P1A.15 On the use of microwave Sounder data for high-temporal rainfall maps based on microwave radiometers

Tuesday, 29 April 2008
Palms ABCD (Wyndham Orlando Resort)
Shoichi Shige, Kyoto University, Kyoto, Kyoto, Japan; and T. Yamamoto, T. Tsukiyama, S. Kida, T. Kubota, and K. Okamoto

The Global Satellite Mapping of Precipitation (GSMaP) microwave radiometer (MWR) algorithm has been developed based on the study in Aonashi and Liu (2000), using various attributes of precipitation derived from Precipitation Radar (PR) on board the Tropical Rainfall Measuring Mission (TRMM) satellite (Kubota et al., 2007). The GSMaP MWR algorithm has been applied for the passive microwave radiometers currently in orbits such as TRMM Microwave Imager (TMI), the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) aboard the NASA Aqua satellite and the Special Sensor Microwave/Imager (SSM/I) aboard the DMSP satellites. Despite the improved rainfall estimates from the passive microwave radiometers, the challenge remains to further close the information gap through more frequent satellites.

The passive microwave radiometers are generally of two types: imagers and sounders. Imagers such as TMI, AMSR-E and SSM/I have channels in the window regions of the spectrum to monitor precipitation. Sounders such as the advanced microwave sounding unit (AMSU) on board the NOAA satellites are primarily developed for profiling atmospheric temperature and moisture with opaque spectral regions. However, rainfall products can be also derived using its window channels (e.g. Ferraro et al., 2005). Because there have been four AMSU instruments in orbits since the launch of NOAA18 in May 2005, they offer more observations of rainfall in time and space together with four microwave imagers (TMI, AMSR-E, and SSM/Is).

In this study, we have been developing rainfall retrievals for the AMSU based on the GSMaP-MWR algorithm, taking the differences between imagers and sounders into account. In principle, rainfall retrievals from TMI are superior to those from AMSU. Thus, a comparison of AMSU estimates against TRMM estimates is very useful for development and validation of AMSU rainfall retrievals. We have applied the preliminary GSMaP-AMSU algorithm to the matched AMSU/TMI data and compared the GSMaP-AMSU estimates with the NOAA standard algorithm retrieved data using the GSMaP-TMI data as the reference.

- Indicates paper has been withdrawn from meeting
- Indicates an Award Winner