基于多神经网络的动态权重集成温度预报订正研究
Research on Temperature Forecast Correction by Dynamic Weight Integration Based on Multi-neural Networks
朱育雷 1杨静 2钟水新 3朱文达 2李智玉 2魏涛 2李彦霖 2顾天红2
作者信息
- 1. 中国气象局广州热带海洋气象研究所/广东省区域数值天气预报重点实验室,广东 广州 510641;贵州省气象台,贵州 贵阳 550002
- 2. 贵州省气象台,贵州 贵阳 550002
- 3. 中国气象局广州热带海洋气象研究所/广东省区域数值天气预报重点实验室,广东 广州 510641
- 折叠
摘要
基于CMA-GD模式预报数据,利用多神经网络的动态权重集成方法,开展了贵州省温度预报订正研究,最终获得本地化温度预报订正产品.结果表明:(1)在对历史数据检验评估的基础上,利用多种神经网络方法可有效降低模式系统误差,通过BP、BP_GA、WAVENN、GRNN、LSTM等神经网络订正,2020年贵州省0～72h预报时效的温度平均绝对误差较模式降低0.01～0.17℃;(2)考虑到不同神经网络订正结果的差异性,采用动态权重方案对订正结果进行优势集成可显著提升预报可靠性.经集成后的温度预报效果优于模式直接输出和各神经网络订正结果,2020年贵州省0～72 h预报时效的温度平均绝对误差较模式降低14.93%,预报准确率提升8.24%.此外,动态权重集成后的订正结果还表现出较好的稳定性.基于该方法形成的本地化客观预报订正产品可为提升贵州复杂地形下温度预报质量以及精细化预报服务水平提供参考依据.
Abstract
Based on the forecast data from the CMA-GD model,the present study uses dynamic weight integration based on multi-neural networks to improve temperature forecasting in Guizhou Province and obtain corrected localized temperature forecasts.The results show that:(1)Based on the verification and evaluation of observational data,multi-neural networks can effectively reduce model bias.The mean absolute error(MAE)of 0～72 h temperature in Guizhou Province in 2020 is reduced by 0.01～0.17℃ with the methods of BP,BP_GA,WAVENN,GRNN,and LSTM,respectively.(2)Given the difference in the correction results of different neural networks,dynamic weight integration is used to integrate the results from different neural networks,and it can significantly improve forecast reliability.The integrated temperature forecast is better than the prediction by CMA-GD as well as the corrections of neural networks in terms of MAE and forecast accuracy(FA).The MAE of 0～72 h temperature in Guizhou Province in 2020 is reduced by 14.93%and the FA is improved by 8.24%compared with those by the CMA-GD model.Moreover,the ensemble method based on dynamic weight shows satisfying stability.In general,the corrected product based on this method can provide guidance to improve the quality of temperature forecasting as well as the level of refined forecasting services under the complex terrain in Guizhou Province.
关键词
神经网络/集成方法/2/m温度/订正/CMA-GDKey words
neural network/integration method/2 m temperature/correction/CMA-GD引用本文复制引用
基金项目
中国气象局/广东省区域数值天气预报重点实验室开放基金(J202003)
贵州省气象局科研业务项目([2021]07-06)
贵州省气象局研究型业务关键技术攻关团队项目(GGTD-202210)
出版年
2024
国家科技期刊平台
NSTL
万方数据