The aim of the present work is to improve the current state of rainfall estimation by proposing a procedure based entirely on the observations made by the US Advanced Microwave Sounding Unit/B (AMSU/B) on board the National Oceanic and Atmospheric Administration (NOAA) satellites. The procedure does not require the integration of additional sensors which could deteriorate the overall spatial and temporal resolution. It exploits both the radiometric observations made at 89 and 150 GHz (window channels) and the ones made in the 183 GHz water vapor band (opaque channels). In particular, the differences between the measurements of some of the AMSU/B channels are analyzed through radiative transfer simulations for the estimation of precipitation over both land and water surfaces and are related to rain rate values in different atmospheric scenarios. The algorithm estimates exhibit a very good agreement with ground-based (both radar and rain gauge) observations in the detection of rainfall and a reasonably good estimation of rain rate values. The probability of detection of precipitation is 75% and 90% for rain rates greater than 1 mm/h and 5 mm/h, respectively. Furthermore, due to the use of both window and opaque channels, the proposed procedure is able to retrieve light rain and not to misidentify snow-covered surfaces as rain.

Rainfall estimation from satellite passive microwave observations in the range 89 GHz to 190 GHz

CUOMO, Vincenzo
2009

Abstract

The aim of the present work is to improve the current state of rainfall estimation by proposing a procedure based entirely on the observations made by the US Advanced Microwave Sounding Unit/B (AMSU/B) on board the National Oceanic and Atmospheric Administration (NOAA) satellites. The procedure does not require the integration of additional sensors which could deteriorate the overall spatial and temporal resolution. It exploits both the radiometric observations made at 89 and 150 GHz (window channels) and the ones made in the 183 GHz water vapor band (opaque channels). In particular, the differences between the measurements of some of the AMSU/B channels are analyzed through radiative transfer simulations for the estimation of precipitation over both land and water surfaces and are related to rain rate values in different atmospheric scenarios. The algorithm estimates exhibit a very good agreement with ground-based (both radar and rain gauge) observations in the detection of rainfall and a reasonably good estimation of rain rate values. The probability of detection of precipitation is 75% and 90% for rain rates greater than 1 mm/h and 5 mm/h, respectively. Furthermore, due to the use of both window and opaque channels, the proposed procedure is able to retrieve light rain and not to misidentify snow-covered surfaces as rain.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11563/4409
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