Application of Machine-Learning-Based Objective Correction Method in the Intelligent Grid Maximum and Minimum Temperature Predictions  

作　　者：Jing Liu Chuan Ren Ningle Yuan Shuai Zhang Yue Wang Jing Liu;Chuan Ren;Ningle Yuan;Shuai Zhang;Yue Wang(Key Opening Laboratory for Northeast China Cold Vortex Research, The Institute of Atmospheric Environment, China Meteorological Administration, Shenyang, China;Shenyang Meteorological Observatory, Shenyang, China;Liaoning Meteorological Warning Center, Shenyang, China;Liaoning Meteorological Information Center, Shenyang, China)

机构地区：[1]Key Opening Laboratory for Northeast China Cold Vortex Research, The Institute of Atmospheric Environment, China Meteorological Administration, Shenyang, China [2]Shenyang Meteorological Observatory, Shenyang, China [3]Liaoning Meteorological Warning Center, Shenyang, China [4]Liaoning Meteorological Information Center, Shenyang, China

出　　处：《Atmospheric and Climate Sciences》2023年第4期507-525,共19页大气和气候科学（英文）

摘　　要：Post-processing correction is an effective way to improve the model forecasting result. Especially, the machine learning methods have played increasingly important roles in recent years. Taking the meteorological observational data in a period of two years as the reference, the maximum and minimum temperature predictions of Shenyang station from the European Center for Medium-Range Weather Forecasts (ECMWF) and national intelligent grid forecasts are objectively corrected by using wavelet analysis, sliding training and other technologies. The evaluation results show that the sliding training time window of the maximum temperature is smaller than that of the minimum temperature, and their difference is the largest in August, with a difference of 2.6 days. The objective correction product of maximum temperature shows a good performance in spring, while that of minimum temperature performs well throughout the whole year, with an accuracy improvement of 97% to 186%. The correction effect in the central plains is better than in the regions with complex terrain. As for the national intelligent grid forecasts, the objective correction products have shown positive skills in predicting the maximum temperatures in spring (the skill-score reaches 0.59) and in predicting the minimum temperature at most times of the year (the skill-score reaches 0.68).Post-processing correction is an effective way to improve the model forecasting result. Especially, the machine learning methods have played increasingly important roles in recent years. Taking the meteorological observational data in a period of two years as the reference, the maximum and minimum temperature predictions of Shenyang station from the European Center for Medium-Range Weather Forecasts (ECMWF) and national intelligent grid forecasts are objectively corrected by using wavelet analysis, sliding training and other technologies. The evaluation results show that the sliding training time window of the maximum temperature is smaller than that of the minimum temperature, and their difference is the largest in August, with a difference of 2.6 days. The objective correction product of maximum temperature shows a good performance in spring, while that of minimum temperature performs well throughout the whole year, with an accuracy improvement of 97% to 186%. The correction effect in the central plains is better than in the regions with complex terrain. As for the national intelligent grid forecasts, the objective correction products have shown positive skills in predicting the maximum temperatures in spring (the skill-score reaches 0.59) and in predicting the minimum temperature at most times of the year (the skill-score reaches 0.68).

关 键 词：Machine Learning Sliding Training Forecast Correction Maximum and Minimum Temperature 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]