CHIRPS Daily: Climate Hazards Group InfraRed Precipitation With Station Data (Version 2.0 Final)

UCSB-CHG/CHIRPS/DAILY
Dataset Availability
1981-01-01T00:00:00Z–2024-02-29T00:00:00Z
Dataset Provider
Earth Engine Snippet
ee.ImageCollection("UCSB-CHG/CHIRPS/DAILY")
Tags
chg climate geophysical precipitation ucsb weather

Description

Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) is a 30+ year quasi-global rainfall dataset. CHIRPS incorporates 0.05° resolution satellite imagery with in-situ station data to create gridded rainfall time series for trend analysis and seasonal drought monitoring.

Bands

Resolution
5566 meters

Bands

Name Units Min Max Description
precipitation mm/d 0* 1444.34*

Precipitation

* estimated min or max value

Terms of Use

Terms of Use

This datasets are in the public domain. To the extent possible under law, Pete Peterson has waived all copyright and related or neighboring rights to Climate Hazards Group Infrared Precipitation with Stations (CHIRPS).

Citations

Citations:
  • Funk, Chris, Pete Peterson, Martin Landsfeld, Diego Pedreros, James Verdin, Shraddhanand Shukla, Gregory Husak, James Rowland, Laura Harrison, Andrew Hoell & Joel Michaelsen. "The climate hazards infrared precipitation with stations-a new environmental record for monitoring extremes". Scientific Data 2, 150066. doi:10.1038/sdata.2015.66 2015.

Explore with Earth Engine

Code Editor (JavaScript)

var dataset = ee.ImageCollection('UCSB-CHG/CHIRPS/DAILY')
                  .filter(ee.Filter.date('2018-05-01', '2018-05-03'));
var precipitation = dataset.select('precipitation');
var precipitationVis = {
  min: 1,
  max: 17,
  palette: ['001137', '0aab1e', 'e7eb05', 'ff4a2d', 'e90000'],
};
Map.setCenter(17.93, 7.71, 2);
Map.addLayer(precipitation, precipitationVis, 'Precipitation');

Python setup

See the Python Environment page for information on the Python API and using geemap for interactive development.

import ee
import geemap.core as geemap

Colab (Python)

dataset = ee.ImageCollection('UCSB-CHG/CHIRPS/DAILY').filter(
    ee.Filter.date('2018-05-01', '2018-05-03')
)
precipitation = dataset.select('precipitation')
precipitation_vis = {
    'min': 1,
    'max': 17,
    'palette': ['001137', '0aab1e', 'e7eb05', 'ff4a2d', 'e90000'],
}

m = geemap.Map()
m.set_center(17.93, 7.71, 2)
m.add_layer(precipitation, precipitation_vis, 'Precipitation')
m
Open in Code Editor