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
tinyurl.com/chg-products/CHIRPS-latest
Slide 2
1) Create historic precipitation climatology CHPclim 2) Convert
IR data to precipitation estimate IRP IRP = b 0 + b 1 *(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 Overview of CHIRPS process
chg.geog.ucsb.edu/data/chirps
tinyurl.com/chg-products/CHIRPS-latest
Slide 3
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 ( 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 < 235 o K
Slide 4
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 Screen GTS and GSOD for false zeroes
Over billion records across 135k stations since 1981 Quality
Control: GSOD duplicates, neighbor coherence, reality checks
Decrease in available station data over time
Slide 5
Station density
Slide 6
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) 2 nd day after new pentad Final CHIRPS (all
available stations) > 15 th of the following month
chg.geog.ucsb.edu/data/chirps
tinyurl.com/chg-products/CHIRPS-latest
Colombia IDEAM SON total [mm] 900 800 700 600 500 400 1985 1990
1995 2000 2005 2010
Slide 11
Colombia IDEAM SON total [mm] 1985 1990 1995 2000 2005 2010
1200 1000 800 600 400 GC33C-0534: The Use of CHIRPS to Analyze
Historical Rainfall in Colombia, Wed. 1:40 - 6pm
Slide 12
Wet season map
Slide 13
CHIRPS WST Bias Ratio (data/GPCC)
Slide 14
CHIRPS WST MAE
Slide 15
CHIRPS WST Correlation
Slide 16
Droughts in historical context CHIRPS MAM anomaly 1984 2000
2011
Slide 17
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. CHPclim provides low bias estimates. Next
release of CHIRPS January 2015.
Slide 18
Thanks to, USGS, USAID, NOAA and NASA SERVIR for funding George
Huffman for TRMM-V7 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
Slide 19
Snippets This code on your webserver:Gives you this image on
your website:
Slide 20
Slide 21
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.
Slide 22
12,000 8,000 4,000 0
Slide 23
Crop Zones Elevation Population
Slide 24
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
Slide 25
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.
Slide 26
chg.geog.ucsb.edu/data/chirps/index.html
tinyurl.com/chg-products/CHIRPS-latest Mean Absolute Error
[mm/month] (less is better)