[3R4-OS-27] OS-27

[3R4-OS-27-02] Improving the post-processing for weather forecasts using machine learning

〇Tomoyuki Takenawa¹, Kazuma Iwase¹ (1. Tokyo University of Marine Science and Technology)

Keywords:Weather Prediction, Post-processing, Machine Learning

To improve the accuracy of weather forecasts, we developed and enhanced post-processing models based on machine learning for numerical calculation results and weather forecast services. For the numerical calculation results, post-processing was applied to the output of the Japan Meteorological Agency's mesoscale model (MSM) for 18 locations across Japan, including plains, mountainous regions, and islands, focusing on precipitation, temperature, and wind speed. Meteorological variables obtained from grid points around the forecast locations were used as input features, and feature selection based on correlation analysis was applied. As a result, the model using LightGBM outperformed neural networks, CNN models, and the MSM post-processing model, MSMG (MSM Guidance), in terms of prediction accuracy. Additionally, for the weather forecast services, we focused on correcting forecasts in mountainous areas where prediction errors are particularly large, and the model using LightGBM achieved higher accuracy than the forecast services.