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

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.