T01:00:00.000000000',
           '1999-11-22T02:00:00.000000000', '1999-11-22T03:00:00.000000000',
           '1999-11-22T04:00:00.000000000', '1999-11-22T05:00:00.000000000',
           '1999-11-22T06:00:00.000000000', '1999-11-22T07:00:00.000000000',
           '1999-11-22T08:00:00.000000000', '1999-11-22T09:00:00.000000000',
           '1999-11-22T10:00:00.000000000', '1999-11-22T11:00:00.000000000',
           '1999-11-22T12:00:00.000000000', '1999-11-22T13:00:00.000000000',
           '1999-11-22T14:00:00.000000000', '1999-11-22T15:00:00.000000000',
           '1999-11-22T16:00:00.000000000', '1999-11-22T17:00:00.000000000',
           '1999-11-22T18:00:00.000000000', '1999-11-22T19:00:00.000000000',
           '1999-11-22T20:00:00.000000000', '1999-11-22T21:00:00.000000000',
           '1999-11-22T22:00:00.000000000', '1999-11-22T23:00:00.000000000'],
          dtype='datetime64[ns]')

  • latitude
    ()
    float64
    3.525

    array(3.5250001)

  • longitude
    ()
    float32
    -110.025

    array(-110.025, dtype=float32)

  • depth
    (depth)
    float32
    -1.25 -3.75 ... -146.25 -148.75

    array([  -1.25,   -3.75,   -6.25,   -8.75,  -11.25,  -13.75,  -16.25,  -18.75,
            -21.25,  -23.75,  -26.25,  -28.75,  -31.25,  -33.75,  -36.25,  -38.75,
            -41.25,  -43.75,  -46.25,  -48.75,  -51.25,  -53.75,  -56.25,  -58.75,
            -61.25,  -63.75,  -66.25,  -68.75,  -71.25,  -73.75,  -76.25,  -78.75,
            -81.25,  -83.75,  -86.25,  -88.75,  -91.25,  -93.75,  -96.25,  -98.75,
           -101.25, -103.75, -106.25, -108.75, -111.25, -113.75, -116.25, -118.75,
           -121.25, -123.75, -126.25, -128.75, -131.25, -133.75, -136.25, -138.75,
           -141.25, -143.75, -146.25, -148.75], dtype=float32)

• Data variables: (10)
  • hbl
    (time)
    float32
    22.5 22.5 22.5 ... 22.5 22.5 20.0

    units :

    m

    long_name :

    KPP boundary layer depth

    array([22.5      , 22.5      , 22.5      , 22.5      , 22.5      ,
           22.5      , 20.       ,  6.381697 ,  4.263205 ,  3.4403372,
            2.9695106,  2.6451426,  3.6016703,  5.0653596,  7.9433293,
           15.794245 , 17.5      , 17.5      , 20.       , 20.       ,
           17.5      , 17.5      , 15.       , 12.5      , 12.5      ,
           12.5      , 12.5      , 12.5      , 12.5      , 12.5      ,
           12.5      ,  7.532503 ,  5.94778  ,  5.227539 ,  4.692258 ,
            4.279661 ,  5.1836925,  6.4958186,  9.302697 , 22.322956 ,
           20.       , 20.       , 20.       , 22.5      , 22.5      ,
           22.5      , 22.5      , 20.       ], dtype=float32)

  • hekman
    (time)
    float32
    nan nan nan nan ... nan nan nan nan

    units :

    m

    long_name :

    Ekman depth

    array([          nan,           nan,           nan,           nan,
                     nan,           nan, 4.5774882e+17, 4.7896335e+17,
           5.0082648e+17, 5.0251086e+17, 5.0428256e+17, 5.0616406e+17,
           5.2138254e+17, 5.3779629e+17, 5.5541012e+17, 5.4295485e+17,
                     nan,           nan,           nan,           nan,
                     nan,           nan,           nan,           nan,
                     nan,           nan,           nan,           nan,
                     nan,           nan, 4.6887079e+17, 5.1898498e+17,
           5.7419720e+17, 5.7926588e+17, 5.8443565e+17, 5.8969482e+17,
           5.8385772e+17, 5.7795458e+17, 5.7205707e+17, 5.6601821e+17,
                     nan,           nan,           nan,           nan,
                     nan,           nan,           nan,           nan],
          dtype=float32)

  • hmonob
    (time)
    float32
    nan nan nan nan ... nan nan nan nan

    units :

    m

    long_name :

    Monin Obukhov length scale

    array([       nan,        nan,        nan,        nan,        nan,
                  nan, 20.855568 ,  6.381697 ,  4.263205 ,  3.4403372,
            2.9695106,  2.6451426,  3.6016703,  5.0653596,  7.9433293,
           15.794245 ,        nan,        nan,        nan,        nan,
                  nan,        nan,        nan,        nan,        nan,
                  nan,        nan,        nan,        nan,        nan,
           22.86214  ,  7.532503 ,  5.94778  ,  5.227539 ,  4.692258 ,
            4.279661 ,  5.1836925,  6.4958186,  9.302697 , 22.322956 ,
                  nan,        nan,        nan,        nan,        nan,
                  nan,        nan,        nan], dtype=float32)

  • hunlimit
    (time)
    float32
    22.5 22.5 22.5 ... 22.5 22.5 20.0

    units :

    m

    long_name :

    KPP boundary layer depth before limiting to min(hekman, hmonob) under stable forcing

    array([22.5, 22.5, 22.5, 22.5, 22.5, 22.5, 20. , 22.5, 22.5, 22.5, 20. ,
           17.5, 17.5, 20. , 22.5, 22.5, 17.5, 17.5, 20. , 20. , 17.5, 17.5,
           15. , 12.5, 12.5, 12.5, 12.5, 12.5, 12.5, 12.5, 12.5, 20. , 22.5,
           22.5, 22.5, 22.5, 22.5, 25. , 25. , 25. , 20. , 20. , 20. , 22.5,
           22.5, 22.5, 22.5, 20. ], dtype=float32)

  • Rib
    (time, depth)
    float32
    -0.0 -0.0043675723 ... 10.773784

    long_name :

    KPP bulk Ri

    array([[-0.0000000e+00, -4.3675723e-03,  4.2244170e-02, ...,
             7.7815323e+00,  7.8425851e+00,  7.8425851e+00],
           [-0.0000000e+00, -6.9185831e-03,  3.6911014e-02, ...,
             7.9089103e+00,  7.9628172e+00,  7.9628172e+00],
           [-0.0000000e+00, -1.2001271e-02,  3.7520770e-02, ...,
             8.1349211e+00,  8.1810265e+00,  8.1810265e+00],
           ...,
           [-0.0000000e+00,  1.5479587e-03,  6.0123704e-02, ...,
             1.0430234e+01,  1.0703924e+01,  1.0703924e+01],
           [-0.0000000e+00,  3.2030668e-03,  5.7468832e-02, ...,
             1.0437504e+01,  1.0726508e+01,  1.0726508e+01],
           [-0.0000000e+00,  1.6819524e-03,  6.5243363e-02, ...,
             1.0474560e+01,  1.0773784e+01,  1.0773784e+01]], dtype=float32)

  • db
    (time, depth)
    float32
    -0.0 -7.231339e-07 ... 0.0

    long_name :

    KPP bulk Ri Δb

    array([[-0.0000000e+00, -7.2313389e-07,  2.6032822e-05, ...,
             4.0595579e+00,  4.1478744e+00,  0.0000000e+00],
           [-0.0000000e+00, -1.0847009e-06,  2.2778719e-05, ...,
             4.0506921e+00,  4.1388516e+00,  0.0000000e+00],
           [-0.0000000e+00, -1.8078348e-06,  2.2778719e-05, ...,
             4.0408864e+00,  4.1288724e+00,  0.0000000e+00],
           ...,
           [-0.0000000e+00,  3.6156695e-07,  4.6642137e-05, ...,
             4.2691140e+00,  4.3606405e+00,  0.0000000e+00],
           [-0.0000000e+00,  7.2313389e-07,  4.3388034e-05, ...,
             4.2715244e+00,  4.3630934e+00,  0.0000000e+00],
           [-0.0000000e+00,  3.6156695e-07,  4.6642137e-05, ...,
             4.2739353e+00,  4.3653803e+00,  0.0000000e+00]], dtype=float32)

  • dV2
    (time, depth)
    float32
    0.0 4.941014e-05 ... 0.37995458 0.0

    long_name :

    KPP bulk Ri ΔV^2 (resolved)

    array([[0.0000000e+00, 4.9410140e-05, 2.7091338e-04, ..., 4.9504235e-01,
            5.0191677e-01, 0.0000000e+00],
           [0.0000000e+00, 5.0319199e-05, 2.7736116e-04, ..., 4.8523474e-01,
            4.9237049e-01, 0.0000000e+00],
           [0.0000000e+00, 4.8988302e-05, 2.6826747e-04, ..., 4.6952727e-01,
            4.7700959e-01, 0.0000000e+00],
           ...,
           [0.0000000e+00, 6.2482315e-05, 3.4160132e-04, ..., 3.8168761e-01,
            3.7987494e-01, 0.0000000e+00],
           [0.0000000e+00, 5.9221282e-05, 3.2668465e-04, ..., 3.8159496e-01,
            3.7949473e-01, 0.0000000e+00],
           [0.0000000e+00, 5.5920602e-05, 3.1129509e-04, ..., 3.8229433e-01,
            3.7995458e-01, 0.0000000e+00]], dtype=float32)

  • dVt2
    (time, depth)
    float32
    0.00038809222 0.00011615871 ... 0.0

    long_name :

    KPP bulk Ri ΔV_t^2 (unresolved)

    array([[0.00038809, 0.00011616, 0.00034533, ..., 0.02664895, 0.02697443,
            0.        ],
           [0.0003921 , 0.00010646, 0.00033976, ..., 0.02693341, 0.02740182,
            0.        ],
           [0.00039596, 0.00010165, 0.00033883, ..., 0.02720605, 0.0276792 ,
            0.        ],
           ...,
           [0.00041859, 0.00017109, 0.00043417, ..., 0.02761424, 0.02751215,
            0.        ],
           [0.00041561, 0.00016654, 0.0004283 , ..., 0.02765273, 0.02726333,
            0.        ],
           [0.00041283, 0.00015905, 0.0004036 , ..., 0.02573573, 0.02523087,
            0.        ]], dtype=float32)

  • KT
    (time, depth)
    float32
    0.0050009997 0.012616122 ... 1e-06

    array([[5.0009997e-03, 1.2616122e-02, 1.9529518e-02, ..., 1.0000000e-06,
            1.0000000e-06, 1.0000000e-06],
           [5.0009997e-03, 1.2785150e-02, 1.9790806e-02, ..., 1.0000000e-06,
            1.0000000e-06, 1.0000000e-06],
           [5.0009997e-03, 1.2957962e-02, 2.0076469e-02, ..., 1.0000000e-06,
            1.0000000e-06, 1.0000000e-06],
           ...,
           [4.9999743e-03, 1.3126186e-02, 2.0311574e-02, ..., 1.0000000e-06,
            1.0000000e-06, 1.0000000e-06],
           [4.9964371e-03, 1.3064079e-02, 2.0179978e-02, ..., 1.0000000e-06,
            1.0000000e-06, 1.0000000e-06],
           [4.9997200e-03, 1.2097055e-02, 1.8087992e-02, ..., 1.0000000e-06,
            1.0000000e-06, 1.0000000e-06]], dtype=float32)

  • KM
    (time, depth)
    float32
    0.0050999997 0.008073231 ... 1e-04

    array([[5.09999972e-03, 8.07323121e-03, 1.25805112e-02, ...,
            9.99999975e-05, 9.99999975e-05, 9.99999975e-05],
           [5.09999972e-03, 8.12131632e-03, 1.26566077e-02, ...,
            9.99999975e-05, 9.99999975e-05, 9.99999975e-05],
           [5.09999972e-03, 8.18027463e-03, 1.27679305e-02, ...,
            9.99999975e-05, 9.99999975e-05, 9.99999975e-05],
           ...,
           [5.09897433e-03, 8.72506946e-03, 1.35796582e-02, ...,
            9.99999975e-05, 9.99999975e-05, 9.99999975e-05],
           [5.09543717e-03, 8.56258348e-03, 1.32943578e-02, ...,
            9.99999975e-05, 9.99999975e-05, 9.99999975e-05],
           [5.09872008e-03, 8.02413933e-03, 1.21125169e-02, ...,
            9.99999975e-05, 9.99999975e-05, 9.99999975e-05]], dtype=float32)

• Attributes: (0)

h = pump.calc.calc_kpp_terms(subset_o.isel(time=slice(120)))

h.hbl.plot(marker=".")
oldh.hbl.plot(color="r")
subset_o.KPPhbl.plot(yincrease=False)
plt.legend(["with interp", "no interp", "MIT"])
plt.gcf().savefig("../images/kpp-interp-check.png")

KPP boundary layer depth vs Monin Obukhov length vs Rib contour over 20 years.

import pump.KPP

kpp = pump.calc.calc_kpp_terms(
    station.sel(latitude=[0, 3.5], method="nearest")
    .sel(time=slice("1999-01-02", "1999-12-30"))
    .chunk({"depth": -1, "time": 1000}),
    debug=False,
)
kpp["hmonob"] = kpp.hmonob.where(kpp.hmonob < 100)

kpp.load(scheduler=client)

xarray.Dataset

• Dimensions:
  • depth: 125
  • latitude: 2
  • time: 8712
• Coordinates: (4)
  • depth
    (depth)
    float32
    -1.25 -3.75 ... -487.70044

    array([  -1.25   ,   -3.75   ,   -6.25   ,   -8.75   ,  -11.25   ,  -13.75   ,
            -16.25   ,  -18.75   ,  -21.25   ,  -23.75   ,  -26.25   ,  -28.75   ,
            -31.25   ,  -33.75   ,  -36.25   ,  -38.75   ,  -41.25   ,  -43.75   ,
            -46.25   ,  -48.75   ,  -51.25   ,  -53.75   ,  -56.25   ,  -58.75   ,
            -61.25   ,  -63.75   ,  -66.25   ,  -68.75   ,  -71.25   ,  -73.75   ,
            -76.25   ,  -78.75   ,  -81.25   ,  -83.75   ,  -86.25   ,  -88.75   ,
            -91.25   ,  -93.75   ,  -96.25   ,  -98.75   , -101.25   , -103.75   ,
           -106.25   , -108.75   , -111.25   , -113.75   , -116.25   , -118.75   ,
           -121.25   , -123.75   , -126.25   , -128.75   , -131.25   , -133.75   ,
           -136.25   , -138.75   , -141.25   , -143.75   , -146.25   , -148.75   ,
           -151.25   , -153.75   , -156.25   , -158.75   , -161.25   , -163.75   ,
           -166.25   , -168.75   , -171.25   , -173.75   , -176.25   , -178.75   ,
           -181.25   , -183.75   , -186.25   , -188.75   , -191.25   , -193.75   ,
           -196.25   , -198.75   , -201.25   , -203.75   , -206.25   , -208.75   ,
           -211.25   , -213.75   , -216.25   , -218.75   , -221.25   , -223.75   ,
           -226.25   , -228.75   , -231.25   , -233.75   , -236.25   , -238.75   ,
           -241.25   , -243.75   , -246.25   , -248.75   , -251.36874, -254.2363 ,
           -257.37625, -260.8145 , -264.5794 , -268.70193, -273.21616, -278.1592 ,
           -283.57184, -289.4987 , -295.98856, -303.0949 , -310.8764 , -319.39716,
           -328.72736, -338.94394, -350.13116, -362.3811 , -375.79474, -390.4827 ,
           -406.56604, -424.17734, -443.4617 , -464.57806, -487.70044],
          dtype=float32)

  • longitude
    ()
    float32
    -110.025

    array(-110.025, dtype=float32)

  • latitude
    (latitude)
    float64
    0.025 3.525

    array([0.025, 3.525])

  • time
    (time)
    datetime64[ns]
    1999-01-02 ... 1999-12-30T23:00:00

    array(['1999-01-02T00:00:00.000000000', '1999-01-02T01:00:00.000000000',
           '1999-01-02T02:00:00.000000000', ..., '1999-12-30T21:00:00.000000000',
           '1999-12-30T22:00:00.000000000', '1999-12-30T23:00:00.000000000'],
          dtype='datetime64[ns]')

• Data variables: (10)
  • hbl
    (time, latitude)
    float32
    5.0 10.0 5.0 10.0 ... 30.0 7.5 32.5

    units :

    m

    long_name :

    KPP boundary layer depth

    array([[ 5. , 10. ],
           [ 5. , 10. ],
           [ 5. , 15. ],
           ...,
           [ 7.5, 30. ],
           [ 7.5, 30. ],
           [ 7.5, 32.5]], dtype=float32)

  • hekman
    (time, latitude)
    float32
    nan nan nan nan ... nan nan nan nan

    units :

    m

    long_name :

    Ekman depth

    array([[nan, nan],
           [nan, nan],
           [nan, nan],
           ...,
           [nan, nan],
           [nan, nan],
           [nan, nan]], dtype=float32)

  • hmonob
    (time, latitude)
    float32
    nan nan nan nan ... nan nan nan nan

    units :

    m

    long_name :

    Monin Obukhov length scale

    array([[nan, nan],
           [nan, nan],
           [nan, nan],
           ...,
           [nan, nan],
           [nan, nan],
           [nan, nan]], dtype=float32)

  • hunlimit
    (time, latitude)
    float32
    5.0 10.0 5.0 10.0 ... 30.0 7.5 32.5

    units :

    m

    long_name :

    KPP boundary layer depth before limiting to min(hekman, hmonob) under stable forcing

    array([[ 5. , 10. ],
           [ 5. , 10. ],
           [ 5. , 15. ],
           ...,
           [ 7.5, 30. ],
           [ 7.5, 30. ],
           [ 7.5, 32.5]], dtype=float32)

  • Rib
    (time, latitude, depth)
    float32
    -0.0 0.2503229 ... 235.58582

    long_name :

    KPP bulk Ri

    array([[[-0.0000000e+00,  2.5032291e-01,  4.8184463e-01, ...,
              8.5198547e+01,  7.9783852e+01,  7.9783852e+01],
            [-0.0000000e+00,  6.3407227e-02,  1.6666980e-01, ...,
              1.4464644e+02,  1.5555121e+02,  1.5555121e+02]],
    
           [[-0.0000000e+00,  2.1697785e-01,  4.8251191e-01, ...,
              8.2865799e+01,  7.7752548e+01,  7.7752548e+01],
            [-0.0000000e+00,  2.3602573e-02,  1.5087619e-01, ...,
              1.4718178e+02,  1.5820285e+02,  1.5820285e+02]],
    
           [[-0.0000000e+00,  2.0862587e-01,  4.8596352e-01, ...,
              7.9324699e+01,  7.4693375e+01,  7.4693375e+01],
            [-0.0000000e+00,  2.6829834e-03,  7.4484199e-02, ...,
              1.4657593e+02,  1.5752759e+02,  1.5752759e+02]],
    
           ...,
    
           [[-0.0000000e+00,  1.5106463e-01,  2.1721765e-01, ...,
              3.3305534e+01,  3.4165131e+01,  3.4165131e+01],
            [-0.0000000e+00, -2.2021918e-02, -1.3400100e-02, ...,
              2.3884598e+02,  2.4341263e+02,  2.4341263e+02]],
    
           [[-0.0000000e+00,  1.3765422e-01,  2.0930561e-01, ...,
              3.3610939e+01,  3.4511612e+01,  3.4511612e+01],
            [-0.0000000e+00, -2.6850004e-02, -2.5710473e-02, ...,
              2.3948860e+02,  2.4501222e+02,  2.4501222e+02]],
    
           [[-0.0000000e+00,  1.3321401e-01,  2.0516455e-01, ...,
              3.3925499e+01,  3.4867664e+01,  3.4867664e+01],
            [-0.0000000e+00, -2.7953496e-02, -3.2485351e-02, ...,
              2.2933173e+02,  2.3558582e+02,  2.3558582e+02]]], dtype=float32)

  • db
    (time, latitude, depth)
    float32
    -0.0 0.0002921461 ... 18.329153 0.0

    long_name :

    KPP bulk Ri Δb

    array([[[-0.00000000e+00,  2.92146113e-04,  2.81154457e-03, ...,
              9.64758301e+00,  1.02288885e+01,  0.00000000e+00],
            [-0.00000000e+00,  1.15701423e-05,  6.94208575e-05, ...,
              2.16456642e+01,  2.28267021e+01,  0.00000000e+00]],
    
           [[-0.00000000e+00,  1.93799890e-04,  2.28510308e-03, ...,
              9.60716248e+00,  1.01870747e+01,  0.00000000e+00],
            [-0.00000000e+00,  4.33880359e-06,  6.36357872e-05, ...,
              2.16410599e+01,  2.28218784e+01,  0.00000000e+00]],
    
           [[-0.00000000e+00,  1.47519313e-04,  1.86857802e-03, ...,
              9.57851028e+00,  1.01570539e+01,  0.00000000e+00],
            [-0.00000000e+00,  3.61566947e-07,  2.71175213e-05, ...,
              2.15756779e+01,  2.27569656e+01,  0.00000000e+00]],
    
           ...,
    
           [[-0.00000000e+00,  9.97924799e-05,  5.81399654e-04, ...,
              1.48661089e+01,  1.56966076e+01,  0.00000000e+00],
            [-0.00000000e+00, -3.25410269e-06, -5.42350426e-06, ...,
              1.73361778e+01,  1.83598938e+01,  0.00000000e+00]],
    
           [[-0.00000000e+00,  8.53298043e-05,  5.14871324e-04, ...,
              1.48435965e+01,  1.56708765e+01,  0.00000000e+00],
            [-0.00000000e+00, -4.70037048e-06, -9.76230785e-06, ...,
              1.73198528e+01,  1.83443890e+01,  0.00000000e+00]],
    
           [[-0.00000000e+00,  7.52059277e-05,  4.62805707e-04, ...,
              1.48244095e+01,  1.56507730e+01,  0.00000000e+00],
            [-0.00000000e+00, -5.42350426e-06, -1.19317092e-05, ...,
              1.73048019e+01,  1.83291531e+01,  0.00000000e+00]]],
          dtype=float32)

  • dV2
    (time, latitude, depth)
    float32
    0.0 0.00049714063 ... 0.0

    long_name :

    KPP bulk Ri ΔV^2 (resolved)

    array([[[0.00000000e+00, 4.97140631e-04, 4.21184534e-03, ...,
             8.76374915e-02, 9.84975249e-02, 0.00000000e+00],
            [0.00000000e+00, 2.31424783e-05, 6.10678326e-05, ...,
             1.04122072e-01, 1.02895468e-01, 0.00000000e+00]],
    
           [[0.00000000e+00, 2.98121187e-04, 3.22474726e-03, ...,
             9.06843692e-02, 1.01800725e-01, 0.00000000e+00],
            [0.00000000e+00, 2.88854189e-05, 6.92425747e-05, ...,
             1.00757688e-01, 9.96746123e-02, 0.00000000e+00]],
    
           [[0.00000000e+00, 1.77986658e-04, 2.44946941e-03, ...,
             9.58900973e-02, 1.07278921e-01, 0.00000000e+00],
            [0.00000000e+00, 8.93922697e-06, 3.50564369e-05, ...,
             9.83087644e-02, 9.73211378e-02, 0.00000000e+00]],
    
           ...,
    
           [[0.00000000e+00, 2.94306810e-04, 1.71662017e-03, ...,
             4.11460787e-01, 4.27512527e-01, 0.00000000e+00],
            [0.00000000e+00, 3.77778924e-05, 1.74939283e-04, ...,
             6.99972967e-03, 9.26842261e-03, 0.00000000e+00]],
    
           [[0.00000000e+00, 2.84027221e-04, 1.57086982e-03, ...,
             4.07791436e-01, 4.23078984e-01, 0.00000000e+00],
            [0.00000000e+00, 3.93800074e-05, 1.75155816e-04, ...,
             7.55817862e-03, 9.58914496e-03, 0.00000000e+00]],
    
           [[0.00000000e+00, 2.54923420e-04, 1.42970495e-03, ...,
             4.04223919e-01, 4.18756485e-01, 0.00000000e+00],
            [0.00000000e+00, 4.20315264e-05, 1.81414740e-04, ...,
             8.54127109e-03, 1.03969378e-02, 0.00000000e+00]]], dtype=float32)

  • dVt2
    (time, latitude, depth)
    float32
    0.00014306219 0.00066993636 ... 0.0

    long_name :

    KPP bulk Ri ΔV_t^2 (unresolved)

    array([[[0.00014306, 0.00066994, 0.00162312, ..., 0.02559898,
             0.02970997, 0.        ],
            [0.0003225 , 0.00015933, 0.00035545, ..., 0.04552326,
             0.04385172, 0.        ]],
    
           [[0.00014078, 0.00059506, 0.0015111 , ..., 0.02525203,
             0.02921846, 0.        ],
            [0.00032754, 0.00015494, 0.00035253, ..., 0.04627858,
             0.04458245, 0.        ]],
    
           [[0.00013828, 0.00052911, 0.00139563, ..., 0.02486057,
             0.02870441, 0.        ],
            [0.00033144, 0.00012582, 0.00032901, ..., 0.04888919,
             0.04714222, 0.        ]],
    
           ...,
    
           [[0.00021248, 0.00036629, 0.00095996, ..., 0.03489474,
             0.03192103, 0.        ],
            [0.00026127, 0.00010999, 0.0002298 , ..., 0.06558336,
             0.06615862, 0.        ]],
    
           [[0.00020689, 0.00033586, 0.00088903, ..., 0.0338385 ,
             0.03099648, 0.        ],
            [0.00026154, 0.00013568, 0.00020455, ..., 0.06476197,
             0.06528218, 0.        ]],
    
           [[0.00020105, 0.00030963, 0.00082607, ..., 0.03274563,
             0.03010559, 0.        ],
            [0.00026222, 0.00015199, 0.00018588, ..., 0.06691624,
             0.06740551, 0.        ]]], dtype=float32)

  • KT
    (time, latitude, depth)
    float32
    0.00012966603 ... 1e-06

    array([[[1.29666034e-04, 2.18233955e-03, 9.19872400e-05, ...,
             9.99999997e-07, 9.99999997e-07, 9.99999997e-07],
            [5.88858093e-04, 7.83956889e-03, 7.58001441e-03, ...,
             9.99999997e-07, 9.99999997e-07, 9.99999997e-07]],
    
           [[1.40170687e-05, 2.16404232e-03, 1.03821752e-04, ...,
             9.99999997e-07, 9.99999997e-07, 9.99999997e-07],
            [4.34163306e-03, 8.00566562e-03, 7.86850788e-03, ...,
             9.99999997e-07, 9.99999997e-07, 9.99999997e-07]],
    
           [[9.99999997e-07, 2.14412622e-03, 1.17710180e-04, ...,
             9.99999997e-07, 9.99999997e-07, 9.99999997e-07],
            [4.95108636e-03, 1.13102421e-02, 1.49047645e-02, ...,
             9.99999997e-07, 9.99999997e-07, 9.99999997e-07]],
    
           ...,
    
           [[2.24266201e-03, 4.82293451e-03, 3.10947536e-03, ...,
             9.99999997e-07, 9.99999997e-07, 9.99999997e-07],
            [5.00099966e-03, 1.46211786e-02, 2.62511652e-02, ...,
             9.99999997e-07, 9.99999997e-07, 9.99999997e-07]],
    
           [[2.71571265e-03, 4.70282603e-03, 3.04097799e-03, ...,
             9.99999997e-07, 9.99999997e-07, 9.99999997e-07],
            [5.00099966e-03, 1.45563791e-02, 2.61273608e-02, ...,
             9.99999997e-07, 9.99999997e-07, 9.99999997e-07]],
    
           [[2.78192759e-03, 4.58586263e-03, 2.99450336e-03, ...,
             9.99999997e-07, 9.99999997e-07, 9.99999997e-07],
            [5.00099966e-03, 1.46936942e-02, 2.76978035e-02, ...,
             9.99999997e-07, 9.99999997e-07, 9.99999997e-07]]], dtype=float32)

  • KM
    (time, latitude, depth)
    float32
    0.00022866603 ... 1e-04

    array([[[2.28666031e-04, 1.07810984e-03, 1.90987237e-04, ...,
             9.99999975e-05, 9.99999975e-05, 9.99999975e-05],
            [6.87858090e-04, 3.68432608e-03, 3.92221520e-03, ...,
             9.99999975e-05, 9.99999975e-05, 9.99999975e-05]],
    
           [[1.13017064e-04, 1.06433465e-03, 2.02821742e-04, ...,
             9.99999975e-05, 9.99999975e-05, 9.99999975e-05],
            [4.44063311e-03, 3.84459435e-03, 4.20552772e-03, ...,
             9.99999975e-05, 9.99999975e-05, 9.99999975e-05]],
    
           [[9.99999975e-05, 1.05444994e-03, 2.16710177e-04, ...,
             9.99999975e-05, 9.99999975e-05, 9.99999975e-05],
            [5.05008642e-03, 5.27657289e-03, 7.19794771e-03, ...,
             9.99999975e-05, 9.99999975e-05, 9.99999975e-05]],
    
           ...,
    
           [[2.34166207e-03, 3.19488416e-03, 2.35595019e-03, ...,
             9.99999975e-05, 9.99999975e-05, 9.99999975e-05],
            [5.09999972e-03, 9.05576814e-03, 1.57470144e-02, ...,
             9.99999975e-05, 9.99999975e-05, 9.99999975e-05]],
    
           [[2.81471270e-03, 3.11372965e-03, 2.30692979e-03, ...,
             9.99999975e-05, 9.99999975e-05, 9.99999975e-05],
            [5.09999972e-03, 9.21308808e-03, 1.60208605e-02, ...,
             9.99999975e-05, 9.99999975e-05, 9.99999975e-05]],
    
           [[2.88092764e-03, 3.03988135e-03, 2.28201272e-03, ...,
             9.99999975e-05, 9.99999975e-05, 9.99999975e-05],
            [5.09999972e-03, 9.49921738e-03, 1.70747973e-02, ...,
             9.99999975e-05, 9.99999975e-05, 9.99999975e-05]]], dtype=float32)

• Attributes: (0)

kpp = pump.calc.calc_kpp_terms(
    station.sel(latitude=[3.5], method="nearest")
    .sel(time=slice("1999-01-02", "1999-01-06"))
    .chunk({"depth": -1, "time": 1000}),
    debug=True,
)

---------------------------------------------------------------------------
KeyboardInterrupt                         Traceback (most recent call last)
<ipython-input-75-276c3fdda056> in <module>
      3     .sel(time=slice("1999-01-02", "1999-01-06"))
      4     .chunk({"depth": -1, "time": 1000}),
----> 5     debug=True,
      6 )

~/pump/pump/calc.py in calc_kpp_terms(station, debug)
   1132     h.dVt2.attrs["long_name"] = "KPP bulk Ri ΔV_t^2 (unresolved)"
   1133     if debug:
-> 1134         h.hbl.plot()
   1135         # kpp.KPPhbl.plot()
   1136         station.KPPhbl.plot()

~/miniconda3/envs/dcpy2/lib/python3.7/site-packages/xarray/plot/plot.py in __call__(self, **kwargs)
    444 
    445     def __call__(self, **kwargs):
--> 446         return plot(self._da, **kwargs)
    447 
    448     @functools.wraps(hist)

~/miniconda3/envs/dcpy2/lib/python3.7/site-packages/xarray/plot/plot.py in plot(darray, row, col, col_wrap, ax, hue, rtol, subplot_kws, **kwargs)
    162 
    163     """
--> 164     darray = darray.squeeze().compute()
    165 
    166     plot_dims = set(darray.dims)

~/miniconda3/envs/dcpy2/lib/python3.7/site-packages/xarray/core/dataarray.py in compute(self, **kwargs)
    826         """
    827         new = self.copy(deep=False)
--> 828         return new.load(**kwargs)
    829 
    830     def persist(self, **kwargs) -> "DataArray":

~/miniconda3/envs/dcpy2/lib/python3.7/site-packages/xarray/core/dataarray.py in load(self, **kwargs)
    800         dask.array.compute
    801         """
--> 802         ds = self._to_temp_dataset().load(**kwargs)
    803         new = self._from_temp_dataset(ds)
    804         self._variable = new._variable

~/miniconda3/envs/dcpy2/lib/python3.7/site-packages/xarray/core/dataset.py in load(self, **kwargs)
    652 
    653             # evaluate all the dask arrays simultaneously
--> 654             evaluated_data = da.compute(*lazy_data.values(), **kwargs)
    655 
    656             for k, data in zip(lazy_data, evaluated_data):

~/miniconda3/envs/dcpy2/lib/python3.7/site-packages/dask/base.py in compute(*args, **kwargs)
    435     keys = [x.__dask_keys__() for x in collections]
    436     postcomputes = [x.__dask_postcompute__() for x in collections]
--> 437     results = schedule(dsk, keys, **kwargs)
    438     return repack([f(r, *a) for r, (f, a) in zip(results, postcomputes)])
    439 

~/miniconda3/envs/dcpy2/lib/python3.7/site-packages/distributed/client.py in get(self, dsk, keys, restrictions, loose_restrictions, resources, sync, asynchronous, direct, retries, priority, fifo_timeout, actors, **kwargs)
   2593                     should_rejoin = False
   2594             try:
-> 2595                 results = self.gather(packed, asynchronous=asynchronous, direct=direct)
   2596             finally:
   2597                 for f in futures.values():

~/miniconda3/envs/dcpy2/lib/python3.7/site-packages/distributed/client.py in gather(self, futures, errors, direct, asynchronous)
   1892                 direct=direct,
   1893                 local_worker=local_worker,
-> 1894                 asynchronous=asynchronous,
   1895             )
   1896 

~/miniconda3/envs/dcpy2/lib/python3.7/site-packages/distributed/client.py in sync(self, func, asynchronous, callback_timeout, *args, **kwargs)
    776         else:
    777             return sync(
--> 778                 self.loop, func, *args, callback_timeout=callback_timeout, **kwargs
    779             )
    780 

~/miniconda3/envs/dcpy2/lib/python3.7/site-packages/distributed/utils.py in sync(loop, func, callback_timeout, *args, **kwargs)
    343     else:
    344         while not e.is_set():
--> 345             e.wait(10)
    346     if error[0]:
    347         typ, exc, tb = error[0]

~/miniconda3/envs/dcpy2/lib/python3.7/threading.py in wait(self, timeout)
    550             signaled = self._flag
    551             if not signaled:
--> 552                 signaled = self._cond.wait(timeout)
    553             return signaled
    554 

~/miniconda3/envs/dcpy2/lib/python3.7/threading.py in wait(self, timeout)
    298             else:
    299                 if timeout > 0:
--> 300                     gotit = waiter.acquire(True, timeout)
    301                 else:
    302                     gotit = waiter.acquire(False)

KeyboardInterrupt: 

kpp.hbl.sel(time="1999-01").plot.line(hue="latitude")

[<matplotlib.lines.Line2D at 0x2b543fbe8490>,
 <matplotlib.lines.Line2D at 0x2b543658ad10>]

xr.Dataset(
    {"mit": station.KPPhbl.reindex_like(kpp.hbl), "smyth": kpp.hbl}
).plot.scatter(x="mit", y="smyth", hue="latitude", hue_style="discrete", s=4)
dcpy.plots.line45()

kpp.hunlimit.hvplot.line() * kpp.hmonob.hvplot.line()

v2 = (
    (station.v.sel(time=dh.time, depth=slice(-100), latitude=slice(-2, 2)) ** 2)
    .mean(["depth", "latitude"])
    .compute()
)
v2.attrs["long_name"] = "$v^2$"
v2

xarray.DataArray

'v'

• time: 1802

• 0.011888596 0.012170398 0.015604051 ... 0.024090406 0.023737494

  array([0.0118886 , 0.0121704 , 0.01560405, ..., 0.03052988, 0.02409041,
         0.02373749], dtype=float32)

• Coordinates: (2)
  • longitude
    ()
    float32
    -110.025

    array(-110.025, dtype=float32)

  • time
    (time)
    datetime64[ns]
    1999-01-03T07:00:00 ... 1999-12-30T08:00:00

    array(['1999-01-03T07:00:00.000000000', '1999-01-03T08:00:00.000000000',
           '1999-01-05T07:00:00.000000000', ..., '1999-12-29T14:00:00.000000000',
           '1999-12-30T07:00:00.000000000', '1999-12-30T08:00:00.000000000'],
          dtype='datetime64[ns]')

• Attributes: (1)

  long_name :

  $v^2$

v = station.v.sel(time=dh.time, depth=slice(-100), latitude=slice(-2, 2)).compute()
tiwv = pump.calc.tiw_avg_filter_v(v.sel(latitude=[0], method="nearest"))

/glade/u/home/dcherian/python/xfilter/xfilter/xfilter.py:175: UserWarning: The 'gappy' kwarg is now deprecated.
  warnings.warn("The 'gappy' kwarg is now deprecated.")

tiwv.mean("latitude").plot(x="time")
v.mean(["latitude", "depth"]).plot(x="time")

[<matplotlib.lines.Line2D at 0x2b0a3540cc18>]

xfilter.bandpass(
    v.chunk({"time": -1}),
    coord="time",
    freq=[1 / 15, 1 / 50],
    cycles_per="D",
    num_discard=0,
).plot()

(array([  27751.,   63152.,   90067.,  218473.,  704834., 1188229.,
         783223.,  270191.,  102573.,   11347.]),
 array([-0.19458834, -0.15993737, -0.1252864 , -0.09063542, -0.05598445,
        -0.02133348,  0.01331749,  0.04796846,  0.08261943,  0.11727041,
         0.15192138]),
 <a list of 10 Patch objects>)

station.theta.reindex_like(kpp[["time"]]).isel(
    depth=0, time=slice(None, None, 24)
).plot(x="time", robust=True)
(2 * v2 / v2.max()).plot.line(x="time")

[<matplotlib.lines.Line2D at 0x2b0a355ff0f0>]

v2.plot.line(hue="latitude")

[<matplotlib.lines.Line2D at 0x2b0a34eb54e0>]

dh = kpp.hunlimit - kpp.hmonob
dh = dh.where(dh > 0, drop=True)
dh = dh.to_dataset(name="dh").merge(v2)
dh.dh.plot.hist(bins=100)
dh["month"] = dh.time.dt.month
dh = dh.assign_coords(latitude=[0, 3.5])
dh.dh.attrs["long_name"] = "$H_{0.3} - L_{MO}$"

import seaborn as sns

df = dh.to_dataframe()

sns.catplot(data=df.reset_index(), x="month", y="dh", kind="box", hue="latitude")

<seaborn.axisgrid.FacetGrid at 0x2b0a49b521d0>

dh.sortby("latitude", ascending=False).plot.scatter(
    row="latitude", x="month", y="dh", s=3
)

<xarray.plot.facetgrid.FacetGrid at 0x2b0a48768240>

kpp.hunlimit.plot.hist()
kpp.hbl.plot.hist()

(array([3095., 1072., 1050.,  935.,  878.,  586.,  537.,  315.,  187.,
          57.]),
 array([ 1.25 ,  8.125, 15.   , 21.875, 28.75 , 35.625, 42.5  , 49.375,
        56.25 , 63.125, 70.   ], dtype=float32),
 <a list of 10 Patch objects>)

Debugging: Double check with augmented output

exf = xr.open_mfdataset(
    "/glade/scratch/bachman/TPOS_MITgcm_20_year/HOLD/*sfc.nc", parallel=True
)

/glade/u/home/dcherian/miniconda3/envs/dcpy/lib/python3.6/site-packages/ipykernel_launcher.py:2: FutureWarning: In xarray version 0.15 the default behaviour of `open_mfdataset`
will change. To retain the existing behavior, pass
combine='nested'. To use future default behavior, pass
combine='by_coords'. See
http://xarray.pydata.org/en/stable/combining.html#combining-multi

  
/glade/u/home/dcherian/miniconda3/envs/dcpy/lib/python3.6/site-packages/xarray/backends/api.py:941: FutureWarning: The datasets supplied have global dimension coordinates. You may want
to use the new `combine_by_coords` function (or the
`combine='by_coords'` option to `open_mfdataset`) to order the datasets
before concatenation. Alternatively, to continue concatenating based
on the order the datasets are supplied in future, please use the new
`combine_nested` function (or the `combine='nested'` option to
open_mfdataset).The datasets supplied require both concatenation and merging. From
xarray version 0.15 this will operation will require either using the
new `combine_nested` function (or the `combine='nested'` option to
open_mfdataset), with a nested list structure such that you can combine
along the dimensions None. Alternatively if your datasets have global
dimension coordinates then you can use the new `combine_by_coords`
function.
  from_openmfds=True,

kpp2 = xr.open_mfdataset(
    "/glade/scratch/bachman/TPOS_MITgcm_20_year/HOLD/*KPP2D.nc",
    chunks=500,
    parallel=True,
    concat_dim="time",
)
kpp3 = xr.open_mfdataset(
    "/glade/scratch/bachman/TPOS_MITgcm_20_year/HOLD/*KPP3D.nc",
    chunks=500,
    parallel=True,
    concat_dim="time",
)

/glade/u/home/dcherian/miniconda3/envs/dcpy/lib/python3.6/site-packages/ipykernel_launcher.py:5: FutureWarning: In xarray version 0.15 the default behaviour of `open_mfdataset`
will change. To retain the existing behavior, pass
combine='nested'. To use future default behavior, pass
combine='by_coords'. See
http://xarray.pydata.org/en/stable/combining.html#combining-multi

  """
/glade/u/home/dcherian/miniconda3/envs/dcpy/lib/python3.6/site-packages/xarray/backends/api.py:941: FutureWarning: Also `open_mfdataset` will no longer accept a `concat_dim` argument.
To get equivalent behaviour from now on please use the new
`combine_nested` function instead (or the `combine='nested'` option to
`open_mfdataset`).The datasets supplied have global dimension coordinates. You may want
to use the new `combine_by_coords` function (or the
`combine='by_coords'` option to `open_mfdataset`) to order the datasets
before concatenation. Alternatively, to continue concatenating based
on the order the datasets are supplied in future, please use the new
`combine_nested` function (or the `combine='nested'` option to
open_mfdataset).The datasets supplied require both concatenation and merging. From
xarray version 0.15 this will operation will require either using the
new `combine_nested` function (or the `combine='nested'` option to
open_mfdataset), with a nested list structure such that you can combine
along the dimensions None. Alternatively if your datasets have global
dimension coordinates then you can use the new `combine_by_coords`
function.
  from_openmfds=True,
/glade/u/home/dcherian/miniconda3/envs/dcpy/lib/python3.6/site-packages/ipykernel_launcher.py:11: FutureWarning: In xarray version 0.15 the default behaviour of `open_mfdataset`
will change. To retain the existing behavior, pass
combine='nested'. To use future default behavior, pass
combine='by_coords'. See
http://xarray.pydata.org/en/stable/combining.html#combining-multi

  # This is added back by InteractiveShellApp.init_path()
/glade/u/home/dcherian/miniconda3/envs/dcpy/lib/python3.6/site-packages/xarray/backends/api.py:941: FutureWarning: Also `open_mfdataset` will no longer accept a `concat_dim` argument.
To get equivalent behaviour from now on please use the new
`combine_nested` function instead (or the `combine='nested'` option to
`open_mfdataset`).The datasets supplied have global dimension coordinates. You may want
to use the new `combine_by_coords` function (or the
`combine='by_coords'` option to `open_mfdataset`) to order the datasets
before concatenation. Alternatively, to continue concatenating based
on the order the datasets are supplied in future, please use the new
`combine_nested` function (or the `combine='nested'` option to
open_mfdataset).The datasets supplied require both concatenation and merging. From
xarray version 0.15 this will operation will require either using the
new `combine_nested` function (or the `combine='nested'` option to
open_mfdataset), with a nested list structure such that you can combine
along the dimensions None. Alternatively if your datasets have global
dimension coordinates then you can use the new `combine_by_coords`
function.
  from_openmfds=True,

eq = (
    station.sel(latitude=0, method="nearest")
    .sel(depth=slice(-150))
    .pipe(pump.calc_reduced_shear)[
        [
            "u",
            "v",
            "theta",
            "salt",
            "dens",
            "tau",
            "usr",
            "uas",
            "vas",
            "wind_angle",
            "netflux",
            "short",
            "KPPhbl",
            "taux",
            "tauy",
        ]
    ]
)

calc uz
calc vz
calc S2
calc N2
calc shred2
Calc Ri

aug = xr.merge([kpp2, kpp3])
aug["time"] = aug.time + pd.Timedelta("23h")
aug
kppeq = aug.sel(longitude=eq.longitude, latitude=3.5, method="nearest")  # .load()
kppeq

xarray.Dataset

• Dimensions:
  • depth: 136
  • time: 169
• Coordinates: (4)
  • time
    (time)
    datetime64[ns]
    1999-09-08 ... 1999-09-15

    array(['1999-09-08T00:00:00.000000000', '1999-09-08T01:00:00.000000000',
           '1999-09-08T02:00:00.000000000', '1999-09-08T03:00:00.000000000',
           '1999-09-08T04:00:00.000000000', '1999-09-08T05:00:00.000000000',
           '1999-09-08T06:00:00.000000000', '1999-09-08T07:00:00.000000000',
           '1999-09-08T08:00:00.000000000', '1999-09-08T09:00:00.000000000',
           '1999-09-08T10:00:00.000000000', '1999-09-08T11:00:00.000000000',
           '1999-09-08T12:00:00.000000000', '1999-09-08T13:00:00.000000000',
           '1999-09-08T14:00:00.000000000', '1999-09-08T15:00:00.000000000',
           '1999-09-08T16:00:00.000000000', '1999-09-08T17:00:00.000000000',
           '1999-09-08T18:00:00.000000000', '1999-09-08T19:00:00.000000000',
           '1999-09-08T20:00:00.000000000', '1999-09-08T21:00:00.000000000',
           '1999-09-08T22:00:00.000000000', '1999-09-08T23:00:00.000000000',
           '1999-09-09T00:00:00.000000000', '1999-09-09T01:00:00.000000000',
           '1999-09-09T02:00:00.000000000', '1999-09-09T03:00:00.000000000',
           '1999-09-09T04:00:00.000000000', '1999-09-09T05:00:00.000000000',
           '1999-09-09T06:00:00.000000000', '1999-09-09T07:00:00.000000000',
           '1999-09-09T08:00:00.000000000', '1999-09-09T09:00:00.000000000',
           '1999-09-09T10:00:00.000000000', '1999-09-09T11:00:00.000000000',
           '1999-09-09T12:00:00.000000000', '1999-09-09T13:00:00.000000000',
           '1999-09-09T14:00:00.000000000', '1999-09-09T15:00:00.000000000',
           '1999-09-09T16:00:00.000000000', '1999-09-09T17:00:00.000000000',
           '1999-09-09T18:00:00.000000000', '1999-09-09T19:00:00.000000000',
           '1999-09-09T20:00:00.000000000', '1999-09-09T21:00:00.000000000',
           '1999-09-09T22:00:00.000000000', '1999-09-09T23:00:00.000000000',
           '1999-09-10T00:00:00.000000000', '1999-09-10T01:00:00.000000000',
           '1999-09-10T02:00:00.000000000', '1999-09-10T03:00:00.000000000',
           '1999-09-10T04:00:00.000000000', '1999-09-10T05:00:00.000000000',
           '1999-09-10T06:00:00.000000000', '1999-09-10T07:00:00.000000000',
           '1999-09-10T08:00:00.000000000', '1999-09-10T09:00:00.000000000',
           '1999-09-10T10:00:00.000000000', '1999-09-10T11:00:00.000000000',
           '1999-09-10T12:00:00.000000000', '1999-09-10T13:00:00.000000000',
           '1999-09-10T14:00:00.000000000', '1999-09-10T15:00:00.000000000',
           '1999-09-10T16:00:00.000000000', '1999-09-10T17:00:00.000000000',
           '1999-09-10T18:00:00.000000000', '1999-09-10T19:00:00.000000000',
           '1999-09-10T20:00:00.000000000', '1999-09-10T21:00:00.000000000',
           '1999-09-10T22:00:00.000000000', '1999-09-10T23:00:00.000000000',
           '1999-09-11T00:00:00.000000000', '1999-09-11T01:00:00.000000000',
           '1999-09-11T02:00:00.000000000', '1999-09-11T03:00:00.000000000',
           '1999-09-11T04:00:00.000000000', '1999-09-11T05:00:00.000000000',
           '1999-09-11T06:00:00.000000000', '1999-09-11T07:00:00.000000000',
           '1999-09-11T08:00:00.000000000', '1999-09-11T09:00:00.000000000',
           '1999-09-11T10:00:00.000000000', '1999-09-11T11:00:00.000000000',
           '1999-09-11T12:00:00.000000000', '1999-09-11T13:00:00.000000000',
           '1999-09-11T14:00:00.000000000', '1999-09-11T15:00:00.000000000',
           '1999-09-11T16:00:00.000000000', '1999-09-11T17:00:00.000000000',
           '1999-09-11T18:00:00.000000000', '1999-09-11T19:00:00.000000000',
           '1999-09-11T20:00:00.000000000', '1999-09-11T21:00:00.000000000',
           '1999-09-11T22:00:00.000000000', '1999-09-11T23:00:00.000000000',
           '1999-09-12T00:00:00.000000000', '1999-09-12T01:00:00.000000000',
           '1999-09-12T02:00:00.000000000', '1999-09-12T03:00:00.000000000',
           '1999-09-12T04:00:00.000000000', '1999-09-12T05:00:00.000000000',
           '1999-09-12T06:00:00.000000000', '1999-09-12T07:00:00.000000000',
           '1999-09-12T08:00:00.000000000', '1999-09-12T09:00:00.000000000',
           '1999-09-12T10:00:00.000000000', '1999-09-12T11:00:00.000000000',
           '1999-09-12T12:00:00.000000000', '1999-09-12T13:00:00.000000000',
           '1999-09-12T14:00:00.000000000', '1999-09-12T15:00:00.000000000',
           '1999-09-12T16:00:00.000000000', '1999-09-12T17:00:00.000000000',
           '1999-09-12T18:00:00.000000000', '1999-09-12T19:00:00.000000000',
           '1999-09-12T20:00:00.000000000', '1999-09-12T21:00:00.000000000',
           '1999-09-12T22:00:00.000000000', '1999-09-12T23:00:00.000000000',
           '1999-09-13T00:00:00.000000000', '1999-09-13T01:00:00.000000000',
           '1999-09-13T02:00:00.000000000', '1999-09-13T03:00:00.000000000',
           '1999-09-13T04:00:00.000000000', '1999-09-13T05:00:00.000000000',
           '1999-09-13T06:00:00.000000000', '1999-09-13T07:00:00.000000000',
           '1999-09-13T08:00:00.000000000', '1999-09-13T09:00:00.000000000',
           '1999-09-13T10:00:00.000000000', '1999-09-13T11:00:00.000000000',
           '1999-09-13T12:00:00.000000000', '1999-09-13T13:00:00.000000000',
           '1999-09-13T14:00:00.000000000', '1999-09-13T15:00:00.000000000',
           '1999-09-13T16:00:00.000000000', '1999-09-13T17:00:00.000000000',
           '1999-09-13T18:00:00.000000000', '1999-09-13T19:00:00.000000000',
           '1999-09-13T20:00:00.000000000', '1999-09-13T21:00:00.000000000',
           '1999-09-13T22:00:00.000000000', '1999-09-13T23:00:00.000000000',
           '1999-09-14T00:00:00.000000000', '1999-09-14T01:00:00.000000000',
           '1999-09-14T02:00:00.000000000', '1999-09-14T03:00:00.000000000',
           '1999-09-14T04:00:00.000000000', '1999-09-14T05:00:00.000000000',
           '1999-09-14T06:00:00.000000000', '1999-09-14T07:00:00.000000000',
           '1999-09-14T08:00:00.000000000', '1999-09-14T09:00:00.000000000',
           '1999-09-14T10:00:00.000000000', '1999-09-14T11:00:00.000000000',
           '1999-09-14T12:00:00.000000000', '1999-09-14T13:00:00.000000000',
           '1999-09-14T14:00:00.000000000', '1999-09-14T15:00:00.000000000',
           '1999-09-14T16:00:00.000000000', '1999-09-14T17:00:00.000000000',
           '1999-09-14T18:00:00.000000000', '1999-09-14T19:00:00.000000000',
           '1999-09-14T20:00:00.000000000', '1999-09-14T21:00:00.000000000',
           '1999-09-14T22:00:00.000000000', '1999-09-14T23:00:00.000000000',
           '1999-09-15T00:00:00.000000000'], dtype='datetime64[ns]')

  • longitude
    ()
    float32
    -110.00916

    array(-110.00916, dtype=float32)

  • latitude
    ()
    float32
    3.4837093

    array(3.4837093, dtype=float32)

  • depth
    (depth)
    float32
    -1.25 -3.75 ... -881.6718 -944.4181

    array([  -1.25   ,   -3.75   ,   -6.25   ,   -8.75   ,  -11.25   ,  -13.75   ,
            -16.25   ,  -18.75   ,  -21.25   ,  -23.75   ,  -26.25   ,  -28.75   ,
            -31.25   ,  -33.75   ,  -36.25   ,  -38.75   ,  -41.25   ,  -43.75   ,
            -46.25   ,  -48.75   ,  -51.25   ,  -53.75   ,  -56.25   ,  -58.75   ,
            -61.25   ,  -63.75   ,  -66.25   ,  -68.75   ,  -71.25   ,  -73.75   ,
            -76.25   ,  -78.75   ,  -81.25   ,  -83.75   ,  -86.25   ,  -88.75   ,
            -91.25   ,  -93.75   ,  -96.25   ,  -98.75   , -101.25   , -103.75   ,
           -106.25   , -108.75   , -111.25   , -113.75   , -116.25   , -118.75   ,
           -121.25   , -123.75   , -126.25   , -128.75   , -131.25   , -133.75   ,
           -136.25   , -138.75   , -141.25   , -143.75   , -146.25   , -148.75   ,
           -151.25   , -153.75   , -156.25   , -158.75   , -161.25   , -163.75   ,
           -166.25   , -168.75   , -171.25   , -173.75   , -176.25   , -178.75   ,
           -181.25   , -183.75   , -186.25   , -188.75   , -191.25   , -193.75   ,
           -196.25   , -198.75   , -201.25   , -203.75   , -206.25   , -208.75   ,
           -211.25   , -213.75   , -216.25   , -218.75   , -221.25   , -223.75   ,
           -226.25   , -228.75   , -231.25   , -233.75   , -236.25   , -238.75   ,
           -241.25   , -243.75   , -246.25   , -248.75   , -251.36874, -254.2363 ,
           -257.37625, -260.8145 , -264.5794 , -268.70193, -273.21616, -278.1592 ,
           -283.57184, -289.4987 , -295.98856, -303.0949 , -310.8764 , -319.39716,
           -328.72736, -338.94394, -350.13116, -362.3811 , -375.79474, -390.4827 ,
           -406.56604, -424.17734, -443.4617 , -464.57806, -487.70044, -513.0195 ,
           -540.7438 , -571.1019 , -604.3441 , -640.7443 , -680.6025 , -724.2472 ,
           -772.03815, -824.36926, -881.6718 , -944.4181 ], dtype=float32)

• Data variables: (10)
  • KPPhbl
    (time)
    float32
    dask.array<chunksize=(1,), meta=np.ndarray>

    
    
    
    
      
    
        
     
     Array 
     Chunk 
      
      
    
        
     Bytes 
     676 B 
     
     8 B 
        
     Shape 
     (169,) 
     
     (2,) 
        
     Count 
     3874 Tasks 
     168 Chunks 
        
     Type 
     float32 
     numpy.ndarray 
      
    
    
    
    
    
    
    

  • KPPfrac
    (time)
    float32
    dask.array<chunksize=(1,), meta=np.ndarray>

    
    
    
    
      
    
        
     
     Array 
     Chunk 
      
      
    
        
     Bytes 
     676 B 
     
     8 B 
        
     Shape 
     (169,) 
     
     (2,) 
        
     Count 
     3874 Tasks 
     168 Chunks 
        
     Type 
     float32 
     numpy.ndarray 
      
    
    
    
    
    
    
    

  • KPPbo
    (time)
    float32
    dask.array<chunksize=(1,), meta=np.ndarray>

    
    
    
    
      
    
        
     
     Array 
     Chunk 
      
      
    
        
     Bytes 
     676 B 
     
     8 B 
        
     Shape 
     (169,) 
     
     (2,) 
        
     Count 
     3874 Tasks 
     168 Chunks 
        
     Type 
     float32 
     numpy.ndarray 
      
    
    
    
    
    
    
    

  • KPPbosol
    (time)
    float32
    dask.array<chunksize=(1,), meta=np.ndarray>

    
    
    
    
      
    
        
     
     Array 
     Chunk 
      
      
    
        
     Bytes 
     676 B 
     
     8 B 
        
     Shape 
     (169,) 
     
     (2,) 
        
     Count 
     3874 Tasks 
     168 Chunks 
        
     Type 
     float32 
     numpy.ndarray 
      
    
    
    
    
    
    
    

  • KPPviscA
    (time, depth)
    float32
    dask.array<chunksize=(1, 136), meta=np.ndarray>

    
    
    
    
      
    
        
     
     Array 
     Chunk 
      
      
    
        
     Bytes 
     91.94 kB 
     
     1.09 kB 
        
     Shape 
     (169, 136) 
     
     (2, 136) 
        
     Count 
     4044 Tasks 
     168 Chunks 
        
     Type 
     float32 
     numpy.ndarray 
      
    
    
    
    
    
    
    

  • KPPdiffT
    (time, depth)
    float32
    dask.array<chunksize=(1, 136), meta=np.ndarray>

    
    
    
    
      
    
        
     
     Array 
     Chunk 
      
      
    
        
     Bytes 
     91.94 kB 
     
     1.09 kB 
        
     Shape 
     (169, 136) 
     
     (2, 136) 
        
     Count 
     4044 Tasks 
     168 Chunks 
        
     Type 
     float32 
     numpy.ndarray 
      
    
    
    
    
    
    
    

  • KPPdbsfc
    (time, depth)
    float32
    dask.array<chunksize=(1, 136), meta=np.ndarray>

    
    
    
    
      
    
        
     
     Array 
     Chunk 
      
      
    
        
     Bytes 
     91.94 kB 
     
     1.09 kB 
        
     Shape 
     (169, 136) 
     
     (2, 136) 
        
     Count 
     4044 Tasks 
     168 Chunks 
        
     Type 
     float32 
     numpy.ndarray 
      
    
    
    
    
    
    
    

  • KPPbfsfc
    (time, depth)
    float32
    dask.array<chunksize=(1, 136), meta=np.ndarray>

    
    
    
    
      
    
        
     
     Array 
     Chunk 
      
      
    
        
     Bytes 
     91.94 kB 
     
     1.09 kB 
        
     Shape 
     (169, 136) 
     
     (2, 136) 
        
     Count 
     4044 Tasks 
     168 Chunks 
        
     Type 
     float32 
     numpy.ndarray 
      
    
    
    
    
    
    
    

  • KPPRi
    (time, depth)
    float32
    dask.array<chunksize=(1, 136), meta=np.ndarray>

    
    
    
    
      
    
        
     
     Array 
     Chunk 
      
      
    
        
     Bytes 
     91.94 kB 
     
     1.09 kB 
        
     Shape 
     (169, 136) 
     
     (2, 136) 
        
     Count 
     4044 Tasks 
     168 Chunks 
        
     Type 
     float32 
     numpy.ndarray 
      
    
    
    
    
    
    
    

  • KPPdbloc
    (time, depth)
    float32
    dask.array<chunksize=(1, 136), meta=np.ndarray>

    
    
    
    
      
    
        
     
     Array 
     Chunk 
      
      
    
        
     Bytes 
     91.94 kB 
     
     1.09 kB 
        
     Shape 
     (169, 136) 
     
     (2, 136) 
        
     Count 
     4044 Tasks 
     168 Chunks 
        
     Type 
     float32 
     numpy.ndarray 
      
    
    
    
    
    
    
    

• Attributes: (0)

kppeq.KPPhbl.load()
kppeq.KPPRi.load()

xarray.DataArray

'KPPRi'

• time: 169
• depth: 136

• nan -0.072856516 -0.049158316 -0.010265699 ... nan nan nan nan

  array([[            nan, -7.28565156e-02, -4.91583161e-02, ...,
           4.15663185e+01,  4.27496376e+01,  4.35260162e+01],
         [            nan, -9.19454768e-02, -7.40848482e-02, ...,
           4.33817825e+01,  4.47163200e+01,  4.55936394e+01],
         [            nan, -1.04447804e-01, -9.60322767e-02, ...,
           4.47098045e+01,  4.61663933e+01,  4.71218681e+01],
         ...,
         [            nan, -3.40390988e-02, -3.26437652e-02, ...,
           1.78085266e+02,  2.02577866e+02,  2.19549545e+02],
         [            nan, -6.18767738e-02, -7.47275874e-02, ...,
           5.50265236e+01,  5.69808998e+01,  5.80998001e+01],
         [            nan,             nan,             nan, ...,
                      nan,             nan,             nan]], dtype=float32)

• Coordinates: (4)
  • time
    (time)
    datetime64[ns]
    1999-09-08 ... 1999-09-15

    array(['1999-09-08T00:00:00.000000000', '1999-09-08T01:00:00.000000000',
           '1999-09-08T02:00:00.000000000', '1999-09-08T03:00:00.000000000',
           '1999-09-08T04:00:00.000000000', '1999-09-08T05:00:00.000000000',
           '1999-09-08T06:00:00.000000000', '1999-09-08T07:00:00.000000000',
           '1999-09-08T08:00:00.000000000', '1999-09-08T09:00:00.000000000',
           '1999-09-08T10:00:00.000000000', '1999-09-08T11:00:00.000000000',
           '1999-09-08T12:00:00.000000000', '1999-09-08T13:00:00.000000000',
           '1999-09-08T14:00:00.000000000', '1999-09-08T15:00:00.000000000',
           '1999-09-08T16:00:00.000000000', '1999-09-08T17:00:00.000000000',
           '1999-09-08T18:00:00.000000000', '1999-09-08T19:00:00.000000000',
           '1999-09-08T20:00:00.000000000', '1999-09-08T21:00:00.000000000',
           '1999-09-08T22:00:00.000000000', '1999-09-08T23:00:00.000000000',
           '1999-09-09T00:00:00.000000000', '1999-09-09T01:00:00.000000000',
           '1999-09-09T02:00:00.000000000', '1999-09-09T03:00:00.000000000',
           '1999-09-09T04:00:00.000000000', '1999-09-09T05:00:00.000000000',
           '1999-09-09T06:00:00.000000000', '1999-09-09T07:00:00.000000000',
           '1999-09-09T08:00:00.000000000', '1999-09-09T09:00:00.000000000',
           '1999-09-09T10:00:00.000000000', '1999-09-09T11:00:00.000000000',
           '1999-09-09T12:00:00.000000000', '1999-09-09T13:00:00.000000000',
           '1999-09-09T14:00:00.000000000', '1999-09-09T15:00:00.000000000',
           '1999-09-09T16:00:00.000000000', '1999-09-09T17:00:00.000000000',
           '1999-09-09T18:00:00.000000000', '1999-09-09T19:00:00.000000000',
           '1999-09-09T20:00:00.000000000', '1999-09-09T21:00:00.000000000',
           '1999-09-09T22:00:00.000000000', '1999-09-09T23:00:00.000000000',
           '1999-09-10T00:00:00.000000000', '1999-09-10T01:00:00.000000000',
           '1999-09-10T02:00:00.000000000', '1999-09-10T03:00:00.000000000',
           '1999-09-10T04:00:00.000000000', '1999-09-10T05:00:00.000000000',
           '1999-09-10T06:00:00.000000000', '1999-09-10T07:00:00.000000000',
           '1999-09-10T08:00:00.000000000', '1999-09-10T09:00:00.000000000',
           '1999-09-10T10:00:00.000000000', '1999-09-10T11:00:00.000000000',
           '1999-09-10T12:00:00.000000000', '1999-09-10T13:00:00.000000000',
           '1999-09-10T14:00:00.000000000', '1999-09-10T15:00:00.000000000',
           '1999-09-10T16:00:00.000000000', '1999-09-10T17:00:00.000000000',
           '1999-09-10T18:00:00.000000000', '1999-09-10T19:00:00.000000000',
           '1999-09-10T20:00:00.000000000', '1999-09-10T21:00:00.000000000',
           '1999-09-10T22:00:00.000000000', '1999-09-10T23:00:00.000000000',
           '1999-09-11T00:00:00.000000000', '1999-09-11T01:00:00.000000000',
           '1999-09-11T02:00:00.000000000', '1999-09-11T03:00:00.000000000',
           '1999-09-11T04:00:00.000000000', '1999-09-11T05:00:00.000000000',
           '1999-09-11T06:00:00.000000000', '1999-09-11T07:00:00.000000000',
           '1999-09-11T08:00:00.000000000', '1999-09-11T09:00:00.000000000',
           '1999-09-11T10:00:00.000000000', '1999-09-11T11:00:00.000000000',
           '1999-09-11T12:00:00.000000000', '1999-09-11T13:00:00.000000000',
           '1999-09-11T14:00:00.000000000', '1999-09-11T15:00:00.000000000',
           '1999-09-11T16:00:00.000000000', '1999-09-11T17:00:00.000000000',
           '1999-09-11T18:00:00.000000000', '1999-09-11T19:00:00.000000000',
           '1999-09-11T20:00:00.000000000', '1999-09-11T21:00:00.000000000',
           '1999-09-11T22:00:00.000000000', '1999-09-11T23:00:00.000000000',
           '1999-09-12T00:00:00.000000000', '1999-09-12T01:00:00.000000000',
           '1999-09-12T02:00:00.000000000', '1999-09-12T03:00:00.000000000',
           '1999-09-12T04:00:00.000000000', '1999-09-12T05:00:00.000000000',
           '1999-09-12T06:00:00.000000000', '1999-09-12T07:00:00.000000000',
           '1999-09-12T08:00:00.000000000', '1999-09-12T09:00:00.000000000',
           '1999-09-12T10:00:00.000000000', '1999-09-12T11:00:00.000000000',
           '1999-09-12T12:00:00.000000000', '1999-09-12T13:00:00.000000000',
           '1999-09-12T14:00:00.000000000', '1999-09-12T15:00:00.000000000',
           '1999-09-12T16:00:00.000000000', '1999-09-12T17:00:00.000000000',
           '1999-09-12T18:00:00.000000000', '1999-09-12T19:00:00.000000000',
           '1999-09-12T20:00:00.000000000', '1999-09-12T21:00:00.000000000',
           '1999-09-12T22:00:00.000000000', '1999-09-12T23:00:00.000000000',
           '1999-09-13T00:00:00.000000000', '1999-09-13T01:00:00.000000000',
           '1999-09-13T02:00:00.000000000', '1999-09-13T03:00:00.000000000',
           '1999-09-13T04:00:00.000000000', '1999-09-13T05:00:00.000000000',
           '1999-09-13T06:00:00.000000000', '1999-09-13T07:00:00.000000000',
           '1999-09-13T08:00:00.000000000', '1999-09-13T09:00:00.000000000',
           '1999-09-13T10:00:00.000000000', '1999-09-13T11:00:00.000000000',
           '1999-09-13T12:00:00.000000000', '1999-09-13T13:00:00.000000000',
           '1999-09-13T14:00:00.000000000', '1999-09-13T15:00:00.000000000',
           '1999-09-13T16:00:00.000000000', '1999-09-13T17:00:00.000000000',
           '1999-09-13T18:00:00.000000000', '1999-09-13T19:00:00.000000000',
           '1999-09-13T20:00:00.000000000', '1999-09-13T21:00:00.000000000',
           '1999-09-13T22:00:00.000000000', '1999-09-13T23:00:00.000000000',
           '1999-09-14T00:00:00.000000000', '1999-09-14T01:00:00.000000000',
           '1999-09-14T02:00:00.000000000', '1999-09-14T03:00:00.000000000',
           '1999-09-14T04:00:00.000000000', '1999-09-14T05:00:00.000000000',
           '1999-09-14T06:00:00.000000000', '1999-09-14T07:00:00.000000000',
           '1999-09-14T08:00:00.000000000', '1999-09-14T09:00:00.000000000',
           '1999-09-14T10:00:00.000000000', '1999-09-14T11:00:00.000000000',
           '1999-09-14T12:00:00.000000000', '1999-09-14T13:00:00.000000000',
           '1999-09-14T14:00:00.000000000', '1999-09-14T15:00:00.000000000',
           '1999-09-14T16:00:00.000000000', '1999-09-14T17:00:00.000000000',
           '1999-09-14T18:00:00.000000000', '1999-09-14T19:00:00.000000000',
           '1999-09-14T20:00:00.000000000', '1999-09-14T21:00:00.000000000',
           '1999-09-14T22:00:00.000000000', '1999-09-14T23:00:00.000000000',
           '1999-09-15T00:00:00.000000000'], dtype='datetime64[ns]')

  • longitude
    ()
    float32
    -110.00916

    array(-110.00916, dtype=float32)

  • latitude
    ()
    float32
    3.4837093

    array(3.4837093, dtype=float32)

  • depth
    (depth)
    float32
    -1.25 -3.75 ... -881.6718 -944.4181

    array([  -1.25   ,   -3.75   ,   -6.25   ,   -8.75   ,  -11.25   ,  -13.75   ,
            -16.25   ,  -18.75   ,  -21.25   ,  -23.75   ,  -26.25   ,  -28.75   ,
            -31.25   ,  -33.75   ,  -36.25   ,  -38.75   ,  -41.25   ,  -43.75   ,
            -46.25   ,  -48.75   ,  -51.25   ,  -53.75   ,  -56.25   ,  -58.75   ,
            -61.25   ,  -63.75   ,  -66.25   ,  -68.75   ,  -71.25   ,  -73.75   ,
            -76.25   ,  -78.75   ,  -81.25   ,  -83.75   ,  -86.25   ,  -88.75   ,
            -91.25   ,  -93.75   ,  -96.25   ,  -98.75   , -101.25   , -103.75   ,
           -106.25   , -108.75   , -111.25   , -113.75   , -116.25   , -118.75   ,
           -121.25   , -123.75   , -126.25   , -128.75   , -131.25   , -133.75   ,
           -136.25   , -138.75   , -141.25   , -143.75   , -146.25   , -148.75   ,
           -151.25   , -153.75   , -156.25   , -158.75   , -161.25   , -163.75   ,
           -166.25   , -168.75   , -171.25   , -173.75   , -176.25   , -178.75   ,
           -181.25   , -183.75   , -186.25   , -188.75   , -191.25   , -193.75   ,
           -196.25   , -198.75   , -201.25   , -203.75   , -206.25   , -208.75   ,
           -211.25   , -213.75   , -216.25   , -218.75   , -221.25   , -223.75   ,
           -226.25   , -228.75   , -231.25   , -233.75   , -236.25   , -238.75   ,
           -241.25   , -243.75   , -246.25   , -248.75   , -251.36874, -254.2363 ,
           -257.37625, -260.8145 , -264.5794 , -268.70193, -273.21616, -278.1592 ,
           -283.57184, -289.4987 , -295.98856, -303.0949 , -310.8764 , -319.39716,
           -328.72736, -338.94394, -350.13116, -362.3811 , -375.79474, -390.4827 ,
           -406.56604, -424.17734, -443.4617 , -464.57806, -487.70044, -513.0195 ,
           -540.7438 , -571.1019 , -604.3441 , -640.7443 , -680.6025 , -724.2472 ,
           -772.03815, -824.36926, -881.6718 , -944.4181 ], dtype=float32)

• Attributes: (0)

kppeq.KPPRi.sel(depth=slice(-150)).plot(y="depth", vmin=0.1, center=0.3)
(-1 * kppeq.KPPhbl).plot()

[<matplotlib.lines.Line2D at 0x2ad5a69346a0>]

eq = eq.sel(time=kppeq.time.values)

itime = 13

subset = eq.isel(time=slice(1, None)).isel(time=itime).sel(depth=slice(-50)).compute()

Rib = KPP.kpp(
    subset.u.values,
    subset.v.values,
    subset.theta.values,
    subset.salt.values,
    subset.depth.values,
    np.arange(0, subset.depth[-1] - 2.5, -2.5),
    subset.taux.values / 1025,
    subset.tauy.values / 1025,
    -(subset.netflux - subset.short).values / 1035 / 3999,
    xr.zeros_like(subset.netflux).values,
    -subset.short.values / 1035 / 3999,
    COR=0,
    hbl=12,
    debug=True,
    r1=0.62,
    amu1=0.6,
    r2=1 - 0.62,
    amu2=20,
    sigma=subset.dens.values - 1000,
    rho0=1035,
)

plt.figure()
plt.plot(Rib, subset.depth)
kppeq.KPPRi.isel(time=itime).sel(depth=slice(-50)).plot(y="depth")

using given sigma
Net sfc buoyancy flux (without shortwave) : 8.00e-08 m²/s³, Shortwave buoyancy flux: -1.57e-07 m²/s³
kbl=5, ksl=0


 surface layer: T=22.981, S=34.409, σ=23.491, Z=-1.250, U=-0.862, V=-0.200
dV2 = 0.8859959840774536, dVt2 = 0.001069049035243624
hbl_top = 3.75, hbl_bottom = 1.25
ustar = 3.391e-03 m/s
--- hekman: 237385246872857568.00, monin-obukhov: 4.22
--- hbl before limiting: 2.50
--- hbl after limiting: 2.50
kbl=2, ksl=0


 surface layer: T=22.981, S=34.409, σ=23.491, Z=-1.250, U=-0.862, V=-0.200
dV2 = 0.8859959840774536, dVt2 = 0.0015779611668915864
hbl_top = 3.75, hbl_bottom = 1.25
ustar = 3.391e-03 m/s
--- hekman: 237385246872857568.00, monin-obukhov: 4.22
--- hbl before limiting: 2.50
--- hbl after limiting: 2.50
kbl=2, ksl=0


 surface layer: T=22.981, S=34.409, σ=23.491, Z=-1.250, U=-0.862, V=-0.200
dV2 = 0.8859959840774536, dVt2 = 0.0020708721121411094
hbl_top = 3.75, hbl_bottom = 1.25
ustar = 3.391e-03 m/s
--- hekman: 237385246872857568.00, monin-obukhov: 4.22
--- hbl before limiting: 2.50
--- hbl after limiting: 2.50

[<matplotlib.lines.Line2D at 0x2aba11f6f4a8>]

subset = eq.compute()  # WTF

hbl = xr.apply_ufunc(
    KPP.kpp,
    subset.u,
    subset.v,
    subset.theta,
    subset.salt,
    subset.depth,
    np.arange(0, subset.depth[-1] - 2.5, -2.5),
    subset.taux / 1035,
    subset.tauy / 1035,
    -(subset.netflux - subset.short) / 1035 / 3999,
    xr.zeros_like(subset.netflux),
    -subset.short / 1035 / 3999,
    kwargs=dict(COR=0, hbl=12, debug=False, r1=0.62, amu1=0.6, r2=1 - 0.62, amu2=20),
    vectorize=True,
    input_core_dims=[["depth"]] * 5
    + [["depth2"]]
    + [
        [],
    ]
    * 5,
    # output_core_dims=[["time"]]
)

Todo

1. Compare diffusivities before TIW and after TIW

2. Compare shear before and after TIW

3. Does the change in sign of velocity matter at the equator? (usl -u(h)). Is u the same sign in the hbl at the equator too?