Spaceborne scatterometers for ocean surface winds usually operate in Ku- or C-band. Rather strict quality control (QC) procedures are included in the Ku-band wind retrieval chain for labeling rain-contaminated observations. Existing QC factors represent the deviation of measurements from the wind geophysical model function (GMF) modeled measurement surface. Other QC indicators flag outliers by examining neighborhood consistency. In this article, spatial heterogeneity of rain is further exploited by a new indicator for Ku-band QC, namely, ${J}_{{OSS}}$ , the speed component of the observation cost function, ${J}_{{O}}$ , of the selected solution ( ${J}_{{OS}}$ ) in the 2-D variational ambiguity removal (2-DVAR) step of the wind retrieval. First, the characteristics of 2-DVAR speeds in rainy condition are analyzed, and then, the ability of ${J}_{{OSS}}$ in quality labeling is proposed and verified by applying it to the Ku-band scatterometer on-board ScatSat. Its effectiveness for rain screening is confirmed with collocated references from the C-band scatterometer on-board the MetOp-B satellite, which are much less affected by rain. With reference to collocated rain rates from the Global Precipitation Mission (GPM), the more direct relations to rain and wind speed errors of the newly proposed QC indicator ${J}_{{OSS}}$ than existing QC indicators, including ${J}_{{OS}}$ , are illustrated by the analysis of its correlation with rain rates. In a novel approach, ${J}_{{OSS}}$ is applied to accept (unflag) more than 75% of the data rejected by the widely applied maximum likelihood estimation (MLE) thresholds (i.e., correct false alarms) in the tropics. The promising results open a new opportunity for improving QC of rain in the Ku-band wind scatterometry benefitting scatterometer applications.

中文翻译：

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改进的 Ku 波段风散射测量的雨筛选

海面风的星载散射计通常在 Ku 或 C 波段运行。相当严格的质量控制 (QC) 程序包含在 Ku 波段风反演链中，用于标记雨水污染的观测。现有的 QC 因素表示测量值与风地球物理模型函数 (GMF) 建模测量表面的偏差。其他 QC 指标通过检查邻域一致性来标记异常值。在本文中，Ku 波段 QC 的一个新指标进一步利用了降雨的空间异质性，即， ${J}_{{OSS}}$ ，观察成本函数的速度分量， ${J}_{{O}}$ , 选定的解决方案 ( ${J}_{{OS}}$ ) 在风反演的二维变分模糊度去除 (2-DVAR) 步骤中。首先分析了2-DVAR速度在雨天条件下的特性，然后， ${J}_{{OSS}}$ 通过将其应用于机载 ScatSat 的 Ku 波段散射计，提出并验证了质量标签。MetOp-B 卫星上的 C 波段散射计的配置参考证实了它对雨水筛查的有效性，这些散射计受雨水的影响要小得多。参考来自全球降水任务 (GPM) 的搭配降雨率，新提出的 QC 指标与降雨和风速误差的更直接关系 ${J}_{{OSS}}$ 比现有的QC指标，包括 ${J}_{{OS}}$ ，通过分析其与降雨率的相关性来说明。以一种新颖的方式， ${J}_{{OSS}}$ 用于接受（取消标记）被热带地区广泛应用的最大似然估计（MLE）阈值（即纠正误报）拒绝的超过 75% 的数据。有希望的结果为改进 Ku 波段风散射测量中的雨水质量控制开辟了新的机会，有利于散射计应用。