%0 Journal Article
%T Study of Rainfall from TRMM Microwave Imager Observation over India
%A Anoop Kumar Mishra
%A Rajesh Kumar
%J ISRN Geophysics
%D 2012
%R 10.5402/2012/921824
%X This paper presents a technique to estimate precipitation over Indian land (6¨C36¡ãN, 65¨C99¡ãE) at spatial grid using tropical rainfall measuring mission (TRMM) microwave imager (TMI) observations. It adopts the methodology recently developed by Mishra (2012) to monitor the rainfall over the land portion. Regional scattering index (SI) developed for Indian region and polarization corrected temperature (PCT) have been utilized in this study. These proxy rain variables (i.e., PCT and SI) are matched with rainfall from precipitation radar (PR) to relate rain rate with PCT, SI, and their combination. Retrieval techniques have been developed using nonlinear relationship between rain and proxy variables. The results have been compared with the observations (independent of training data set) from PR. Results have also been validated with the observations from automatic weather station (AWS) rain gauges. It is observed from the validation results that nonlinear algorithm using single variable SI underestimates the low rainfall rates (below 20£¿mm/h) but overestimates the high rain rates (above 20£¿mm/h). On the other hand, algorithm using PCT overestimates the high rain rates (above 25£¿mm/h). Validation results with rain gauges show a CC of 0.68 and RMSE of 4.76£¿mm when both SI and PCT are used. 1. Introduction High rainfall events in India have increased by 50% during past 50 years [1]. Hence an accurate prediction of high rainfall event is essential for prevention and management of disasters. In India, there is a low spatial density of automatic rain gauges and Doppler Weather Radars (DWRs). So satellite-based rainfall estimates are highly valuable for synoptic situations. Rainfall estimations based on infrared (IR) measurements from satellite have large errors because IR radiances from cloud tops have only indirect and weak relationship with surface rainfall [2, 3]. On the other hand, satellite microwave measurements provide more direct estimates of rainfall. Microwave algorithms are generally classified as statistical or physical [4]. Statistical algorithms (e.g., [5]) use observed data to derive an empirical relationship between brightness temperature and precipitation. Physical algorithm (e.g., [6¨C9]) uses a data base of radiative transfer calculations based on atmospheric profiles, which are compared with an observed set of brightness temperature. The higher the number of channels used, the greater the chances of finding an accurate hydrometeor profile in data base. Precipitation estimation algorithms from microwaves using radiative transfer models have been
%U http://www.hindawi.com/journals/isrn.geophysics/2012/921824/