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nsidc-tools

Source Dataset Citations

Daily and Monthly Sea Ice Index Data (NSIDC ID: G02135)

Fetterer, F., K. Knowles, W. Meier, M. Savoie, and A. K. Windnagel. 2017, updated daily. Sea Ice Index, Version 3. Boulder, Colorado USA. NSIDC: National Snow and Ice Data Center. doi: http://dx.doi.org/10.7265/N5K072F8.

EASE-Grid Sea Ice Age Data (NSIDC ID: NSIDC-0611)

Tschudi, M., C. Fowler, J. Maslanik, J. S. Stewart, and W. Meier. 2016. EASE-Grid Sea Ice Age, Version 3. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. doi: http://dx.doi.org/10.5067/PFSVFZA9Y85G.

Initial Setup

conda create -n nsidc-tools-env python=2 numpy pandas requests statsmodels basemap beautiful-soup

  • Activate the environment: conda activate nsidc-tools-env
  • Edit config.cfg as necessary

Tool descriptions

DailyIceIndexPlotter

Usage: python nsidc-tools/DailyIceIndexPlotter.py config.cfg

  • Downloads the latest Daily Sea Ice Index datasets from NSIDC FTP to local storage. (~5MB)
  • Initializes pandas dataframes for North hemisphere, South hemisphere, and Global areas of interest.
  • Generates overlapping line plots to show daily progression of sea ice extent (smoothed with 5-day window mean):

Daily Arctic Sea Ice Index

MonthlyIceIndexPlotter

Extra Notes: There is one constant at the top of the script that can be edited at the user's discretion:

  • PLOT_MONTH: Force the script to plot a specific month rather than the current month

Usage: python nsidc-tools/MonthlyIceIndexPlotter.py config.cfg

  • Downloads the latest Monthly Sea Ice Index datasets from NSIDC FTP to local storage. (<1MB)
  • Initializes pandas dataframes for the downloaded datasets with some data sanitizing.
  • For the current month, calculates linear trend using Ordinary Least Squares regression based on the 1981-2010 mean sea ice extent.
  • Plots results in 2 ways but basically shows the same thing 😁:
    • Monthly mean sea ice extent overlaid by trendline:

Monthly Arctic Sea Ice Index

  • Departure from 1981-2010 mean in terms of % difference:

Monthly Arctic Sea Ice Anomalies

IceAgePlotter

Extra Requirements: You must have a NASA EarthData Catalogue account, and a properly-setup .netrc file:

  • Windows: echo machine urs.earthdata.nasa.gov login X password Y >> %USERPROFILE%\.netrc (replace X and Y with your username and password)

Extra Notes: There are several constants at the top of the script that can be edited at the user's discretion:

  • AGE_CONST: The dataset ID for local storage folder organization
  • AGE_FILESIZE: The storage size of each source dataset in KB
  • GEO_BOUNDS: List of floats for the boundary masking
    • [SouthernMostLat, NorthernMostLat, WestmostLon, EastmostLon]
  • WEEK: Which week number should be plotted, default is the current week

Usage: python nsidc-tools/IceAgePlotter.py config.cfg

  • Asks the user whether or not to download any missing Weekly Sea Ice Age datasets from NASA EarthData DAAC to local storage. (~880MB for full dataset)
  • Reads all available local ice age datasets and generates pandas dataframes for the pixel counts of the following ice age classes:
    • First-year Ice
    • Second-year Ice
    • Third-year Ice
    • Fourth-year Ice
    • Fifth-year Ice and Older
  • Plots cumulative percentage of each ice age class by year for the current week number, and also shows a map of the spatial distribution of sea ice age composition for the most recent binary dataset:

Monthly Arctic Sea Ice Anomalies

Why does this exist?

Combination of seeing the great plots in the NSIDC Arctic Sea Ice News & Analysis and an effort to improve my skills with Pandas, Requests, Matplotlib and Basemap.