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Ensemble Learning for Bias Correction of Station Temperature Forecast Based on ECMWF Products
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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。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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图 2 不同模型订正结果相对站点气温预报改善率分布
(蓝色表示订正改善率为正，红色表示订正改善率为负)
Fig. 2 average correction improvement rate of different models compared to station temperature forecast
(the blue denotes a positive correction improvement rate, the red denotes a negative correction improvement rate)
图 3 2018年6—8月贵阳站、原平站、福州站、台南站72 h气温预报与观测对比
Fig. 3 Comparison of temperature forecast with lead time of 72 h to the observation at Guiyang, Yuanping, Fuzhou and Tainan from Jun to Aug in 2018
图 4 2018年7月15—22日贵阳站、原平站、福州站和台南站气温预报整体均方根误差对比
Fig. 4 Comparison of averaged root mean square error of temperature forecast at Guiyang, Yuanping, Fuzhou and Tainan from 15 Jul to 22 Jul in 2018
表 1 2018年不同预报时效的气温预报均方根误差(单位：℃)
Table 1 Root mean square error of temperature forecast with different lead times in 2018(unit:℃)
预报时效/h 站点气温预报 LR模型 ANN模型 LSTM-FCN模型 ALS模型 6~24 2.73 2.11 1.84 1.86 1.81 30~48 2.87 2.48 2.26 2.24 2.20 54~72 2.98 2.64 2.45 2.43 2.38 78~96 3.08 2.76 2.65 2.56 2.53 102~120 3.19 2.87 2.74 2.68 2.66 126~144 3.35 3.02 2.93 2.86 2.83 150~168 3.57 3.20 3.14 3.07 3.05 
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表 2 2018年6—8月4个站72 h预报时效气温预报均方根误差(单位：℃)
Table 2 Root mean square error of temperature forecast with lead time of 72 h at 4 stations from Jun to Aug in 2018(unit:℃)
站点 站点气温预报 LR模型 ANN模型 LSTM-FCN模型 ALS模型 贵阳站 2.02 1.57 1.55 1.48 1.48 原平站 4.15 2.88 2.63 2.64 2.60 福州站 2.82 1.88 1.80 1.75 1.75 台南站 1.86 1.61 1.55 1.49 1.49
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