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| 基于径向基函数神经网络温度预报订正方法及评估 |
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张亚刚1,2,3, 杨银1,2,3, 张成军1,2,3, 纪晓玲1,2,3, 杨文军1,2,4, 毛璐1,2,3
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1.中国气象局旱区特色农业气象灾害监测预警与风险管理重点实验室，宁夏 银川 750002;2.宁夏回族自治区气象防灾减灾重点实验室，宁夏 银川 750002;3.宁夏气象台，宁夏 银川 750002;4.贺兰县气象局，宁夏 银川 750200
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| 摘要: |
| 根据中央气象台自2017年10月—2018年9月20:00起报未来72 h 0.05 °×0.05 °分辨率格点日最高、最低温度指导预报和国家气象信息中心格点温度实况，应用Matlab神经网络工具箱提供的newrbe函数，建立基于径向基函数(RBF)神经网络的温度预报模型，对2018年10月—2019年9月RBF预报产品进行格点检验评估，并与同期的EC模式预报产品做了对比。结果表明：（1） 通过RBF模型订正后的24 h、48 h和72 h日最高和最低温度预报准确率较中央气象台指导预报（NMC）分别提高了7.21%、6.98%、5.48%和5.67%、4.46%、4.47%，均为正技巧，且春、夏、秋季预报订正效果要好于冬季；（2） 分区域预报检验来看，除海源、同心、彭阳的最高温度预报和海源、惠农的最低温度预报误差偏较大外，其他区域的误差基本都小于2 ℃。特别是对强降温、霜冻天气的温度预报准确率高于NMC，对预报员有一定的参考价值。 |
| 关键词: 径向基 神经网络 最高温度 最低温度 |
| DOI：10.16032/j.issn.1004-4965.2021.013 |
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| TEMPERATURE FORECAST CORRECTION METHOD AND EVALUATION BASED ON RADIAL BASIS FUNCTION NEURAL NETWORK |
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ZHANG Ya-gang1,2,3, YANG Yin1,2,3, ZHANG Cheng-jun1,2,3, JI Xiao-ling1,2,3, YANG Wen-jun1,2,4, MAO Lu1,2,3
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1. Key Laboratory of Meteorological Disaster Monitoring and Early Warning and Risk Management of Characteristic Agriculture in Arid Regions, CMA, Yinchuan 750002, China;2. Key Laboratory of Meteorological Disaster Prevention and Mitigation in Ningxia, Yinchuan 750002, China;3. Ningxia Meteorological Observatory, Yinchuan 750002, China;4. Helan Meteorological Bureau, Yinchuan 750200, China
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| Abstract: |
| The present study uses the daily maximum and minimum temperature 72 hours guidance forecast with 0.05°×0.05° grid resolution released by the National Meteorological Center (NMC) for the period from October 2017 to September 2018, the actual temperature observed by the national meteorological information grid, and the newrbe function provided by the Matlab neural network toolbox to establish a temperature forecast model based on the radial basis function (RBF) neural network. Then, the temperature forecast model is used to conduct grid inspection and evaluation of the RBF forecast products for the period from October 2018 to September 2019. It is also compared with the EC model forecast products for the same period. The results show that: (1) The accuracy of the daily maximum and minimum temperature forecasts at 24h, 48h and 72h after the correction of the RBF model is increased by 7.21% and 6.98%, 5.48% and 5.67%, 4.46% and 4.47%, respectively. They are all positive skills, and the correction in spring, summer and autumn forecast is larger than that in winter. (2) According to the sub-regional forecast inspection, the errors are basically less than 2 degrees, except for those of the maximum temperature of Haiyuan, Tongxin, and Pengyang and the minimum temperature of Haiyuan and Huinong. In particular, the accuracy of temperature forecast for severe cooling and frost weather is higher than that by the NMC, and the former can serve as reference for weather forecasters. |
| Key words: radial basis neural network maximum temperature minimum temperature |