| 基于ECMWF产品的站点气温预报集成学习误差订正 |
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| 引用本文： | 陈昱文,黄小猛,李熠,陈悦,徐挚仁,黄兴.基于ECMWF产品的站点气温预报集成学习误差订正[J].应用气象学报,2020,31(4):494-503. |
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| 作者姓名： | 陈昱文 黄小猛 李熠 陈悦 徐挚仁 黄兴 |
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| 作者单位： | 1.清华大学地球系统科学系地球系统数值模拟教育部重点实验室, 北京 100084 |
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| 摘 要： | 提出一种基于数值模式预报产品的气温预报集成学习误差订正方法，通过人工神经网络、长短期记忆网络和线性回归模型组合出新的集成学习模型（ALS模型），采用2013—2017年的欧洲中期天气预报中心数值天气预报模式2 m气温预报产品和中国部分气象站点数据，利用气象站点气温、风速、气压、相对湿度4个观测要素，挖掘观测数据的时序特征并结合模式2 m气温预报结果训练机器学习模型，对2018年模式2 m气温6~168 h格点预报产品插值到站点后的预报结果进行偏差订正。结果表明:ALS模型可将站点气温预报整体均方根误差由3.11℃降至2.50℃，降幅达0.61℃（19.6%），而传统的线性回归模型降幅为0.23℃（8.4%）。ALS模型对站点气温预报误差较大的区域和气温峰值预报的订正效果尤为显著，因此，集成学习方法在数值模式预报结果订正中具有较大的应用潜力。
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| 关 键 词： | 气温预报 机器学习 集成学习 数值天气预报 |
| 收稿时间： | 2020-01-15 |
Ensemble Learning for Bias Correction of Station Temperature Forecast Based on ECMWF Products |
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| Affiliation: | 1.Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 1000842.National Supercomputing Center in Wuxi, Wuxi 214011 |
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| Abstract: | To improve the accuracy of numerical weather prediction (NWP) and its ability for extreme weather event forecast, a hybrid model based on ensemble learning is proposed and tested by post-processing one of the most successfully predicted variables, temperature at 2 m height. The NWP dataset used is provided by The International Grand Global Ensemble (TIGGE) project in the European Centre from Medium-Range Weather Forecasts (ECMWF), with a horizontal resolution of 0.125°×0.125° and lead times from 6 to 168 h (with a 6 h increment, 28 lead times totally). The observation is collected from 301 stations covering China expect for Xizang and Qinghai, including 4 variables, temperature, pressure, relative humidity and wind speed every 3 hours. The ECMWF product and observation span a period of 6 years ranging from 1 January 2013 to 31 December 2018. Data from 2013 to 2017 are used for machine learning and model training, and data in 2018 are used for testing. The hybrid model named ALS consists of 2 stages. Stage 1 trains two separate models, a long short-term memory combined with a fully connected neural network (LSTM-FCN) and an artificial neural network (ANN). Stage 2 blends the output of LSTM-FCN and ANN using a linear regression (LR) model. The correction result is the output of LR. ALS model is then applied to correct the station temperature forecast with lead time from 6 to 168 h. Outcomes are verified by observations from stations, while LR model is used as control model. ALS model reduces the average root mean square error (RMSE) of the station temperature forecast by 0.61℃ (19.6%), and by 0.23℃ (8.4%) compared with the LR model. ALS model reduces RMSE at more stations compared with LR model (252 vs. 186). ALS model is particularly effective in areas where the accuracy of station temperature forecast is low, such as Guizhou and Yunnan. Forecasts for stations in these areas are significantly improved with an average RMSE reduction over 40%. Moreover, case analysis of high temperature show that ALS model improves the forecast accuracy of high temperature events significantly, with a RMSE reduction of 30.5% at 4 stations compared to station temperature forecast. It demonstrates that ensemble learning can be used to supplement weather forecast. |
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