AlongTrack Data - SWOT

Contents

AlongTrack Data - SWOT#

In this notebook, we will look at how one can do some simple regridding processes with the AlongTrack SWOT data available from the 2020a OSSE Data Challenge.

import autoroot
import typing as tp
from dataclasses import dataclass
import functools as ft
import numpy as np
import pandas as pd
import xarray as xr
import einops
from metpy.units import units
import pint_xarray
import xarray_dataclasses as xrdataclass
import matplotlib.pyplot as plt
import matplotlib.colors as colors
import matplotlib.ticker as ticker
import seaborn as sns

sns.reset_defaults()
sns.set_context(context="talk", font_scale=0.7)

%load_ext autoreload
%autoreload 2

The autoreload extension is already loaded. To reload it, use:
  %reload_ext autoreload

Data Structure#

from oceanbench._src.geoprocessing.gridding import coord_based_to_grid

%%time

ds_nadir_gridded = coord_based_to_grid(
    ds_nadir, 
    ds_natl60,
    data_vars=["ssh"], 
    t_res=pd.to_timedelta(12, unit="hour")
)

CPU times: user 2.38 s, sys: 29.7 ms, total: 2.41 s
Wall time: 2.42 s

%%time

ds_swot_gridded = coord_based_to_grid(
    ds_swot, 
    ds_natl60,
    data_vars=["ssh"], 
    t_res=pd.to_timedelta(12, unit="hour")
)

CPU times: user 4.25 s, sys: 20.8 ms, total: 4.27 s
Wall time: 4.29 s

%%time

ds_swotnadir_gridded = coord_based_to_grid(
    ds_swotnadir, 
    ds_natl60,
    data_vars=["ssh"], 
    t_res=pd.to_timedelta(12, unit="hour")
)

CPU times: user 4.36 s, sys: 30.9 ms, total: 4.39 s
Wall time: 4.41 s

import holoviews as hv
hv.extension("matplotlib")

variable = "ssh" # "vort_r" # "ke" #  
cmap = "viridis" # "RdBu_r" # "YlGnBu_r" #
field_name = "NATL60"

ssh_ds = xr.Dataset({
    field_name: ds_natl60[variable],
    "NADIR": np.isfinite(ds_nadir_gridded[variable]),
    "SWOT": np.isfinite(ds_swot_gridded[variable]),
    "SWOTNADIR": np.isfinite(ds_swotnadir_gridded[variable]),
})


to_plot_ds = ssh_ds.transpose("time", "lat", "lon")#.isel(time=slice(25, 55, 1))

clim = (
    to_plot_ds[
        [field_name, "NADIR", "SWOT", "SWOTNADIR"]
    ].to_array().pipe(lambda da: (da.quantile(0.005).item(), da.quantile(0.995).item()))
)

images = hv.Layout([
    hv.Dataset(to_plot_ds)
    .to(hv.QuadMesh, ["lon", "lat"], v).relabel(v)
    .options(cmap=cmap, clim=clim)
    for v in to_plot_ds]
).cols(2).opts(sublabel_format="")

hv.output(images, holomap="gif", fps=2, dpi=125)
# hv.save(images, filename="dc20a_natl60", fmt="gif", fps=2, dpi=125)