SEMINAR SERIES and COLLOQUIA

Microwave satellite-based rainfall techniques have become the core of many multi-satellite algorithms, so any improvement on these basic MW estimates has a huge impact on many hydrological products (e.g., CMORPH, TMPA, etc.). This seminar is focused on the development of some improvements of basic MW rainfall retrievals and the validation of those proposed improvements.

The first part is devoted to the improvement and evaluation of AMSU precipitation retrievals. The Advanced Microwave Sounding Unit – B (AMSU-B) high frequency channels (89 and 150-GHz) provide the ability to detect the scattering associated with precipitation sized ice particles and, indirectly with precipitation rate. While the main advantage of this approach is the availability of three NOAA POES satellites spaced approximately 4 h apart with a spatial resolution of 16 km at nadir and a wider swath than SSMI (2200 km); the weakness of this technique are related with its present inability to retrieve rain that has little or no ice; and with the cross-scan characteristics of the instrument (different footprints for different local zenithal angles). After applying the proposed correction scheme, a remarkable improvement of rain rate distribution is observed for both surface types (land and ocean) compared with original algorithm.

The second half is related with an application for the extension of hydrological products climate records. Global monthly rainfall estimates and other hydrological products like integrated Cloud Liquid Water (CLW) and Total Precipitable Water (TPW) have been produced from 1987 to present using measurements from the Defense Meteorological Satellite Program (DMSP) series of Special Sensor Microwave Imager (SSM/I). Since the DMSP F16 and F17 satellites was successfully launched carrying onboard the Special Sensor Microwave Imager/Sounder (SSMI/S), the main objective of this research is focused on the application of SSMI/S channels to evaluate the performance of several hydrological products using the heritage of existing algorithms for SSMI.

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