ICESat-2 interpolation workflow

Summary Producing the ICESat-2 interpolated data variables followed a series of steps: using linear interpolation to fill in missing data, smoothing the data using gaussian smoothing, and then restricting the resulting interpolated/smoothed data using a KDTree method. Below, we detail the methods undertaken and demonstrate the effect each step has on the raw sea ice thickness data.

Version history: Version 1 (01/01/2022)

import xarray as xr # For working with gridded climate data 
import pandas as pd
import numpy as np
from utils.read_data_utils import read_book_data # Helper function for reading the data from the bucket
from utils.plotting_utils import interactiveArcticMaps

# Interpolating/smoothing packages 
from scipy.interpolate import griddata
from scipy.spatial import KDTree
from astropy.convolution import convolve
from astropy.convolution import Gaussian2DKernel

# Plotting dependencies
import cartopy.crs as ccrs
import holoviews as hv
import matplotlib.pyplot as plt
from matplotlib.axes import Axes
from cartopy.mpl.geoaxes import GeoAxes
GeoAxes._pcolormesh_patched = Axes.pcolormesh # Helps avoid some weird issues with the polar projection 
%config InlineBackend.figure_format = 'retina'
import matplotlib as mpl
mpl.rcParams['figure.dpi'] = 150 # Sets figure size in the notebook

# Remove warnings to improve display
import warnings 
warnings.filterwarnings('ignore')