Using ICESat-2 ATL15 (Gridded Arctic Land Ice Height) to investigate ice-surface height anomalies#

Written by#

Key learning outcomes:#

  • How to gather data from disparate sources.

  • What is a Coordinate Reference System (CRS) and why it matters.

  • How to use geometries including Points and Polygons to define an area of interest and subset data.

  • The basics of how the icepyx library simplifies obtaining and interacting with ICESat-2 data.

  • How Xarray can simplify the import of multi-dimensional data.

  • Open, plot, and explore gridded raster data.

Computing environment#

We will set up our computing environment with library imports and utility functions

Tip: If you need to import a library that is not pre-installed, use %pip install <package-name> alone within a Jupyter notebook cell to install it for this instance of CryoCloud (the pip installation will not presist between logins. All of the libraries we intend to use are pre-installed so we can skip this step.

# import internal libraries
import os

# import external libraries
import earthaccess
import geopandas as gpd
import h5py
import hvplot.xarray
import icepyx as ipx
import matplotlib as mpl
%matplotlib widget
import matplotlib.colors as colors
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
from mpl_toolkits.axes_grid1 import make_axes_locatable
import numpy as np
import pandas as pd
from pyproj import CRS, Transformer
import rioxarray
import s3fs
import xarray as xr

# define utility function
def ll2ps(lon, lat):
    Transform coordinates from geodetic coordinates (lon, lat)
    to Greenland (epsg:3413) coordinates (x, y)
    x, y = ll2ps(lon, lat)
    * lon, lat in decimal degrees (lon: W is negative; lat: S is negative)
    * x, y in [m]
    crs_ll = CRS("EPSG:4326")
    crs_xy = CRS("EPSG:3413")
    ll_to_xy = Transformer.from_crs(crs_ll, crs_xy, always_xy = True)
    x, y = ll_to_xy.transform(lon, lat)
    return x, y