Ensemble Learning for Bias Correction of Station Temperature Forecast Based on ECMWF Products

Chen Yuwen; Huang Xiaomeng; Li Yi; Chen Yue; Tsui Chi; Huang Xing

doi:10.11898/1001-7313.20200411

Vol.31, NO.4, 2020

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Chen Yuwen, Huang Xiaomeng, Li Yi, et al. Ensemble learning for bias correction of station temperature forecast based on ECMWF products. J Appl Meteor Sci, 2020, 31(4): 494-503. DOI: 10.11898/1001-7313.20200411.

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Chen Yuwen, Huang Xiaomeng, Li Yi, et al. Ensemble learning for bias correction of station temperature forecast based on ECMWF products. J Appl Meteor Sci, 2020, 31(4): 494-503. DOI: 10.11898/1001-7313.20200411.

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Ensemble Learning for Bias Correction of Station Temperature Forecast Based on ECMWF Products

DOI: 10.11898/1001-7313.20200411

Chen Yuwen¹,
Huang Xiaomeng^{1,2
,
,},
Li Yi²,
Chen Yue¹,
Tsui Chi²,
Huang Xing^1,2

1.
Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084
2.
National Supercomputing Center in Wuxi, Wuxi 214011

Received Date: 2020-01-15
Rev Recd Date: 2020-04-09
Publish Date: 2020-07-31

Abstract

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.
- temperature forecast;
- machine learning;
- ensemble learning;
- numerical weather prediction

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References(43)

Figures and Tables

Fig. 1 The structure of ALS model

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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)

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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

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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

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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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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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