ABSTRACT 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 (R Rain) 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 . 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.

中文翻译：

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来自 SARAL AltiKa 和无源微波辐射计观测的降水测量

摘要 本研究试图利用“先进研究和全球观测卫星 (Argos) 和 Ka 波段高度计 (ALtiKa)” (SARAL/AltiKa) 和无源微波辐射计 (PMR) 测量来检索降水和降雨率 (R雨）在全球海洋上空。高度测量受到降雨的影响，因此需要识别这些测量。在这项研究中，同时进行的 SARAL 和热带降雨测量任务 - 降水雷达 (TRMM-PR) 测量用于显示某些高度计导出参数和 PMR 导出的亮度温度的降雨敏感性。基于降雨敏感参数的概率分布，提出了概率降雨识别算法，它经过优化以减少降雨估计的误报情况。第二步，开发基于遗传算法（GA）的雨量测量算法。与配置的全球降水测量 (GPM) 任务微波成像仪 (GMI) 和特殊传感器微波成像仪 (SSMIS) 测量相比，该算法应用于全年（2016 年）的独立 SARAL 测量。在 TRMM 纬度覆盖区域内（即 < ± 40° 纬度），与 GMI 和 SSMIS 的比较显示相关系数 (r) 分别为 0.78 和 0.76，均方根差 (RMSD) 分别为 0.48 和 0.43 (mm h−1) . 与 GMI 和 SSMIS 的比较，超出了 TRMM-PR 的纬度覆盖范围（即在 ± 40° 纬度之外），分别显示 r 为 0.63 和 0.60，RMSD 分别为 0.59 和 0.55（mm h-1）。该算法进一步应用于测量与热带气旋相关的降雨。然后将这些降雨测量值与来自 SSMIS 的几乎同时发生的降雨测量值进行比较，比较结果在论文中给出。