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首页> 外文期刊>Geoscience and Remote Sensing, IEEE Transactions on >A Neural Network Technique for Improving the Accuracy of Scatterometer Winds in Rainy Conditions
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A Neural Network Technique for Improving the Accuracy of Scatterometer Winds in Rainy Conditions

机译：一种提高雨天散射仪风精度的神经网络技术

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摘要

We exhibit a technique for improving wind accuracy in Ku-band ocean wind scatterometers in the presence of rain. The technique is autonomous in that it only makes use of measurements made by the scatterometer itself, so that no colocation of an external data set (e.g., rain radiometers) is required to perform the correction. The only inputs to the technique are the normalized radar cross-section measurements for each wind vector cell, the cross-track distance of the cell as a proxy for measurement geometry, and the nominal retrieved wind vector for the cell without rain correction. This last input is used to avoid modifying winds not contaminated by rain. The technique was applied to QuikSCAT data for the month of January 2008, resulting in a marked improvement to rainy data. For data that were determined to be rain contaminated by the Jet Propulsion Laboratory rain flag, the rms speed error with respect to National Data Buoy Center buoy winds improved from 8.9 to 3.5 m/s for colocations within 25 km. The rms speed error in rain also improved when compared with the European Centre Medium-Range Weather Forecast winds from 7 to 3 m/s. Data that were not flagged as rain contaminated were not significantly changed, despite the fact that the technique does not make use of the rain flag. The technique was able to distinguish between rain-contaminated wind cells and rain-free wind cells and to substantially improve the wind speed accuracy of the former using QuikSCAT data alone without recourse to any external information about the extent of the rain.
机译：我们展示了一种在有雨的情况下提高Ku波段海洋风散射仪的风力精度的技术。该技术是自主的，因为其仅利用由散射仪本身进行的测量，因此不需要外部数据集（例如，雨水辐射仪）的共置来执行校正。该技术的唯一输入是每个风矢量像元的归一化雷达横截面测量，作为测量几何形状的代用品的像元的跨轨距离，以及无需降雨校正的标称检索到的风矢量。最后输入用于避免修改不受雨水污染的风。该技术已应用于2008年1月的QuikSCAT数据，从而显着改善了下雨数据。对于确定为被喷气推进实验室雨旗污染的数据，对于25公里以内的同一地点，国家数据浮标中心浮标风的均方根速度误差从8.9改善到3.5 m / s。与7到3 m / s的欧洲中心中程天气预报风相比，雨中的均方根速度误差也得到了改善。尽管该技术未使用雨水标记，但未标记为受雨水污染的数据并未发生重大变化。该技术能够区分受雨水污染的风单元和无雨风单元，并且仅使用QuikSCAT数据就可以显着提高前者的风速精度，而无需借助任何有关降雨范围的外部信息。

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