| 金沙江下游多种面雨量集成预报方法的对比分析 |
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| 引用本文： | 周芳弛,李国平,宋雯雯,游家兴.金沙江下游多种面雨量集成预报方法的对比分析[J].气象科技,2023,51(1):85-93. |
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| 作者姓名： | 周芳弛 李国平 宋雯雯 游家兴 |
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| 作者单位： | 成都信息工程大学大气科学学院， 成都 610225;1 成都信息工程大学大气科学学院， 成都 610225； 2 江苏省气象灾害预报预警与评估省部共建协同创新中心， 南京 210044;四川省高原与盆地暴雨旱涝灾害四川省重点实验室， 成都 610072;四川省中国三峡建工（集团）有限公司，成都 610041 |
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| 基金项目： | 国家自然科学基金（42175002，42075013），金沙江下游梯级水电站气象预报关键技术研究及系统建设项目(JG/20015B），高原与盆地暴雨旱涝灾害四川省重点实验室科技发展基金（SCQXKJYJXMS202117）资助 |
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| 摘 要： | 集成方法有利于提高降水要素预报的准确性和可预报性。本文基于格点实况资料和智能网格预报、西南区域数值预报、ECMWF模式预报、GRAPES模式预报产品，以面雨量为研究对象，采用多元回归法、BP神经网络法、评分权重法、加权集成预报法和算术平均法，得到集成面雨量预报，再运用平均绝对误差、模糊评分、正确率、TS评分、偏差分析等方法，对2020年4—10月金沙江下游面雨量预报效果进行对比分析。结果表明：多元回归集成法和BP神经网络法的预报效果总体上优于其他几种集成方法。在考虑流域面雨量的预报量级时，下游可以采用预报量级较小的模式和集成方法。集成后偏差百分比均有降低，且多元回归法和BP神经网络法对预报量级较小的模式有矫正作用。在面雨量有无、小雨和中雨预报中，多元回归法集成效果较好，在大雨量级预报中，BP神经网络法集成效果较好。这些结论可为流域面雨量预报提供参考借鉴。
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| 关 键 词： | 金沙江下游 面雨量 集成方法 预报检验 |
| 收稿时间： | 2021/12/5 0:00:00 |
| 修稿时间： | 2022/10/28 0:00:00 |
Comparative Analysis of Multiple Ensemble Forecasting Methods of Areal Rainfall in Lower Reaches of Jinsha River |
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| ZHOU Fangchi,LI Guoping,SONG Wenwen,YOU Jiaxing.Comparative Analysis of Multiple Ensemble Forecasting Methods of Areal Rainfall in Lower Reaches of Jinsha River[J].Meteorological Science and Technology,2023,51(1):85-93. |
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| Authors: | ZHOU Fangchi LI Guoping SONG Wenwen YOU Jiaxing |
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| Abstract: | The ensembled method is beneficial in improving the accuracy and predictability of precipitation element forecasts. This paper is based on grid data and smart grid forecasts, southwestern regional numerical forecasts, ECMWF model forecasts and GRAPES model forecast data, with area rainfall as the research object, using the multiple regression method, BP neural network method, scoring weight method, weighted ensembled forecasting method and the arithmetic average method to obtain the ensembled areal rainfall forecast, and then the average absolute error, fuzzy score, correct rate, TS score, deviation analysis and other methods are used to compare and analyze the forecast effect of the lower reaches of the Jinsha River from April to October 2020. The results show that the prediction effect of the multiple regression method and the BP neural network method are generally better than those of the other ensemble methods. When considering the forecast magnitude of the area rainfall in the basin, the model and ensemble method with smaller forecast magnitude can be employed downstream. After ensemble, the deviation percentages are reduced, and the multiple regression method and the BP neural network method have a corrective effect on the models with smaller forecast magnitudes. In the forecast of whether there is precipitation, light rain and moderate rain, the multiple regression method has a better ensemble effect. In the heavy rainfall forecast, the BP neural network method has a better ensemble effect. These conclusions can provide references for future surface rainfall forecasting in the river valley. |
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| Keywords: | lower reaches of the Jinsha River area rainfall ensemble method forecast verification |
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