PRISM temperature and other weather variable grids (from https://prism.oregonstate.edu/recent/) that have been converted to political entities (county FIPS and ZIP Codes) for use in:
Parks, R.M., Anderson, G.B., Nethery, R.C. et al. Tropical cyclone exposure is associated with increased hospitalization rates in older adults. Nat Commun 12, 1545 (2021). https://doi.org/10.1038/s41467-021-21777-1
Elser H, Parks RM, Moghavem N, Kiang MV, Bozinov N, Henderson VW, Rehkopf DH, Casey JA. (2021). Anomalously warm weather and acute care visits in patients with multiple sclerosis: A retrospective study of privately insured individuals in the US, PLoS Medicine https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1003580
Other papers to be posted here very soon once published.
Currently in RDS from R but will convert to csv. In Python easy to load RDS files (https://stackoverflow.com/questions/40996175/loading-a-rds-file-in-pandas).
Work in progress by Robbie M Parks et al.
2018...2022... and beyond
Variable:
tmean - mean daily temperature
ppt - daily precipitation
Contents of project:
Countries covered (I have other countries from ERA5 in another location):
USA
Input:
PRISM 4k values
Code (prog):
Bash files:
grid_county_intersection_prism_fips.sh - Perform for chosen year by county for each day in chosen year
grid_county_intersection_prism_zip.sh - Perform for chosen year by zip code for each day in chosen year
Actual processing function:
02_grid_county_intersection/grid_county_intersection_raster_prism.R - does the heavy lifting called in by bash files for either fips or zip
Output:
grid_county_intersection_raster_prism - either fips or zip files by year