Abstract
Rain affects the wind measurement accuracy of the Ku-band spaceborne scatterometer. In order to improve the quality of the retrieved wind field, it is necessary to identify and flag rain-contaminated data. In this study, an HY-2A scatterometer is used to study rain identification. In addition to the conventional parameters, such as the retrieved wind speed, the wind direction relative to the along-track direction, and the normalized beam difference, the experiment expands the mean deviation of the backscattering coefficient, the beam difference between fore and aft, and the node number of the wind vector cell (WVC) as the sensitive parameters according to the microwave scattering characteristics of rain and the actual measurement situation of the HY-2A. Furthermore, a rain identification model for HY2 (HY2RRM) with the K-Nearest Neighborhood (KNN) algorithm was built. After several tests, the accuracy of the selected HY2RRM approach is found to about 88%, and about 70% of rain-contaminated data can be accurately identified. The research results are helpful for better understanding the characteristics of microwave backscattering and provide a possible way to further improve the wind field retrieval accuracy of the HY-2A scatterometer and other Ku-band scatterometers.
Highlights
IntroductionThe acquisition of sea surface wind field data at a high spatial and temporal resolution has very important scientific implications and practical significance
In order to compensate for the contribution of missing parameters, this study further analyzed the scattering characteristics of raindrops to microwaves and the measurement characteristics of the HY-2A scatterometer, and we found that the node number of wind vector cell (WVC), the mean deviation of the backscattering coefficient, and the backscatter difference between the fore and after beam were helpful to identify rain-contaminated data
The accuracy of the most tightened HY2RRM was 88.16%, the accuracy of rain identification was 69.61%, the rejection rate for data was 12.1%, and the retrieval accuracy increases for the wind speed and direction were 11.22% and 4.04%, respectively
Summary
The acquisition of sea surface wind field data at a high spatial and temporal resolution has very important scientific implications and practical significance. The advent of spaceborne scatterometers has provided a technology for obtaining real-time data of sea surface wind fields with high precision and spatio-temporal resolution [1]. Drops of rainfall attenuate and scatter the microwave signal, and the splashing of raindrops changes the roughness of the sea surface, which increases the variance of scatterometer measurements [3,4]. If the influence of rain is not considered in wind field retrieval, the contribution of raindrops will be interpreted as the characteristics of wind, reducing the quality of wind field retrieval.
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