These scripts aim to visualize and extract time series of long-term climate data at discrete spatial locations from the CRU TS monthly high-resolution gridded multivariate climate dataset (Harris et al. 2020; https://www.nature.com/articles/s41597-020-0453-3).

https://github.com/johannesuhl/netcdf2mp4/blob/main/netcdf2mp4.py will create the above visualization, and extract_cru_data_municipality.py will extract CRU time series for specific locations, do some conversions and also visualize the data.

This repository contains a point shapefile holding the centroids of municipalities in Mexico (mex_admbnda_adm2_govmex_20210618_pt.shp, adopted from data obtained at https://data.humdata.org/dataset/mexican-administrative-level-0-country-1-estado-and-2-municipio-boundary-polygons).

CRU data needs to be obtained from https://catalogue.ceda.ac.uk/uuid/c26a65020a5e4b80b20018f148556681 and unzipped in a subfolder of the directory where the script extract_cru_data_municipality.py resides.

extract=True: The script extract_cru_data_municipality.py will read the municipalities shapefile, and create a CSV file containing the CRU temperature estimates for each point in time, for each municipality in Mexico.

vis=True: Optionally, the script will plot the data for each point in time and create an animated GIF. The user can constrain the visualization to a specific coordinate range (note that these coordinates are image coordinates, not world coordinates):

currarr=currarr[mx_subset_imgcoo[0]:mx_subset_imgcoo[1],mx_subset_imgcoo[2]:mx_subset_imgcoo[3]]

or to a specific month:

if not currdate.month in [8]:
    continue

netcdf2geotiff=True:By setting netcdf2geotiff=True, the script will export the CRU data to GeoTIFF for a given (or all) point in time.

Harris, I., Osborn, T. J., Jones, P., & Lister, D. (2020). Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset. Scientific data, 7(1), 1-18. https://doi.org/10.1038/s41597-020-0453-3