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Machine Learning Correction of Wind, Temperature and Humidity Elements in Beijing-Tianjin-Hebei Region
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摘要: 基于线性回归方法、梯度提升回归方法(GBRT方法)、XGBoost方法和堆叠集成学习方法(Stacking方法)4种机器学习方法，采用误差分析建模思路，针对北京城市气象研究院研发的睿图-睿思系统对2020年12月—2021年11月所有起报时次未来3~12 h的2 m温度、2 m相对湿度、10 m风速以及10 m风向4种气象要素预报，开展京津冀复杂地形下的站点预报误差订正技术研究及试验应用。结果表明：基于预报误差分析构建的4种订正模型中，由于Stacking方法集成了前3种方法的优势，在4个季节的4种气象要素订正中均表现最佳，其他3种单一机器学习方法试验中，XGBoost方法表现最佳，其后依次为GBRT方法、线性回归方法，但均对预报准确率有明显的正向提升效果。总体上，基于机器学习方法构建的预报误差订正模型可有效降低系统原始预报误差，有助于进一步提升复杂地形下站点客观释用产品的预报准确性。
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关键词:
- 睿图-睿思;
- 机器学习;
- XGBoost方法;
- Stacking方法
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.-
Key words:
- RMAPS-RISE;
- machine learning;
- XGBoost;
- Stacking
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图 1 不同季节各起报时次有效样本量
Fig. 1 Number of valid samples for each initial time in different seasons
图 2 RISE系统春季2 m温度预报订正前后均方根误差对比
Fig. 2 Comparison of 2 m temperature root mean square error of RISE in spring before and after correction
图 3 RISE系统4个季节2 m相对湿度预报订正前后均方根误差对比
Fig. 3 Comparison of 2 m relative humidity root mean square error of RISE in four seasons before and after correction
图 4 RISE系统夏季10 m风速预报订正前后均方根误差对比
Fig. 4 Comparison of 10 m wind speed root mean square error of RISE in summer before and after correction
图 5 2022年2月19日2 m温度(柱状) 与10 m风速(折线) 订正对比
Fig. 5 Comparison of 2 m temperature (the bar) and 10 m wind speed (the line) on 19 Feb 2022
表 1 4种方法对2 m温度预报的季节订正结果
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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表 2 4种方法对2 m相对湿度的季节订正结果
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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表 3 4种方法对10 m风速的季节订正结果
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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表 4 4种方法对10 m风向的季节订正结果
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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