This package is designed to re-create the so-called climate stripes (originally created by Ed Hawkins at the University of Reading). This code is part of a class assignment for ATMS 597 (Spring 2020) at the University of Illinois at Urbana Champaign. Use this class function to download daily temperature data from Global Historical Climate Network (GHCN) datasets hosted by the National Centers for Environmental Information (NCEI), and plot the anomalies as climate stripes according to the time duration and sampling interval desired by the user. The code also plots the mean values as a separate line plot, overlayed on the stripes.

The source code is in .py format and can be found in ATMS-597-SP-2020-Project-2-Group-D (Current Directory)/scripts/

Required packages

• NumPy
• Requests
• Pandas
• Json
• Matplotlib

The stripes frequencies supported are:

• Yearly
  • 'Y'
• Monthly
  • 'M'
• Weekly
  • 'W'
• N days, where N is a natural number less than 1000
  • N

from scripts.climate_stripes import climate_stripes

# Input GHCN station ID, dates and NCEI token to create a class object 
stripes = climate_stripes(station_id, "1950-01-01", "2015-12-31", Token)

# Plot at desired frequency (Y,M or W) without line plot and save figure  
stripes.plot('Y','Yearly_Stripes',lineplot = False)

# Call the plot method to plot 100-day stripes with line plot, without saving the figure
stripes.plot(100, '', lineplot = True)

Full Example Notebook

• Divyansh Chug
• Dongwei Fu
• Puja Roy