378
Multiscale Hydrologic Remote Sensing: Perspectives and Applications
15.3.2 gloBal PReciPitation cliMatology PRoject
The first such multisensor blending algorithm is the GPCP combination computed
on a monthly 2.5° latitude–longitude grid from 1979 to present. The Mesoscale
Applications and Processes Group at NASA/GSFC developed and computed the
current GPCP Version 2 Satellite-Gauge dataset based on a variety of input datasets
provided by other GPCP components (Huffman et al. 1997; Adler et al. 2003).
GPCP is a mature global precipitation product that uses multiple sources of observations, including surface information, to permit a more complete understanding of
the spatial and temporal patterns of global precipitation. The merging of estimates
from multiple sources takes advantage of the strengths offered by each type: (1) local
unbiased estimates where rain gauge data are available, (2) physically based MW
rain rates estimated from LEO satellites, and (3) high-temporal-resolution indirect
estimates from VIS/IR sensors on GEO satellites. Data from over 6000 rain gauge
stations together with satellite IR and PMW observations have been merged to estimate monthly rainfall on a 2.5° global grid from 1979 to the present. The GPCP
Global Precipitation Climatology Centre (GPCC) maintains a collection of highquality rain gauge measurements that are used to prepare comprehensive land-based
rainfall analyses. The careful combination of satellite-based rainfall estimates provides the most complete analysis of rainfall available to date over the global oceans
and adds necessary spatial detail and bias reduction to the rainfall analyses over
land. In addition to the combination of these datasets, careful examination of the
uncertainties in the rainfall analysis is provided as part of the GPCP products. Given
its monthly 2.5° resolution, GPCP is of help in representing temporal and spatial
variations of precipitation for climate change and water cycle studies.
15.3.3 tRMM-BaSed MultiSatellite PReciPitation analySiS algoRithM
According to Huffman et al. (2007), the TMPA is intended to provide a “best” estimate of quasiglobal precipitation from the wide variety of modern satellite-borne
precipitation-related sensors and surface rain gauge networks. The TMPA estimates
are produced in four stages: (1) the MW precipitation estimates are calibrated and
combined; (2) the IR precipitation estimates are created using the calibrated MW
precipitation; (3) the MW and IR estimates are combined; and (4) the rain gauge
data are incorporated. Figure 15.2 presents a block diagram of the TMPA estimation
procedure. Each TMPA precipitation field is best interpreted as the precipitation
rate effective at the nominal observation time. TMPA provides two standard 3B42level products for the research community: the near-real-time 3B42RT and post-realtime 3B42V6, both available at 3-hourly and 0.25° grid resolution covering the globe
50°S–N latitude band.
The real-time product, 3B42RT, uses the TRMM Combined Instrument (TRMM
precipitation radar and TMI) dataset to calibrate precipitation estimates derived
from available LEO MW radiometers and then merges all of the estimates at 3-h
intervals. Gaps in the analyses are filled using GEO IR data regionally calibrated
to the merged MW product. The post-real-time product, 3B42V6, adjusts the
monthly accumulations of the 3-hourly fields from 3B42RT based on a monthly
Multiscale Hydrologic Remote Sensing: Perspectives and Applications
15.3.2 gloBal PReciPitation cliMatology PRoject
The first such multisensor blending algorithm is the GPCP combination computed
on a monthly 2.5° latitude–longitude grid from 1979 to present. The Mesoscale
Applications and Processes Group at NASA/GSFC developed and computed the
current GPCP Version 2 Satellite-Gauge dataset based on a variety of input datasets
provided by other GPCP components (Huffman et al. 1997; Adler et al. 2003).
GPCP is a mature global precipitation product that uses multiple sources of observations, including surface information, to permit a more complete understanding of
the spatial and temporal patterns of global precipitation. The merging of estimates
from multiple sources takes advantage of the strengths offered by each type: (1) local
unbiased estimates where rain gauge data are available, (2) physically based MW
rain rates estimated from LEO satellites, and (3) high-temporal-resolution indirect
estimates from VIS/IR sensors on GEO satellites. Data from over 6000 rain gauge
stations together with satellite IR and PMW observations have been merged to estimate monthly rainfall on a 2.5° global grid from 1979 to the present. The GPCP
Global Precipitation Climatology Centre (GPCC) maintains a collection of highquality rain gauge measurements that are used to prepare comprehensive land-based
rainfall analyses. The careful combination of satellite-based rainfall estimates provides the most complete analysis of rainfall available to date over the global oceans
and adds necessary spatial detail and bias reduction to the rainfall analyses over
land. In addition to the combination of these datasets, careful examination of the
uncertainties in the rainfall analysis is provided as part of the GPCP products. Given
its monthly 2.5° resolution, GPCP is of help in representing temporal and spatial
variations of precipitation for climate change and water cycle studies.
15.3.3 tRMM-BaSed MultiSatellite PReciPitation analySiS algoRithM
According to Huffman et al. (2007), the TMPA is intended to provide a “best” estimate of quasiglobal precipitation from the wide variety of modern satellite-borne
precipitation-related sensors and surface rain gauge networks. The TMPA estimates
are produced in four stages: (1) the MW precipitation estimates are calibrated and
combined; (2) the IR precipitation estimates are created using the calibrated MW
precipitation; (3) the MW and IR estimates are combined; and (4) the rain gauge
data are incorporated. Figure 15.2 presents a block diagram of the TMPA estimation
procedure. Each TMPA precipitation field is best interpreted as the precipitation
rate effective at the nominal observation time. TMPA provides two standard 3B42level products for the research community: the near-real-time 3B42RT and post-realtime 3B42V6, both available at 3-hourly and 0.25° grid resolution covering the globe
50°S–N latitude band.
The real-time product, 3B42RT, uses the TRMM Combined Instrument (TRMM
precipitation radar and TMI) dataset to calibrate precipitation estimates derived
from available LEO MW radiometers and then merges all of the estimates at 3-h
intervals. Gaps in the analyses are filled using GEO IR data regionally calibrated
to the merged MW product. The post-real-time product, 3B42V6, adjusts the
monthly accumulations of the 3-hourly fields from 3B42RT based on a monthly