GPCPGPCP

The Global Precipitation Climatology Project – Accomplishments and future outlook

Arnold GruberDirector of the GPCP

NOAA NESDIS

IPWG23-27 September 2002, Madrid, Spain

GPCPGPCP

GPCPGPCP

GPCPGPCPGlobal Precipitation Climatology Global Precipitation Climatology

ProjectProject

Organized in 1986 Organized in 1986 Component of the Global Energy and Water Component of the Global Energy and Water

Cycle Experiment (GEWEX) of WCRP Cycle Experiment (GEWEX) of WCRP Objectives: Objectives:

Improve understanding of seasonal to inter-annual Improve understanding of seasonal to inter-annual and longer term variability of the global and longer term variability of the global hydrological cyclehydrological cycle

Determine the atmospheric heating needed for Determine the atmospheric heating needed for climate prediction models climate prediction models

Provide an observational data set for model Provide an observational data set for model validation and initialization and other hydrological validation and initialization and other hydrological applicationsapplications

GPCPGPCPGlobal Precipitation Climatology Project

Data Processing Centres

GMS Meteosat GOES NOAA

JAPAN EUROPE UNITED STATES

Geostationary Satellite Precipitation Data Centre

Surface Reference Data Centre scattering

(ocean)

Polar SatellitePrecipitation Data Center

Emission(land+ocean)

NASA-GSFC NOAA-NESDIS

GPC Merge Development CentreMerged Global Analysis

Global TropicsNOAA - NWS – J. Janowiak

NASA - GSFC –R.Adler

MW Component CAL/VAL Component

IR Component

Station Observations(CLIMAT, SYNOP National Collections)Gauge - Only AnalysisGlobal Precipitation Climatology CentreDWD - GERMANY, B. Rudolf

(EVAC- UOK- M.Morrissey)Algorithm Intercompararison ProgramValidation

GPCPGPCP

A. Chang R.FerraroA. Chang R. Ferraro

GPCPGPCPRemote Sensing Estimates used in GPCP• Infra –red

GOES – RR linearly related to fractional

pixels Tcld<235K– Most effective for deep convective

clouds, used only in 40N,S zone– High spatial and temporal resolution– false signatures, insensitive to warm

top rain TOVS– Regression between cloud

parameters and rain gauges– Used in high latitudes where MW

and GPI techniques is poor OPI– OLR precipitation Index

• Microwave (SSM/I)– Closely related to

hydrometeors– Emission from cloud drops

( 29 GHz). Most effective over water surfaces ( Tsfc <<Tcld)

– Scattering by ice particles over land over land ( 89, Tcld< Ta)

– only ice clouds over land, low resolution, no estimate over snow and ice

GPCPGPCP

GPCPGPCP

Monthly Mean Analysis Procedures

Monthly means –stepwise bias corrections; i.e., IR, adjusted to MW, satellite, adjusted to gauges, final blending uses inverse error weighting ( Huffman, et al 1995 and Huffman et al, 1997)

Pentad – combines satellite estimates by maximum likelihood estimates, then bias removal by solving a Poisson equation with gauges as boundary conditions. ( Xie and Arkin, 1996,1997)

All products sum to monthly means

GPCPGPCPIMPORTANT POINT

Algorithms are designed for liquid precipitation

Gauges

Used to produce a gridded analysis, incorporates water equivalent of solid precipitation

Final GPCP Precipitation Field

satellite estimates adjusted to large scale gauge analysis ( water equivalent of solid precipitation incorporated in this stage)

GPCPGPCP

GPCPGPCP

Current ProductsCurrent Products Monthly mean 2.5° x 2.5° latitude/longitude (Adler Monthly mean 2.5° x 2.5° latitude/longitude (Adler

et al., 2002, submitted J Hydromet )et al., 2002, submitted J Hydromet ) Merged satellite and gauge, error estimatesMerged satellite and gauge, error estimates Satellite components: microwave and infrared Satellite components: microwave and infrared

estimates, error estimatesestimates, error estimates Gauge analysis, error estimates (Rudolf, DWD Gauge analysis, error estimates (Rudolf, DWD

Germany)Germany) Intermediate analysis products, e.g., merged Intermediate analysis products, e.g., merged

satellite estimatessatellite estimates Daily 1 x 1 degree, ( Huffman et al, 2001, J. Daily 1 x 1 degree, ( Huffman et al, 2001, J.

Hydromet) Hydromet) Pentad ( Xie, et al, 2002, In press, J Climate)Pentad ( Xie, et al, 2002, In press, J Climate)

Global Precipitation Climatology Global Precipitation Climatology ProjectProject

http://lwf.ncdc.noaa.gov/oa/wmo/wdcamet-ncdc.html

1985 2000199519901979& Continuing- Version 2 , Pentad 1997 Daily 1x 1, deg

GPCPGPCPGlobal Precipitation Climatology Project

Annual Mean Precipitation

mm/day

GPCPGPCPGPCPGPCP

GPCPGPCP1 x 1 degree, daily precipitation January 1, 1998

mm/day

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Validation

GPCPGPCPValidation –Surface Reference Data Center – EVAC Univ Oklahoma

Director: Mark Morrissey

Monthly, Daily – various locations around world

http://srdc.evac.ou.edu

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GPCPGPCP

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Applications

GPCPGPCP

GPCPGPCP

Courtesy R. Adler

GPCPGPCP

PREDICTED

OBSERVED

Global Precipitation

GPCPGPCP

GPCPGPCP

GPCPGPCP

Monthly Anomaly (5N-5S) Sea Surface Temperature (C)

Precipitation (mm/day)

GPCPGPCPMean Annual Difference

GPCPGPCP

• New Instruments/Improved Algorithms– TRMM: a calibration source– AMSR: improved MW algorithm

• Use of Multiple Satellites– Operational and research satellites e.g. multiple microwave

observations from AMSU, AMSR, SSM/I, TRMM• Solid precipitation

– Snow rate• Precipitation in complex terrain

– A challenge - microphysical cloud properties to detect “warm top rain”

Future Outlook/Issues

GPCPGPCP

GPCPGPCPObservation Times by end of 20020

6

12

18

915

21 3

DMSPNOAAAquaADEOS

GPCPGPCP

GPCPGPCP

Global Precipitation Climatology Data• Monthly Mean 2.5 x 2.5 degree – 1979 and continuing

• Pentad ( 5 day) 2.5 x 2.5 degree – 1979 and continuing

• Daily, 1 x 1 degree - 1997 and continuing

Available On Line from World Data Center A at The National Climatic Data Center:

http://lwf.ncdc.noaa.gov/oa/wmo/wdcamet-ncdc.html