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Assessing the Relative Performance of Microwave-Based Satellite Rain-Rate Retrievals Using TRMM Ground Validation Data

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  • 1 Science Systems & Applications, Inc., Lanham, and NASA Goddard Space Flight Center, Greenbelt, Maryland
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Abstract

Spaceborne microwave sensors provide critical rain information used in several global multisatellite rain products, which in turn are used for a variety of important studies, including landslide forecasting, flash flood warning, data assimilation, climate studies, and validation of model forecasts of precipitation. This study employs 4 yr (2003–06) of satellite data to assess the relative performance and skill of the Special Sensor Microwave Imager [SSM/I (F13, F14, and F15], Advanced Microwave Sounding Unit [AMSU-B (N15, N16, and N17)], Advanced Microwave Scanning Radiometer for Earth Observing System [AMSR-E (Aqua)], and the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) in estimating surface rainfall based on direct instantaneous comparisons with ground-based rain estimates from the TRMM Ground Validation (GV) sites at Kwajalein, Republic of the Marshall Islands (KWAJ), and Melbourne, Florida (MELB). The relative performance of each of these satellite estimates is examined via comparisons with space- and time-coincident GV radar-based rain-rate estimates. Because underlying surface terrain is known to affect the relative performance of the satellite algorithms, the data for MELB were further stratified into ocean, land, and coast categories using a 0.25° terrain mask. Of all the satellite estimates compared in this study, TMI and AMSR-E exhibited considerably higher correlations and skills in estimating–observing surface precipitation. While SSM/I and AMSU-B exhibited lower correlations and skills for each of the different terrain categories, the SSM/I absolute biases trended slightly lower than AMSR-E over ocean, where the observations from both emission and scattering channels were used in the retrievals. AMSU-B exhibited the least skill relative to GV in all of the relevant statistical categories, and an anomalous spike was observed in the probability distribution functions near 1.0 mm h−1. This statistical artifact appears to be related to attempts by algorithm developers to include some lighter rain rates, not easily detectable by its scatter-only frequencies. AMSU-B, however, agreed well with GV when the matching data were analyzed on monthly scales. These results signal to developers of global rainfall products, such as the TRMM Multisatellite Precipitation Analysis (TMPA) and the Climate Data Center’s Morphing (CMORPH) technique, that care must be taken when incorporating data from these input satellite estimates to provide the highest-quality estimates in their products.

Corresponding author address: David B. Wolff, NASA/GSFC, Code 613.1, Greenbelt, MD 20771. Email: david.b.wolff@nasa.gov

Abstract

Spaceborne microwave sensors provide critical rain information used in several global multisatellite rain products, which in turn are used for a variety of important studies, including landslide forecasting, flash flood warning, data assimilation, climate studies, and validation of model forecasts of precipitation. This study employs 4 yr (2003–06) of satellite data to assess the relative performance and skill of the Special Sensor Microwave Imager [SSM/I (F13, F14, and F15], Advanced Microwave Sounding Unit [AMSU-B (N15, N16, and N17)], Advanced Microwave Scanning Radiometer for Earth Observing System [AMSR-E (Aqua)], and the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) in estimating surface rainfall based on direct instantaneous comparisons with ground-based rain estimates from the TRMM Ground Validation (GV) sites at Kwajalein, Republic of the Marshall Islands (KWAJ), and Melbourne, Florida (MELB). The relative performance of each of these satellite estimates is examined via comparisons with space- and time-coincident GV radar-based rain-rate estimates. Because underlying surface terrain is known to affect the relative performance of the satellite algorithms, the data for MELB were further stratified into ocean, land, and coast categories using a 0.25° terrain mask. Of all the satellite estimates compared in this study, TMI and AMSR-E exhibited considerably higher correlations and skills in estimating–observing surface precipitation. While SSM/I and AMSU-B exhibited lower correlations and skills for each of the different terrain categories, the SSM/I absolute biases trended slightly lower than AMSR-E over ocean, where the observations from both emission and scattering channels were used in the retrievals. AMSU-B exhibited the least skill relative to GV in all of the relevant statistical categories, and an anomalous spike was observed in the probability distribution functions near 1.0 mm h−1. This statistical artifact appears to be related to attempts by algorithm developers to include some lighter rain rates, not easily detectable by its scatter-only frequencies. AMSU-B, however, agreed well with GV when the matching data were analyzed on monthly scales. These results signal to developers of global rainfall products, such as the TRMM Multisatellite Precipitation Analysis (TMPA) and the Climate Data Center’s Morphing (CMORPH) technique, that care must be taken when incorporating data from these input satellite estimates to provide the highest-quality estimates in their products.

Corresponding author address: David B. Wolff, NASA/GSFC, Code 613.1, Greenbelt, MD 20771. Email: david.b.wolff@nasa.gov

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