A station-data-based model residual machine learning method for fine-grained meteorological grid prediction

• 作者: Chuansai ZHOU Haochen LI Chen YU Jiangjiang XIA Pingwen ZHANG
• 来源期刊: 应用数学和力学（英文版）
• 学科: 数学
• 年，卷（期）: 2022,（2）
• 页码范围: 155-166
• 页数: 12页
• 分类号: O29
• 语种: 英文

摘要

Fine-grained weather forecasting data,i.e.,the grid data with high-resolution,have attracted increasing attention in recent years,especially for some specific applications such as the Winter Olympic Games.Although European Centre for Medium-Range Weather Forecasts (ECMWF) provides grid prediction up to 240 hours,the coarse data are unable to meet high requirements of these major events.In this paper,we propose a method,called model residual machine learning (MRML),to generate grid prediction with high-resolution based on high-precision stations forecasting.MRML applies model output machine learning (MOML) for stations forecasting.Subsequently,MRML utilizes these forecasts to improve the quality of the grid data by fitting a machine learning (ML)model to the residuals.We demonstrate that MRML achieves high capability at diverse meteorological elements,specifically,temperature,relative humidity,and wind speed.In addition,MRML could be easily extended to other post-processing methods by invoking different techniques.In our experiments,MRML outperforms the traditional downscaling methods such as piecewise linear interpolation (PLI) on the testing data.