Proposed Recipes for CHIRPS 2.0

rabernat commented 2 years ago

Note: I became aware of this dataset and the potential recipe via this twitter thread with @alexgleith

Source Dataset

CHIRPS: Rainfall Estimates from Rain Gauge and Satellite Observations

Since 1999, USGS and CHC scientists—supported by funding from USAID, NASA, and NOAA—have developed techniques for producing rainfall maps, especially in areas where surface data is sparse.

Estimating rainfall variations in space and time is a key aspect of drought early warning and environmental monitoring. An evolving drier-than-normal season must be placed in a historical context so that the severity of rainfall deficits can be quickly evaluated. However, estimates derived from satellite data provide areal averages that suffer from biases due to complex terrain, which often underestimate the intensity of extreme precipitation events. Conversely, precipitation grids produced from station data suffer in more rural regions where there are less rain-gauge stations. CHIRPS was created in collaboration with scientists at the USGS Earth Resources Observation and Science (EROS) Center in order to deliver complete, reliable, up-to-date data sets for a number of early warning objectives, like trend analysis and seasonal drought monitoring.

Link to the website / online documentation for the data: https://www.chc.ucsb.edu/data/chirps
The file format (e.g. netCDF, csv): gziped TIF
How are the source files organized? (e.g. one file per day): daily
How are the source files accessed (e.g. FTP): http

See script at https://github.com/digitalearthafrica/deafrica-scripts/blob/main/deafrica/data/chirps.py for a great starting point

Transformation / Alignment / Merging

None

Output Dataset

The DE-Africa folks are converting from regular GeoTIFF to COG + STAC catalog. So this could be a useful test scenario for that sort of pipeline.

alexgleith commented 2 years ago

Hey folks, if you'd like to test or explore the data, we have it stored on S3 with static STAC as well as listed in a STAC API. (Also in a Pangeo-like Open Data Cube Jupyter environment.)

S3: s3://deafrica-input-datasets/rainfall_chirps_monthly and soon _daily
STAC API: https://explorer.digitalearth.africa/stac/
Sandbox (JupyterHub): https://sandbox.digitalearth.africa/

rabernat commented 2 years ago

Great to see you here Alex! Thanks for the info.

pangeo-forge / staged-recipes