Error quantification and cross calibration of sea surface wind speeds from multiple remote sensing satellites
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摘要: 利用多源卫星散射计和辐射计构建高时间分辨率的海面风遥感数据集是当前海洋遥感研究的热点。本文针对2019年同时期在轨运行的卫星散射计和辐射计，利用浮标数据和欧洲中期天气预报中心（ECMWF）第五代大气再分析数据（ERA5），定量评估了不同传感器获取的海面风速数据的误差特性和标定系数，阐明不同卫星遥感海面风单位误差相对大小，为多源卫星海面风场融合、同化等定量应用提供技术支撑。与常用的中性参考风相比，微波散射计和辐射计反演的风速更适合用等效应力风解释，以便实现卫星遥感数据的优化应用。现有微波散射计和辐射计遥感的海面风速与浮标和ERA5等效应力风在总体上具有良好的一致性，但在高风速条件下（风速大于20 m/s）呈明显的偏差。本文提出的一种用于风速误差横向对比的指示因子，实现了散射计与辐射计风速相对误差估计，为多源数据同化应用中的误差设置提供重要的参考。结果表明：5种散射计风速固有误差介于0.40～0.73之间，5种微波辐射计的风速固有误差介于0.86～1.23。总体而言，在0～20 m/s风速范围内，散射计的风速精度优于辐射计。Abstract: In the field of ocean remote sensing research, the construction of a high temporal resolution sea surface wind (SSW) dataset through the use of multi-source satellite data has become a popular topic. This article aims to quantitatively evaluate the error characteristics and calibration coefficients of SSW speed data obtained from different sensors using buoy data and ERA5 for the satellites scatterometer and radiometer in orbit simultaneously in 2019. The unit error of various satellite remote sensing SSW is comparatively analyzed, providing technical support for quantitative applications such as multi-source satellite SSW blending and assimilation. Furthermore, compared with equal neutral wind speeds, the wind speeds inferred by microwave scatterometer and radiometer are better suited to be interpreted by stress-equivalent winds, which enables the optimal application of satellite remote sensing data. The SSW speeds remotely sensed by existing scatterometers and radiometers are generally consistent with the buoy and ERA5 stress-equivalent wind, but exhibit some deviations at high wind speeds (wind speed greater than 20 m/s). An indicator factor for lateral comparison of wind speed errors is proposed to achieve relative error estimation of scatterometer and radiometer wind speeds, which provides an important reference for error setting in multi-source data assimilation applications. The results demonstrate that the inherent wind speed errors of the five scatterometers range from 0.40 to 0.73, and the inherent wind speed errors of the five microwave radiometers range from 0.86 to 1.23. In conclusion, it can be observed that the scatterometers generally exhibit better accuracy in SSW speed estimation than radiometers in the wind speed range of 0 m/s to 20 m/s.
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Key words:
- sea surface wind /
- scatterometer /
- radiometer /
- stress-equivalent wind /
- error estimation
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图 1 卫星遥感风速与ERA5 u10n和u10s的差值平均随纬度的变化
Fig. 1 Latitude dependence of the difference between satellite wind products and ERA5 wind speed
表 1 星载散射计发射现状及主要技术参数
Tab. 1 Present situation and main technical parameters of scatterometers emission
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表 2 星载辐射计发射现状及主要技术参数
Tab. 2 Present situation and main technical parameters of radiometer emission
卫星 传感器 在轨时间 空间分辨率/km 频率/GHz 风产品类型 Coriolis WindSAT 2003年1月 8～71 6.8、10.7、18.7、23.8、37.0 10 m风速 GCOM-W1 AMSR-2 2012年5月 6～75 6.925、10.65、18.7、23.8、36.5、89 10 m风速 SSMIS-F16 SSMIS 2003年10月 约50 19.35、22.4、37、91.66 10 m风速 SSMIS-F17 SSMIS 2006年12月 约50 19.35、22.4、37、91.66 SSMIS-F18 SSMIS 2009年10月 约50 19.35、22.4、37、91.66
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表 3 卫星传感器风速的偏差趋势和校准系数
Tab. 3 Deviation trend and calibration coefficient of satellite sensor wind speed
传感器类型 序号 系统 偏差趋势 偏差校准系数 散射计 1 CFOSCAT 0.97 −0.37 2 HY-2B 0.99 −0.08 3 ASCAT-A 0.95 −0.35 4 ASCAT-B 0.97 −0.20 5 OSCAT-2 0.97 −0.34 辐射计 1 SSMIS-16 0.96 −0.64 2 SSMIS-17 0.94 −0.20 3 SSMIS-18 0.96 −0.44 4 AMSR-2 0.94 −0.64 5 WindSAT 0.95 −0.19
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表 4 不同风数据的平均随机误差标准差
Tab. 4 The average random error standard deviation of different sources
传感器类型 平均随机误差标准差/(m·s−1) 浮标 0.98 散射计 0.47 辐射计 0.78 ERA5 0.76
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表 5 卫星遥感海面风速固有误差
Tab. 5 Satellite remote sensing sea surface wind speed inherent errors
传感器类型 序号 系统 固有误差 散射计 1 HSCAT 0.732 2 CSCAT 0.712 3 ASCAT-A 0.464 4 ASCAT-B 0.403 5 OSCAT-2 0.731 辐射计 1 SSMIS-16 1.225 2 SSMIS-17 1.216 3 SSMIS-18 1.107 4 AMSR-2 0.859 5 WindSAT 0.856
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