Abstract

The paper presents the application of the artificial neural network algorithm for the retrieval of total precipitable water in the atmosphere over water and land from the measurements of MTVZA-GYa microwave radiometer on board the Meteor-M No. 2-2 satellite. Satellite-based estimates of total precipitable water were compared with radiosonde and AERONET data, as well as with the ECMWF numerical weather prediction model output. According to the comparison, the root-mean-square error (RMSE) does not exceed 4.5 mm for radiosonde data and is less than 4 mm for the ECMWF and AERONET data. The best accuracy is provided over water with the RMSE not exceeding 3 mm. The total precipitable water estimates retrieved from MTVZA-GYa and NOAA-20/ATMS radiometer data are consistent over water, while the MTVZA-GYa based estimates are more accurate over land.

中文翻译：

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使用神经网络算法从 Meteor-M No. 2-2 MTVZA-GYa 数据中检索总可降水量

摘要

本文介绍了人工神经网络算法在 Meteor-M 2-2 号卫星上 MTVZA-GYa 微波辐射计测量值反演水陆上大气中可降水总量的应用。基于卫星的可降水总量估计值与无线电探空仪和 AERONET 数据以及 ECMWF 数值天气预报模型输出进行了比较。根据比较，无线电探空仪数据的均方根误差 (RMSE) 不超过 4.5 毫米，而 ECMWF 和 AERONET 数据的均方根误差 (RMSE) 小于 4 毫米。在 RMSE 不超过 3 mm 的水上提供最佳精度。从 MTVZA-GYa 和 NOAA-20/ATMS 辐射计数据检索到的总可降水量估算在水上是一致的，而基于 MTVZA-GYa 的估算在陆地上更准确。