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This study attempts to exploit ‘Satellite with Advanced Research and Global Observation Satellite (Argos) and Ka-band Altimeter (ALtiKa)’ (SARAL/AltiKa) and Passive Microwave Radiometer (PMR) measurements for the retrieval of precipitation and rain rate (RRain) over the global oceans. The altimetric measurements are affected by the presence of rain and these measurements are thus required to be identified. In this research, the concurrent SARAL and Tropical Rainfall Measuring Mission – Precipitation Radar (TRMM-PR) measurements are used to show the rain sensitivity of some of the altimeter derived parameters and PMR-derived Brightness Temperature(TB). Based on the probability distribution of rain-sensitive parameters, a probabilistic rain identification algorithm is proposed, which is optimized to reduce the false-alarm cases for rain estimation. In the second step, a Genetic Algorithm (GA)-based rain measurement algorithm is developed. The algorithm is applied to a full year (2016) of independent SARAL measurements, compared with collocated Global Precipitation Measurement (GPM) Mission Microwave Imager (GMI) and Special Sensor Microwave Imager Sounder (SSMIS) measurements. Within TRMM latitudinal coverage area (i.e. < ± 40° latitudes), the comparison with GMI and SSMIS shows correlation coefficient (r) of 0.78 and 0.76 and root mean square difference (RMSD) of 0.48 and 0.43 (mm h−1), respectively. The comparison, beyond latitudinal coverage of TRMM-PR (i.e. outside ± 40° latitudes), with GMI and SSMIS, shows r of 0.63 and 0.60 and RMSD of 0.59 and 0.55 (mm h−1), respectively. The algorithm is further applied to measure rain associated with a tropical cyclone. These rain measurements are then compared with near-concurrent rain measurements from SSMIS, and comparison results are presented in the paper.
Varma et al. (Tue,) studied this question.