基于机器学习的CMA-MESO模式气温预报订正方法研究

doi:10.3969/j.issn.1673-503X.2025.03.003

Abstract

Abstract: To improve the accuracy of temperature forecasts in Xiong'an New Area and the upstream Baoding region,this study utilizes forecast products from the CMA-MESO mesoscale weather model and surface observation data.Three machine learning methods-Linear Regression,Long Short-Term Memory Fully Convolutional Network(LSTM-FCN),and Light Gradient Boosting Machine(LightGBM) are applied.Four forecast correction schemes are designed,focusing on station classification and feature selection.The results show that models using regionally divided stations outperform those using all stations collectively,and LightGBM delivers the best performance among all schemes.Specifically,when composite feature factors are constructed by combining observed data from 48 hours prior to the forecast start time and forecast or observed variables from 4·k hours before the forecast time(within the 0-36 h lead time:for lead times 0-12 h,actual observations from the 0-12 h period before the forecast time are used,with k ranging from 0-12; for lead times 13-36 h,forecast data from 12 h before the forecast time are used,with k fixed at 12),the predictive performance of LightGBM is further improved.For all 37 forecast lead times,the accuracy is improved over the original CMA-MESO model forecasts.Particularly in plateau regions with elevations above 1000 meters,the RMSE improvement exceeds 30%.Moreover,these methods continue to demonstrate strong adaptability under transitional weather conditions.In terms of overall forecasting performance,LightGBM proves to be the best,achieving a root mean square error(RMSE)of 1.86 ℃,a mean absolute error(MAE)of 1.42 ℃,and an accuracy of 75%,representing improvements of 36.5%,38.9%,and 44.4% respectively compared to the CMA-MESO forecast.

Key words: CMA-MESO, Forecast correction, Machine Learning, Temperature forecast

CLC Number:

P457.3

ZHANG Hui, CHEN Junming, WANG Yaqiang, MA Fenglian, ZHOU Yu, LU Yukun, LIU Tong, ZHANG Liangyu. A study on the temperature forecast correction method of the CMA-MESO model based on machine learning[J]. Journal of Meteorology and Environment, 2025, 41(3): 18-28.

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A study on the temperature forecast correction method of the CMA-MESO model based on machine learning

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