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首页> 外文期刊>Advances in Geosciences >Statistical and neural classifiers in estimating rain rate from weather radar measurements
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Statistical and neural classifiers in estimating rain rate from weather radar measurements

机译：统计和神经分类器，用于根据天气雷达测量值估算降雨率

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Weather radars are used to measure theelectromagnetic radiation backscattered by cloud raindrops. Clouds thatbackscatter more electromagnetic radiation consist of larger droplets ofrain and therefore they produce more rain. The idea is to estimate rain rateby using weather radar as an alternative to rain-gauges measuring rainfallon the ground. In an experiment during two days in June and August 1997 overthe Italian-Swiss Alps, data from weather radar and surrounding rain-gaugeswere collected at the same time. The statistical KNN and the neural SOMclassifiers were implemented for the classification task using the radardata as input and the rain-gauge measurements as output. The proposed systemmanaged to identify matching pattern waveforms and the rainfall rate on theground was estimated based on the radar reflectivities with a satisfactoryerror rate, outperforming the traditional Z/R relationship. It isanticipated that more data, representing a variety of possiblemeteorological conditions, will lead to improved results. The results inthis work show that an estimation of rain rate based on weather radarmeasurements treated with statistical and neural classifiers is possible.
机译：天气雷达用于测量云雨滴向后散射的电磁辐射。向后散射更多电磁辐射的云由较大的雨滴组成，因此会产生更多的雨。这个想法是通过使用天气雷达来代替测量地面降雨的雨量计来估计降雨率。在1997年6月和1997年8月的两天中，对意大利-瑞士阿尔卑斯山进行的一次实验中，同时收集了来自天气雷达和周围雨量计的数据。统计KNN和神经SOM分类器用于分类任务，使用雷达数据作为输入，雨量计测量作为输出。拟议的系统设法识别出匹配的模式波形，并基于具有令人满意的误差率的雷达反射率估算了地面上的降雨率，该系统优于传统的 Z / R 关系。可以预料到，代表各种可能的气象条件的更多数据将带来更好的结果。这项工作的结果表明，基于经统计和神经分类器处理的天气雷达测量，估计降雨率是可能的。

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