| 遗传算法优化的BP神经网络在地面温度多模式集成预报的应用研究 |
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| 引用本文： | 雷彦森,蔡晓军,王文,李江峰,李倩文.遗传算法优化的BP神经网络在地面温度多模式集成预报的应用研究[J].气象科学,2018,38(6):806-814. |
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| 作者姓名： | 雷彦森 蔡晓军 王文 李江峰 李倩文 |
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| 作者单位： | 南京信息工程大学 气象灾害教育部重点实验室/气候与环境变化国际合作联合实验室/气象灾害预报预警与评估协同创新中心, 南京 210044;武汉中心气象台, 武汉 430074,南京信息工程大学 气象灾害教育部重点实验室/气候与环境变化国际合作联合实验室/气象灾害预报预警与评估协同创新中心, 南京 210044,南京信息工程大学 气象灾害教育部重点实验室/气候与环境变化国际合作联合实验室/气象灾害预报预警与评估协同创新中心, 南京 210044,南京信息工程大学 气象灾害教育部重点实验室/气候与环境变化国际合作联合实验室/气象灾害预报预警与评估协同创新中心, 南京 210044,南京信息工程大学 气象灾害教育部重点实验室/气候与环境变化国际合作联合实验室/气象灾害预报预警与评估协同创新中心, 南京 210044 |
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| 基金项目： | 国家自然科学基金资助项目（41275091） |
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| 摘 要： | 基于TIGGE资料集中的ECMWF、CMA和JMA的数值预报产品,利用加权集成、回归集成和消除偏差集成等线性集成方式与遗传算法优化的BP神经网络(GABP)集成,对我国大部开展地面2 m温度在24 h、48 h和72 h预报时效的多模式集成预报试验。通过对2013年1—6月的预报检验,结果表明:GABP集成预报效果有较大提升,均方误差明显小于各单一模式预报。GABP集成的误差分布在新疆和华北均方误差较大,但是在预报效果改进上GABP集成在西部地区相对单一模式的误差减小更加明显。在进行几种多模式集成方式时,GABP集成相比线性方法预报结果更加精准。对于天气过程个例的预报,GABP集成预报出预报量的变化趋势,预报效果优于单一模式和线性集成预报。无论是较长时间段还是短时间的天气过程,在改进预报效果上GABP集成都起到了最佳的作用。
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| 关 键 词： | 遗传算法 BP神经网络 多模式集成 温度预报 |
| 收稿时间： | 2018/3/1 0:00:00 |
| 修稿时间： | 2018/3/14 0:00:00 |
Application research of BP neural network optimized by genetic algorithm in multi-model ensemble forecast about ground temperature |
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| LEI Yansen,CAI Xiaojun,WANG Wen,LI Jiangfeng and LI Qianwen.Application research of BP neural network optimized by genetic algorithm in multi-model ensemble forecast about ground temperature[J].Scientia Meteorologica Sinica,2018,38(6):806-814. |
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| Authors: | LEI Yansen CAI Xiaojun WANG Wen LI Jiangfeng and LI Qianwen |
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| Institution: | Key Laboratory of Meteorological Disaster, Ministry of Education/Joint International Reaserch Laboratory of Climate and Environment Change/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing 210044, China;Wuhan Central Meteorological Observatory, Wuhan 430074, China,Key Laboratory of Meteorological Disaster, Ministry of Education/Joint International Reaserch Laboratory of Climate and Environment Change/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing 210044, China,Key Laboratory of Meteorological Disaster, Ministry of Education/Joint International Reaserch Laboratory of Climate and Environment Change/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing 210044, China,Key Laboratory of Meteorological Disaster, Ministry of Education/Joint International Reaserch Laboratory of Climate and Environment Change/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing 210044, China and Key Laboratory of Meteorological Disaster, Ministry of Education/Joint International Reaserch Laboratory of Climate and Environment Change/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing 210044, China |
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| Abstract: | Based on the numerical forecast products of ECMWF, CMA and JMA gathered in the TIGGE data, the multi-model ensemble forecasting experiment about temperature at 2 m above ground in most parts of China at the 24 h, 48 h and 72 h forecasting lead time is carried out using some linear ensemble methods such as weighted ensemble, regression ensemble and bias-removed ensemble as well as BP neural network ensemble optimized by genetic algorithm (GABP). Through the test for forecast from January to June in 2013, results show that forecasting result of GABP ensemble has been greatly improved, whose mean square error is obviously less than that of any single model. The error distribution of GABP ensemble forecast shows relatively larger mean square error in Xinjiang and North China than in other parts, but in terms of forecast result improvement, the error of GABP ensemble has reduced more obviously compared with that of single-model forecast in western China. When several multi-model ensemble experiments are being carried out, the forecasting result of GABP ensemble is more accurate than that of linear ensemble methods. For forecast about a case of synoptic process, GABP ensemble can reveal variation trend of the predict, and its forecasting result is better than that of single-model forecast and linear-ensemble forecast. No matter for long-time or short-time synoptic process, GABP ensemble plays the best role in improving forecasting results. |
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| Keywords: | Genetic algorithm BP neural network Multi-model ensemble Temperature forecast |
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