Abstract

Different methods for the statistical correction of the forecasts of surface parameters using the COSMO-Ru13-ENA model with the lead time up to 117 hours are considered. The methods include the systematic correction using the data from recent observations at a weather station, the correction based on special neural networks as well as different combinations of these two techniques. The study presents the estimates of the results of applying the analyzed correction methods to the forecasts of surface air temperature, dew point, and wind speed modulus based on the independent sample for 2018 with the total volume of 2.34 × 10⁷ forecasts. The correction method based on neural networks reduces forecast errors even at the points where meteorological observations have not been carried out.

中文翻译：

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基于神经网络的COSMO模型天气预报的统计校正

摘要

考虑了使用COSMO-Ru13-ENA模型进行表面参数预测的统计校正的不同方法，交货时间长达117小时。这些方法包括使用气象站最近观测的数据进行系统校正，基于特殊神经网络的校正以及这两种技术的不同组合。该研究基于对2018年的独立样本进行的分析，提出了将分析的校正方法应用于地表气温，露点和风速模量的预测结果的估计，其总量为2.34×10 ^7个预测。基于神经网络的校正方法可以减少预报误差，即使在尚未进行气象观测的地方也是如此。