2026-03-10T04:03:57Z https://ipsj.ixsq.nii.ac.jp/oai

oai:ipsj.ixsq.nii.ac.jp:00240018 2025-01-19T08:06:51Z 581:11492:11503

Interpolation of Mountain Weather Forecasts by Machine Learning Interpolation of Mountain Weather Forecasts by Machine Learning Kazuma, Iwase Tomoyuki, Takenawa Kazuma, Iwase Tomoyuki, Takenawa [一般論文] weather forecast, machine learning Recent advances in numerical simulation methods based on physical models and their combination with machine learning have improved the accuracy of weather forecasts. However, the accuracy decreases in complex terrains such as mountainous regions because these methods usually use grids of several kilometers square and simple machine learning models. While deep learning has also made significant progress in recent years, its direct application is difficult to utilize the physical knowledge used in the simulation. This paper proposes a method that uses machine learning to interpolate future weather in mountainous regions using forecast data from surrounding plains and past observed data to improve weather forecasts in mountainous regions. We focus on mountainous regions in Japan and predict temperature and precipitation mainly using LightGBM as a machine learning model. Despite the use of a small dataset, through feature engineering and model tuning, our method partially achieves improvements in the RMSE with significantly less training time. ------------------------------ This is a preprint of an article intended for publication Journal of Information Processing(JIP). This preprint should not be cited. This article should be cited as: Journal of Information Processing Vol.32(2024) (online) DOI　http://dx.doi.org/10.2197/ipsjjip.32.873 ------------------------------ Recent advances in numerical simulation methods based on physical models and their combination with machine learning have improved the accuracy of weather forecasts. However, the accuracy decreases in complex terrains such as mountainous regions because these methods usually use grids of several kilometers square and simple machine learning models. While deep learning has also made significant progress in recent years, its direct application is difficult to utilize the physical knowledge used in the simulation. This paper proposes a method that uses machine learning to interpolate future weather in mountainous regions using forecast data from surrounding plains and past observed data to improve weather forecasts in mountainous regions. We focus on mountainous regions in Japan and predict temperature and precipitation mainly using LightGBM as a machine learning model. Despite the use of a small dataset, through feature engineering and model tuning, our method partially achieves improvements in the RMSE with significantly less training time. ------------------------------ This is a preprint of an article intended for publication Journal of Information Processing(JIP). This preprint should not be cited. This article should be cited as: Journal of Information Processing Vol.32(2024) (online) DOI　http://dx.doi.org/10.2197/ipsjjip.32.873 ------------------------------ 情報処理学会 2024-10-15 eng journal article https://ipsj.ixsq.nii.ac.jp/records/240018 1882-7764 AN00116647 情報処理学会論文誌 65 10 https://ipsj.ixsq.nii.ac.jp/record/240018/files/IPSJ-JNL6510010.pdf application/pdf 7.3 MB 2026-10-15