Quantitative Precipitation Estimation over Ocean Using Bayesian Approach from Microwave Observations during the Typhoon Season

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Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/27659

Title:	Quantitative Precipitation Estimation over Ocean Using Bayesian Approach from Microwave Observations during the Typhoon Season
Authors:	Hu,JC;Chen,WJ;Chiu,JC;Wang,JL;Liu,GR
Contributors:	太空及遙測研究中心
Keywords:	RESOLVING MODEL SIMULATIONS;RAINFALL MEASURING MISSION;SOUTH CHINA SEA;PART I;SATELLITE-OBSERVATIONS;PROFILING ALGORITHM;GAUGE DATA;TRMM;RETRIEVALS;CLOUD
Date:	2009
Issue Date:	2010-06-29 18:51:37 (UTC+8)
Publisher:	中央大學
Abstract:	We have developed a new Bayesian approach to retrieve oceanic rain rate front the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), with an emphasis on typhoon cases in the West Pacific. Retrieved rain rates are validated with measurements of rain gauges located on Japanese islands. To demonstrate improvement, retrievals are also compared with those from the TRMM/Precipitation Radar (PR), the Goddard Profiling Algorithm (GPROF), and a multi-channel linear regression statistical method (MLRS). We have found that qualitatively, all methods retrieved similar horizontal distributions in terms of locations of eyes and rain bands of typhoons. Quantitatively, our new Bayesian retrievals have the best linearity and the smallest root mean square (RMS) error against rain gauge data for 16 typhoon overpasses in 2004. The correlation coefficient and RMS of our retrievals are 0.95 and similar to 2 mm hr(-1), respectively. In particular, at heavy rain rates, Our Bayesian retrievals outperform those retrieved from GPROF and MLRS. Overall, the new Bayesian approach accurately retrieves surface rain rate for typhoon cases. Accurate rain rate estimates from this method can be assimilated in models to improve forecast and prevent potential damages in Taiwan during typhoon seasons.
Relation:	TERRESTRIAL ATMOSPHERIC AND OCEANIC SCIENCES
Appears in Collections:	[Center for Space and Remote Sensing Research ] journal & Dissertation

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