| 基于探空数据集成学习的短时强降水预报试验 |
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| 引用本文： | 韩丰,杨璐,周楚炫,吕终亮.基于探空数据集成学习的短时强降水预报试验[J].应用气象学报,2021,32(2):188-199. |
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| 作者姓名： | 韩丰 杨璐 周楚炫 吕终亮 |
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| 作者单位： | 1.国家气象中心, 北京 100081 |
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| 摘 要： | 使用2015—2019年6—9月08:00（北京时）我国119个探空站的大气层结和对流参数作为特征参数，基于XGBoost集成学习方法，建立短时强降水事件预报模型。同时，提出一种面向高影响天气的模型优化思路，通过使用分段权重损失函数，进行模型调优，在空报率不超过一定阈值的情况下，可提升模型预报的命中率和TS评分。设计分段权重损失函数权重敏感性试验和损失函数对比试验，选取7个区域中心探空站对比分析模型优化方法的有效性和泛化性。利用2019年6—9月全国探空数据针对短时强降水预报开展批量独立检验和个例分析，结果表明:改进后的预报模型TS评分提高0.05~0.1，命中率提高0.15以上，空报率提高0.05~0.1，表现出明显的“宁空勿漏”预报倾向，模型预报能力得到明显提升；在全国短时强降水预报试验中，预报模型命中率为0.65，空报率为0.37，漏报率为0.34，TS评分为0.47，说明该模型对短时强降水天气具有一定预报能力。
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| 关 键 词： | 短时强降水 探空数据 集成决策树 损失函数优化 |
| 收稿时间： | 2020-08-04 |
An Experimental Study of the Short-time Heavy Rainfall Event Forecast Based on Ensemble Learning and Sounding Data |
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| Institution: | 1.National Meteorological Center, Beijing 1000812.Institute of Urban Meteorology, China Meteorological Administration, Beijing 100089 |
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| Abstract: | Sounding analysis is one important method for short-term heavy rainfall event forecasting. By using sounding data of 119 stations at 0800 BT of 1 June -30 September during 2015-2019, based on XGBoost integrated learning framework, a prediction model for short-term heavy rainfall events (not less than 20 mm·h-1) is proposed. Sounding data and derivative physical elements are used as characteristics parameters. The model can forecast whether short-term heavy rainfall occurs around the sounding station in following 12 h. Then an optimization method of high-risk weather is proposed. Using piecewise cost function as a loss function, different weighting factors are used to make the model more sensitive. This will ensure the total number of false prediction samples do not increase, but more false alarms rather than missing ones, leading to a slight increase on threat score (TS), a great improvement on probability of detection (POD) and the false alarm rate (FAR) will not exceed the threshold such as 0.5. After that, two tests are designed including a weighted sensitivity test for the piecewise loss function and a comparison test of the loss function using 12 datasets of 7 regional center sounding stations. The efficiency of the model optimization method is verified and the prediction ability before and after the improvement are compared. At last, a test of national short-term heavy precipitation forecast is designed, by using sounding data from 1 June to 30 September in 2019 as independent test set. Results show that reducing wTP will decrease the number of hits and false alarm of the model's forecast; reducing wFN will increase the number of hits and false alarms; wTN and wFP have little influence on the prediction. Compare with other commonly used cost function, the model with piecewise weight cost function has better forecasting skills, in which the TS is improved by 0.05-0.1, the POD is increased by more than 0.15, and the FAR is improved by 0.05-0.1. The model shows a clear tendency of forecasting positive instead of missing. In addition, the model shows similar results in all independent experiments, indicating that the optimization method has consistent effects on the results. The independent test of the national short-term heavy rainfall forecast experiment shows that the improved model has a certain short-term heavy rainfall forecast ability, with POD of 0.66, FAR of 0.37, and TS of 0.47. Above all, a short-term heavy rain prediction model is constructed based on the integrated decision tree and sounding data. The optimization method which could enhance the forecast skill of model is also proposed and verified. |
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