The Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) Dataset: Quasi-Global Precipitation Estimates for Drought Monitoring and Trend Analysis Peterson PJ, Funk CC, Landsfeld MF, Husak GJ, Pedreros DH, Verdin JP, Rowland JD, Michaelsen JC, Shukla S, McNally A, Verdin AP AGU Fall Meeting: Tuesday, 2014.12.16 chg.geog.ucsb.edu/data/chirps

Thanks to, USGS, USAID, NOAA and NASA SERVIR for funding George Huffman for TRMM data Wassila Thiaw and Nicholas Novella for CPC IR data Ken Knapp for B1 IR data GHCN, GTS and GSOD Tufa Dinku at IRI for feedback Jim Rowland at EROS for feedback Regional data providers INSIVUMEH, ETESA, Jorgeluis Vazquez, CATIE, Eric Alfaro, IDEAM, Tamuka Magadrize, Sharon Nicholson, Dave Allured, Haline Heidinger, Junior

Overview of CHIRPS process 1) Create historic precipitation climatology CHPclim 2) Convert IR data to precipitation estimate IRP IRP = b0 + b1*(Cold Cloud Duration Percent) 3) Apply time variability of IRP to CHPclim to make CHIRP CHIRP = CHPclim * (IRP %normal) 4) Blend in stations with CHIRP to make CHIRPS chg.geog.ucsb.edu/data/chirps

Station density

CHIRPS characteristics Spatial Extent: Quasi-Global: all longitudes, 50N-50S Spatial resolution: 0.05° x 0.05° Temporal extent: 1981 – present Temporal resolution: daily, pentads, dekads, monthly, 3-monthly Two products, different latency: Preliminary CHIRPS (GTS only) 2nd day after new pentad Final CHIRPS (all available stations) > 15th of the following month chg.geog.ucsb.edu/data/chirps

Colombia IDEAM SON 338 validation stations SON time series stats Source correlation MAE CHIRP 0.39 65.7 CHIRPS 0.97 38.3 CFS 0.76 221.0 CPC-Unif 0.45 154.0 ECMWF 0.76 203.0 GPCC 0.96 20.6

Colombia IDEAM SON total [mm] 900 800 700 600 500 400 1985 1990 1995 2000 2005 2010

Colombia IDEAM SON total [mm] 1200 1000 800 600 400 1985 1990 1995 2000 2005 2010 Diego Pedreros Poster GC33C-0534: The Use of CHIRPS to Analyze Historical Rainfall in Colombia, Wed. 1:40 - 6pm

Wet season map

CHIRPS WST Bias Ratio (data/GPCC)

CHIRPS WST Correlation

chg.geog.ucsb.edu/data/chirps Conclusions CHIRPS 30+ year record provides historical context for modern droughts. CHIRPS is comparable to GPCC with higher spatial resolution and lower latency. CHIRPS supports consistent drought monitoring. Starting with CHPclim leads to low bias estimates. CHIRPS adds to FEWS NET’s confluence of evidence. CHIRPS v2.0 will be released late January 2015 chg.geog.ucsb.edu/data/chirps

IR to IRP Cold Cloud Duration Regress Cold Cloud Duration (CCD) to TRMM-V7 pentad precipitation [mm/day] at each pixel for each month (2000-2012). Use CCD to calculate near real time precipitation (IRP) from CPC-IR (½ hourly). Apply to B1 IR data (3-hourly) from 1981-2000 to extend IRP time series. TRMM-V7 rain rate [mm/day] % of time IR temperature < 235o K

CHG Station Climatology Database (CSCD) Global sources: GHCN, GTS, GSOD Regional/National sources: Sahel, Nicholson, Peru, SUNFUN, Tanzania, Mozambique, Zambia, Ethiopia, Malawi, Mozambique, Belize, Guatemala, Central America, Mexico, SMN, Colombia, Panama, Afghanistan, Himalaya, Brazil Over ½ billion records across 135k stations since 1981 Quality Control: False zeroes, location check, elevations, GSOD duplicates, neighbor coherence, reality checks Decrease in available station data over time

Colombia IDEAM AMJ/SON Monthly AMJ stats 1981-2013 Source correlation MAE CHIRP 0.38 71.9 CHIRPS 0.96 40.7 CFS 0.82 281.0 CPC-Unif 0.40 166.0 ECMWF 0.72 255.0 GPCC 0.98 12.9 Monthly SON stats 1981-2013 Source correlation MAE CHIRP 0.39 65.7 CHIRPS 0.97 38.3 CFS 0.76 221.0 CPC-Unif 0.45 154.0 ECMWF 0.76 203.0 GPCC 0.96 20.6

Colombia IDEAM AMJ total [mm] 900 800 700 600 500 400 1985 1990 1995 2000 2005 2010

Colombia IDEAM AMJ total [mm] 1200 1000 800 600 400 1985 1990 1995 2000 2005 2010

Droughts in historical context CHIRPS MAM anomaly 1984 2000 2011

CHIRPS WST MAE

Snippets This code on your webserver: Gives you this image on your website:

Construct Wet Season Total comparisons For each dataset, ARC2, CFS, CHIRP, CHIRPS, CPCU, ECMWF, GPCC, RFE2, TAMSAT and TRMM-RT7 Construct cubes of Wet Season Totals and compare to GPCC.

12,000 8,000 4,000

Crop Zones Elevation Population

The GeoCLIM Climatological Analysis The Climatological Analysis tool in the GeoCLIM allows the user to calculate statistics, trends and frequencies for a season for a given set of years. chg.geog.ucsb.edu/data/chirps/index.html tinyurl.com/chg-products/CHIRPS-latest

The Water Requirement Satisfaction Index (WRSI) model The WRSI is an indicator of crop performance based on the availability of water to the crop during a growing season. The main data inputs in this model are precipitation and evapotranspiration.

Mean Absolute Error [mm/month] (less is better) chg.geog.ucsb.edu/data/chirps/index.html tinyurl.com/chg-products/CHIRPS-latest

CHIRPS WST Correlation CHIRPS ARC2 RFE2 TAMSAT

CHIRPS WST Bias Ratio CHIRPS ARC2 RFE2 TAMSAT

CHIRPS WST MAE CHIRPS ARC2 RFE2 TAMSAT

Cross validation stats for April

Cross validation stats for April