Machine Learning Correction of Wind, Temperature and Humidity Elements in Beijing-Tianjin-Hebei Region

Han Nianfei; Yang Lu; Chen Mingxuan; Song Linye; Cao Weihua; Han Lei

doi:10.11898/1001-7313.20220409

Vol.33, NO.4, 2022

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Article Navigation > Journal of Applied Meteorological Science > 2022 > 33(4): 489-500

Han Nianfei, Yang Lu, Chen Mingxuan, et al. Machine learning correction of wind, temperature and humidity elements in Beijing-Tianjin-Hebei Region. J Appl Meteor Sci, 2022, 33(4): 489-500. DOI: 10.11898/1001-7313.20220409.

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Han Nianfei, Yang Lu, Chen Mingxuan, et al. Machine learning correction of wind, temperature and humidity elements in Beijing-Tianjin-Hebei Region. J Appl Meteor Sci, 2022, 33(4): 489-500. DOI: 10.11898/1001-7313.20220409.

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Machine Learning Correction of Wind, Temperature and Humidity Elements in Beijing-Tianjin-Hebei Region

DOI: 10.11898/1001-7313.20220409

Han Nianfei^{1, 2},
Yang Lu^{2
,
,},
Chen Mingxuan²,
Song Linye²,
Cao Weihua²,
Han Lei^{1, 2}

1.
Ocean University of China, Qingdao 266100
2.
Beijing Institute of Urban Meteorology, Beijing 100089

Received Date: 2022-03-15
Rev Recd Date: 2022-04-25
Publish Date: 2022-07-13

Abstract

Abstract

Weather conditions have an important impact on agricultural production, transportation, economic activities, so the improvement of forecast accuracy has been a constant concern of the society. After more than 100 years of continuous development, the accuracy of numerical weather model has been continuously improved, but there are still inevitable forecast errors. Therefore, it is an important issue worthy of study to improve the prediction accuracy by studying various error correction methods and post-processing the results of numerical weather prediction.Machine learning method is applied to revise four meteorological elements forecasted by RMAPS-RISE(rapid-update multi-scale analysis and prediction system-rapid integration and seamless ensemble) system developed by Beijing Institute of Urban Meteorology. First, the data are preprocessed by interpolating the system forecast data and extracting the data of each element site from the grid data. The observations of automatic weather stations and forecast data are processed to establish unified datasets for the application and modeling of machine learning. Second, linear regression method, gradient boosting regression method, XGBoost method and Stacking method are designed to combine various machine learning algorithms to improve the generalization ability of the model. In addition, an error analysis model is constructed according to four correction methods, and the correction technology research and experimental application of the forecast errors of each station's initial time under the complex terrain of Beijing-Tianjin-Hebei are carried out. Finally, the improvement of the revised forecast of different machine learning methods compared with the original RMAPS-RISE system forecast accuracy is compared.In the experimental part, two modeling ideas are proposed, and four machine learning methods are used to conduct correction and comparison experiments. It shows among the modeling ideas based on error analysis, the Stacking method has the best effect, effectively reducing the forecast error of the original system for the next 3-12 hours for 24 initial times. Among the other three single machine learning method, XGBoost method performs the best, followed by the gradient boosting regression method and linear regression method, and all of them have a significant positive effect on the prediction accuracy. Overall, the forecast error correction model based on machine learning methods can effectively reduce the original forecast error of RMAPS-RISE system, and they have broad application prospects in forecast correction. It is helpful to further improve the forecast accuracy of the objective interpretation product of the site under complex terrain.
- RMAPS-RISE;
- machine learning;
- XGBoost;
- Stacking

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References(32)

Figures and Tables

Fig. 1 Number of valid samples for each initial time in different seasons

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Fig. 2 Comparison of 2 m temperature root mean square error of RISE in spring before and after correction

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Fig. 3 Comparison of 2 m relative humidity root mean square error of RISE in four seasons before and after correction

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Fig. 4 Comparison of 10 m wind speed root mean square error of RISE in summer before and after correction

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Fig. 5 Comparison of 2 m temperature (the bar) and 10 m wind speed (the line) on 19 Feb 2022

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Fig. 6 The same as in Fig. 5, but for 4 Mar 2022

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Table 1 Corrected results for 2 m temperature based on four methods in each season

统计量	方法	春季	夏季	秋季	冬季
均方根误差平均值/℃	线性回归方法	2.16	1.93	1.93	2.40
	GBRT方法	2.08	1.82	1.83	2.28
	XGBoost方法	1.88	1.61	1.60	1.98
	Stacking方法	1.55	1.44	1.44	1.77
均方根误差变化百分比/%	线性回归方法	-10.00	-15.72	-19.92	-19.19
	GBRT方法	-13.00	-20.52	-24.07	-23.23
	XGBoost方法	-21.67	-29.69	-33.61	-33.33
	Stacking方法	-35.40	-37.12	-40.25	-40.40
注：RISE产品春季、夏季、秋季、冬季均方根误差平均值分别为2.40，2.29，2.41，2.97℃。

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Table 2 Corrected results for 2 m relative humidity based on four methods in each season

统计量	方法	春季	夏季	秋季	冬季
均方根误差平均值/%	线性回归方法	12.34	10.64	12.00	12.57
	GBRT方法	11.72	9.85	11.12	11.61
	XGBoost方法	10.24	8.66	9.69	10.18
	Stacking方法	8.89	7.61	8.50	9.04
均方根误差变化百分比/%	线性回归方法	-11.35	-19.45	-21.41	-17.30
	GBRT方法	-15.80	-25.44	-27.18	-23.62
	XGBoost方法	-26.44	-34.44	-36.44	-33.03
	Stacking方法	-36.14	-42.39	-44.34	-40.53
注：RISE产品春季、夏季、秋季、冬季均方根误差平均值分别为13.92%, 13.21%, 15.27%, 15.20%。

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Table 3 Corrected results for 10 m wind speed based on four methods in each season

统计量	方法	春季	夏季	秋季	冬季
均方根误差平均值/(m·s^-1)	线性回归方法	1.31	1.07	1.25	1.43
	GBRT方法	1.26	1.01	1.08	1.31
	XGBoost方法	1.12	0.89	0.94	1.13
	Stacking方法	0.97	0.79	0.82	1.00
均方根误差变化百分比/%	线性回归方法	-8.39	-14.40	-19.87	-23.94
	GBRT方法	-11.89	-19.20	-30.77	-30.32
	XGBoost方法	-21.68	-28.80	-39.74	-39.89
	Stacking方法	-32.17	-36.80	-47.44	-46.81
注：RISE产品春季、夏季、秋季、冬季均方根误差平均值分别为1.43，1.25，1.56，1.88 m·s^-1。

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Table 4 Corrected results for 10 m wind direction based on four methods in each season

统计量	方法	春季	夏季	秋季	冬季
平均绝对偏差平均值/(°)	线性回归方法	69.93	77.20	74.15	72.13
	GBRT方法	68.47	75.16	72.90	69.60
	XGBoost方法	63.10	69.70	68.20	63.96
	Stacking方法	58.80	65.63	64.88	60.32
平均绝对偏差变化百分比/%	线性回归方法	-1.23	-4.43	-16.27	-8.98
	GBRT方法	-3.29	-6.96	-17.68	-12.18
	XGBoost方法	-10.88	-13.72	-22.99	-19.29
	Stacking方法	-16.95	-18.75	-26.74	-23.89
注：RISE产品春季、夏季、秋季、冬季平均绝对偏差平均值分别为70.80°, 80.79°, 88.56°, 79.25°。

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