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The Global Precipitation Climatology Project – Accomplishments and future outlook. Arnold Gruber Director of the GPCP NOAA NESDIS IPWG 23-27 September 2002, Madrid, Spain. Global Precipitation Climatology Project. Organized in 1986
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The Global Precipitation Climatology Project – Accomplishments and future outlook Arnold Gruber Director of the GPCP NOAA NESDIS IPWG 23-27 September 2002, Madrid, Spain
Global Precipitation Climatology Project • Organized in 1986 • Component of the Global Energy and Water Cycle Experiment (GEWEX) of WCRP • Objectives: • Improve understanding of seasonal to inter-annual and longer term variability of the global hydrological cycle • Determine the atmospheric heating needed for climate prediction models • Provide an observational data set for model validation and initialization and other hydrological applications
Global Tropics NOAA - NWS – J. Janowiak Global Precipitation Climatology Project IR Component Data Processing Centres GMS Meteosat GOES NOAA JAPAN EUROPE UNITED STATES MW Component CAL/VAL Component Polar Satellite Precipitation Data Center Geostationary Satellite Precipitation Data Centre Surface Reference Data Centre Emission scattering (EVAC- UOK- M.Morrissey) (ocean) (land+ocean) Algorithm Intercompararison Program NASA-GSFC NOAA-NESDIS Validation A. Chang A. Chang R. Ferraro R.Ferraro GPC Merge Development Centre Merged Global Analysis NASA - GSFC –R.Adler Station Observations (CLIMAT, SYNOP National Collections) Gauge - Only Analysis Global Precipitation Climatology Centre DWD - GERMANY, B. Rudolf
Remote 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
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
IMPORTANT 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)
Global Precipitation Climatology Project • Current Products • Monthly mean 2.5° x 2.5° latitude/longitude (Adler et al., 2002, submitted J Hydromet ) • Merged satellite and gauge, error estimates • Satellite components: microwave and infrared estimates, error estimates • Gauge analysis, error estimates (Rudolf, DWD Germany) • Intermediate analysis products, e.g., merged satellite estimates • Daily 1 x 1 degree, ( Huffman et al, 2001, J. Hydromet) • Pentad ( Xie, et al, 2002, In press, J Climate) http://lwf.ncdc.noaa.gov/oa/wmo/wdcamet-ncdc.html 1985 1990 1995 2000 • & Continuing- Version 2 , Pentad 1997 Daily 1x 1, deg
Global Precipitation Climatology Project Annual Mean Precipitation mm/day
1 x 1 degree, daily precipitation January 1, 1998 mm/day
Validation –Surface Reference Data Center – EVAC Univ Oklahoma Director: Mark Morrissey Monthly, Daily – various locations around world http://srdc.evac.ou.edu
Global Precipitation PREDICTED OBSERVED
Monthly Anomaly (5N-5S) Sea Surface Temperature (C) Precipitation (mm/day)
Future Outlook/Issues • 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”
Observation Times by end of 2002 0 21 3 18 6 DMSP NOAA Aqua ADEOS 9 15 12
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