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Rainfall Algorithms Using Oceanic Satellite Observations from MWHS-2
Advances in Atmospheric Sciences ( IF 6.5 ) Pub Date : 2021-03-24 , DOI: 10.1007/s00376-020-0258-5
Ruiyao Chen , Ralf Bennartz

This paper describes three algorithms for retrieving precipitation over oceans from brightness temperatures (TBs) of the Micro-Wave Humidity Sounder-2 (MHWS-2) onboard Fengyun-3C (FY-3C). For algorithm development, scattering-induced TB depressions (ΔTBs) of MWHS-2 at channels between 89 and 190 GHz were collocated to rain rates derived from measurements of the Global Precipitation Measurement’s Dual-frequency Precipitation Radar (DPR) for the year 2017. ΔTBs were calculated by subtracting simulated cloud-free TBs from bias-corrected observed TBs for each channel. These ΔTBs were then related to rain rates from DPR using (1) multilinear regression (MLR); the other two algorithms, (2) range searches (RS) and (3) nearest neighbor searches (NNS), are based on k-dimensional trees. While all three algorithms produce instantaneous rain rates, the RS algorithm also provides the probability of precipitation and can be understood in a Bayesian framework. Different combinations of MWHS-2 channels were evaluated using MLR and results suggest that adding 118 GHz improves retrieval performance. The optimal combination of channels excludes high-peaking channels but includes 118 GHz channels peaking in the mid and high troposphere. MWHS-2 observations from another year were used for validation purposes. The annual mean 2.5° × 2.5° gridded rain rates from the three algorithms are consistent with those from the Global Precipitation Climatology Project (GPCP) and DPR. Their correlation coefficients with GPCP are 0.96 and their biases are less than 5%. The correlation coefficients with DPR are slightly lower and the maximum bias is ∼8%, partly due to the lower sampling density of DPR compared to that of MWHS-2.



中文翻译:

利用来自MWHS-2的大洋卫星观测的降雨算法

本文介绍了三种从风云3C(FY-3C)上的微波湿度探测器2(MHWS-2)的亮度温度(TBs)中检索海洋降水的算法。为了进行算法开发,在89和190 GHz之间的信道上,MWHS-2的散射引起的TB凹陷(ΔTBs)与根据2017年全球降水测量的双频降水雷达(DPR)的测量结果得出的降雨率并置。ΔTBs通过从每个通道的偏差校正观测到的TB中减去模拟的无云TB,可以计算出TB。然后,使用(1)多元线性回归(MLR)将这些ΔTB与DPR的降雨率相关;其他两种算法(2)范围搜索(RS)和(3)最近邻搜索(NNS)基于k维树。虽然这三种算法都会产生瞬时降雨率,RS算法还提供了降水的概率,并且可以在贝叶斯框架中理解。使用MLR对MWHS-2信道的不同组合进行了评估,结果表明增加118 GHz可以提高检索性能。信道的最佳组合不包括高峰值信道,但包括在中高对流层达到峰值的118 GHz信道。来自另一年的MWHS-2观测值用于验证目的。三种算法的年平均2.5°×2.5°格网降雨率与全球降水气候学项目(GPCP)和DPR的一致。它们与GPCP的相关系数为0.96,偏差小于5%。与DPR的相关系数略低,最大偏差约为8%,

更新日期:2021-03-24
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