HR: 1400h
AN: H53B-09    [Abstracts]
TI: An Inter-comparison of Passive Microwave Rainfall Derived From Various Sensors and Algorithms With TRMM 2A12
AU: * Joyce, R
EM: robert.joyce@noaa.gov
AF: RSIS, 1040 S.W. 13th Drive, Boca Raton, FL 33486, United States
AU: Janowiak, J
EM: john.janowiak@noaa.gov
AF: CPC/NCEP/NOAA, 5200 Auth Road, Camp Springs, MD 20746, United States
AU: Xie, P
EM: pingping.xie@noaa.gov
AF: CPC/NCEP/NOAA, 5200 Auth Road, Camp Springs, MD 20746, United States
AB: A passive microwave (PMW) based blended satellite rainfall estimation technique such as CMORPH will inherently suffer problems when combining and propagating rainfall derived from vastly different frequencies, sampling characteristics, and estimation algorithms resulting in pronounced relative biases. PMW precipitation products are currently derived from AMSR-E, TMI, SSMI & SSMIS, and AMSU-B instruments aboard the AQUA, TRMM, DSMP, and POES satellites respectively. Inter-calibration is crucial because precipitation products that are generated from instruments with different spectral characteristics will not provide the same value for the same scene. However, first rainfall detection frequency, rain rate distribution, and total rainfall from each instrument type must be inter-compared and quantified in order to implement calibration procedures. Certain sensor/algorithms have well known problems such as scan angle dependencies and oceanic rainfall detection deficiencies in the current NESDIS algorithm for the AMSU-B cross-tracking sensor. Despite these problems, the skill of the AMSU-B rainfall estimates is better than IR derived precipitation skill. Furthermore, because the AMSU-B instrument is deployed on four polar orbiting satellites with equatorial crossing times of approximately 1900, 1430, 1330, and 2230, the instrument reasonably samples the diurnal cycle with a broad 2300 km swath. Even algorithms for the same sensor will have significant differences such as the GPROF and EDRR rainfall algorithms for the DMSP SSMI. Rainfall from these various PMW sensor/algorithms and as well as an IR algorithm are inter-compared with temporally and spatially coincident TRMM TMI 2A12 rainfall over various regions, latitudes, seasons, and surface types.
DE: 1640 Remote sensing (1855)
DE: 1840 Hydrometeorology
DE: 3354 Precipitation (1854)
SC: Hydrology [H]
MN: 2007 Joint Assembly