Abstract
We have developed a new Bayesian approach to retrieve oceanic rain rate from 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 ~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.
Highlights
Precipitation measurements are an essential component for understanding the variability and feedback of surfaceatmospheric processes in water and energy cycles (McCabe et al 2008)
We found similar results in another overpass for Typhoon AERE (Fig. 11): two bands with heavy rain rates are seen both in the Precipitation Radar (PR) rain map and in Baye_AVE retrievals, but are less distinct in retrievals of Goddard Profiling Algorithm (GPROF) and MLRS
We developed a new Bayesian approach to retrieve oceanic rain rate (RR) from Tropical Rainfall Measuring Mission (TRMM)/TRMM Microwave Imager (TMI) microwave brightness temperatures, with an emphasis on typhoon cases in the West Pacific
Summary
Precipitation measurements are an essential component for understanding the variability and feedback of surfaceatmospheric processes in water and energy cycles (McCabe et al 2008). Global precipitation is observed from various platforms, including rain gauges, surface radars, and spaceborne visible, infrared, microwave, and radar sensors (Nesbitt et al 2004). Satellite microwave observations are widely used to retrieve surface rainfall because of their ability to penetrate clouds (Adler et al 2001). The Tropical Rainfall Measuring Mission (TRMM) satellite, launched in 1997, has successfully provided passive microwave as well as active radar observations in the tropics. The TRMM Microwave Imager (TMI) measures dual polarizations at frequencies ranging from 10 to 85 GHz, while the Precipitation Radar (PR) is operated at 13.8 GHz (Kummerow et al 1998). Tremendous efforts were made in both development and validation of retrieval methods from TMI and PR data (Benedetti et al 2005)
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