| 基于机器学习的复杂地形下短期数值天气预报误差分析与订正 |
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| 引用本文： | 任萍,陈明轩,曹伟华,王在文,韩雷,宋林烨,杨璐.基于机器学习的复杂地形下短期数值天气预报误差分析与订正[J].气象学报,2020,78(6):1002-1020. |
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| 作者姓名： | 任萍 陈明轩 曹伟华 王在文 韩雷 宋林烨 杨璐 |
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| 作者单位： | 1.中国海洋大学，青岛，266100 |
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| 基金项目： | 国家重点研发计划项目（2018YFF0300102）、北京市自然科学基金项目（8192016）、国家自然科学基金项目（41875049、41801022） |
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| 摘 要： | 初步研发了一套基于机器学习方法XGBoost且考虑地形特征影响的数值预报多模式集成技术，并与传统的等权重平均和线性回归方法的集成效果进行了对比分析。利用北京地区快速更新循环数值预报系统每天8次循环预报给出的近地面2 m温度、2 m相对湿度、10 m风速、10 m风向数据产品，分别基于机器学习方法XGBoost、等权重平均方法、线性回归方法构建了3种体现地形因子影响的多模式预报时间滞后集成模型。试验对比分析了暖季、冷季每日不同时刻的模式预报集成订正效果。结果表明:分季节试验中，基于XGBoost模型对2 m温度、10 m风速的集成预报结果相对原始最优预报结果误差明显优于其他两种传统方法。XGBoost对2 m温度集成的误差可降低11.02%—18.09%，10 m风速集成误差可降低31.23%—33.22%，10 m风向集成误差可降低4.1%—8.23%。2 m相对湿度的集成预报误差与传统方法接近。基于XGBoost的多模式集成预报模型可以充分“挖掘”不同模式或不同时刻快速更新循环预报优点，有效降低模式的系统性误差，提供准确性更高的多模式集成确定性预报产品。
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| 关 键 词： | 集成 数值预报 机器学习 XGBoost 线性回归 等权重 |
| 收稿时间： | 2020/1/21 0:00:00 |
| 修稿时间： | 2020/6/30 0:00:00 |
Error analysis and correction of short-term numerical weather prediction under complex terrain based on machine learning |
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| REN Ping,CHEN Mingxuan,CAO Weihu,WANG Zaiwen,HAN Lei,SONG Linye,YANG Lu.Error analysis and correction of short-term numerical weather prediction under complex terrain based on machine learning[J].Acta Meteorologica Sinica,2020,78(6):1002-1020. |
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| Authors: | REN Ping CHEN Mingxuan CAO Weihu WANG Zaiwen HAN Lei SONG Linye YANG Lu |
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| Institution: | 1.Ocean University of China，Qingdao 266100，China2.Institute of Urban Meteorology，CMA，Beijing 100089，China |
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| Abstract: | A set of multi-mode integration technology of numerical prediction based on machine learning method XGBoost and consideration of the influence of topographical features has been preliminarily developed. Its integration effect was compared with that of traditional equal weight average and linear regression methods. Based on the data products of the rapid update cycle numerical prediction system in Beijing, which can provide cycle predictions including 2 m air temperature, 2 m relative humidity, 10 m wind speed and 10 m wind direction near the ground 8 times a day, three integrated models of multi-model forecast time lag integrated models were construct based on the machine learning method XGBoost, the equal weight average method and the linear regression method, respectively. Experiments were conducted to compare and analyze the effect of the integrated correction of model predictions at different times in a warm and a cold season every day. The results indicate that in the seasonal test, the integrated prediction results of 2 m air temperature and 10 m full wind speed based on the XGBoost model are significantly improved compared with the original optimal prediction results, and are much better than the results of the other two traditional methods. Using the XGBoost method, the error of 2 m air temperature integration can be reduced by 11.02%—18.09%, the error of 10 m full wind speed integration can be reduced by 31.23%—33.22%, and the error of 10 m wind direction integration can be reduced by 4.1%—8.23%. The integrated forecast error of 2 m relative humidity is close to the that from the traditional method. As a whole, the developed multi-mode integrated prediction model based on XGBoost can fully "excavate" the advantages of different modes or the rapid updating cycle prediction at different times, and therefore effectively reduces the systematic error of the mode and provides a multi-mode integrated deterministic prediction product with higher accuracy. |
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| Keywords: | Integration Numerical prediction Machine learning XGBoost Linear regression Equal weight |
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