Access Antarctica Data - Hands-On with ESA EarthCODE

This notebook opens Antarctica analysis-ready data cubes from EarthCODE object storage, inspects the merged Xarray dataset, and visualises selected ice shelf variables.

import matplotlib.pyplot as plt
import xarray as xr
from dask.diagnostics import ProgressBar
import hvplot.xarray
import numpy as np, matplotlib.pyplot as plt, imageio.v2 as imageio
import math
import rioxarray

import xarray as xr

cube_paths = [
    "https://s3.waw4-1.cloudferro.com/EarthCODE/OSCAssets/antarctica_cube/icetemp.zarr",
    "https://s3.waw4-1.cloudferro.com/EarthCODE/OSCAssets/antarctica_cube/sec.zarr",
    "https://s3.waw4-1.cloudferro.com/EarthCODE/OSCAssets/antarctica_cube/antarctica-combined.zarr",
    "https://s3.waw4-1.cloudferro.com/EarthCODE/OSCAssets/antarctica_cube/icemask_composite.zarr/"
]

ds = xr.open_mfdataset(cube_paths, engine="zarr",chunks={},compat='no_conflicts')
ds

minx, miny,maxx, maxy = -1657448.0-60000, -544313.0-20000, -1516351.0401517956, -381472.01474966406


b_melt = ds.ice_shelf_basal_melt_rate.sel(x=slice(minx, maxx), y=slice(miny, maxy)).hvplot()
ds.calving_fronts.sel(x=slice(minx, maxx), y=slice(miny, maxy)).hvplot(groupby='time') * b_melt

ds.calving_fronts.sel(x=slice(minx, maxx), y=slice(miny, maxy)).sum(dim=["x", "y"]).plot()

import xarray as xr
ds = xr.open_zarr("https://s3.waw4-1.cloudferro.com/EarthCODE/OSCAssets/s14science_amazonas/s14science_amazonas_tree_cover_change.zarr")
ds

ds.isel(time=10).tree_cover_change.coarsen(lat=1000, lon=1000, boundary="trim").max().plot()