Comparisons with BGEP ULS sea ice draft estimates
Comparisons with BGEP ULS sea ice draft estimates#
Summary: In this notebook, we produce comparisons of the gridded ICESat-2 sea ice thickness data with draft measurements obtained from Upward Looking Sonar moorings deployed in the Beaufort Sea.
With more years of data we hope to explore seasonal de-trending of the data. Current tools (e..g seasonal_decompose from statsmodel) require more years of data then currently provided by the ICESat2-/BGEP overlap period (2018 to 2021)
Some previous studies have removed zero draft values from ULS before undertaking these comparisons. Hard to know what is best here. ATL10 are available for passive microwave concentrations > 50%. Our gridded thickness data includes all this (baring some additional anomaly filters) so could also include reasonably large stretches of thin ice/open water too. I have toyed with included a ‘thickness where we have ice’ variable too, which I could easily include later, then we would just compare this to positive values of ULS draft?
Could require a minimum number of IS2 grid-cells, but would perhaps need to depend on the chosen comp_res. Note how in some September/October months there appears to be no data overlap, I included some maps at the end to highlight this more, the moorings are right on the edge of the ice pack in September 2019 for instance.
We do a fair bit of averaging to try and reduce noise from various sources, including sampling differences/representation errors. One more direct way of dealing with this would be to use the day_of_the_month information in the IS-2 thickness data to see what actual day the nearest grid-cells to the moorings best represent and comapre that to the daily ULS data. This leads to using the along-track data too, hard to know when to stop..
We could have made better use of Pandas in this analysis but I was a bit time pressured.
Version history: Version 1 (01/01/2022)
Import notebook dependencies#
import xarray as xr import pandas as pd import numpy as np import itertools import pyproj from netCDF4 import Dataset import scipy.interpolate from utils.read_data_utils import read_book_data # Helper function for reading the data from the bucket from utils.plotting_utils import compute_gridcell_winter_means, interactiveArcticMaps, interactive_winter_mean_maps, interactive_winter_comparison_lineplot # Plotting from scipy import stats import datetime # Plotting dependencies import cartopy.crs as ccrs from textwrap import wrap import hvplot.pandas 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'] = 200 # Sets figure size in the notebook # Remove warnings to improve display import warnings warnings.filterwarnings('ignore')