The Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) Dataset: Quasi-Global...
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- Slide 1
- 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
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- 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
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- 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
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- 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
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- Station density
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- 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
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- Colombia IDEAM AMJ/SON Monthly AMJ stats 1981-2013 Source
correlationMAE CHIRP0.38 71.9 CHIRPS0.96 40.7 CFS0.82281.0
CPC-Unif0.40166.0 ECMWF0.72255.0 GPCC0.98 12.9 Monthly SON stats
1981-2013 Source correlationMAE CHIRP0.39 65.7 CHIRPS0.97 38.3
CFS0.76221.0 CPC-Unif0.45154.0 ECMWF0.76203.0 GPCC0.96 20.6
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- Colombia IDEAM AMJ total [mm] 900 800 700 600 500 400 1985 1990
1995 2000 2005 2010
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- Colombia IDEAM AMJ total [mm] 1985 1990 1995 2000 2005 2010
1200 1000 800 600 400
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- Colombia IDEAM SON total [mm] 900 800 700 600 500 400 1985 1990
1995 2000 2005 2010
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- 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
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- Wet season map
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- CHIRPS WST Bias Ratio (data/GPCC)
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- CHIRPS WST MAE
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- CHIRPS WST Correlation
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- Droughts in historical context CHIRPS MAM anomaly 1984 2000
2011
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- 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:
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- 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.
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- 12,000 8,000 4,000 0
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- Crop Zones Elevation Population
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- 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
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- 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)
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- CHIRPS WST Correlation RFE2 TAMSAT CHIRPS ARC2
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- CHIRPS WST Bias Ratio RFE2 TAMSAT CHIRPS ARC2
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- CHIRPS WST MAE RFE2 TAMSAT CHIRPS ARC2
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- Cross validation stats for April
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