首页|耦合多元混沌要素的非线性交叉预测误差模式对极端天气延伸期预测研究

耦合多元混沌要素的非线性交叉预测误差模式对极端天气延伸期预测研究

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  • 万方数据
  • 维普
本文首先分析随机误差、初始误差和模式参数误差对建立的单变量非线性交叉预测误差模式(Nonlinear Cross Prediction Error，NCPE)的极端天气延伸期预报敏感性，及不同气象要素场对延伸期预报的敏感性，再利用可降水、温度、位势高度资料建立多混沌变量耦合的模式(Multivariate NCPE，MNCPE)，深入对比分析两种模式延伸期预报特征。结果表明:随机误差和初始误差的比率量级差异会影响NCPE模式的延伸期预报特征，存在临界值效应;而针对参数误差，合适的相空间维m能较好表征吸引子的局部细节。不同气象要素场对暴雨、台风等灾害天气延伸期预报的敏感性也表现不一。耦合多变量的MNCPE模式，相比单变量NCPE，能更完备表征混沌吸引子局部结构的动力特征，降低10～30 d延伸期预报的不确定性。
Study on extreme weather for extended range forecasting with coupled multivariate chaotic elements based on Nonlinear Cross Prediction Error model
This paper first analyzed the sensitivity of extreme weather extended range forecasting to the random errors,initial errors,and model parameter errors in the established single-variable Nonlinear Cross Prediction Error(NCPE)pattern,as well as the sensitivity of different meteorological element fields to extended range forecasting.And then established a Multi-chaotic variables Nonlinear Cross Prediction Error(MNCPE)model based on precipitation,temperature,and geopotential height data,and conducts an in-depth comparative analysis of these two models.Results show that,the ratio order difference between random errors and initial errorshas a critical value effect on the extended range forecasting characteristics for NCPE model.While for parameter errors,an appropriate phase space embedding dimension m can better represent the local details of the attractor.The sensitivity of different meteorological element fields to extended range forecasting of diverse extreme weather,such as heavy rain and typhoons also varies.The MNCPE model,compared to the single-variable NCPE,can more comprehensively represent the dynamic features of the local structure of the chaotic attractor and reduce the uncertainty of the 10-30-day extended range forecasting.

extended range forecastingrandom errorinitial errormodel parameter errorcoupled multivariable chaotic system

王瑾、阮腾飞、谢静、夏志业

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陕西省气象台,西安 710000

合江县气象局,四川合江 646200

达州市气象局,四川达州 635711

成都信息工程大学资源环境学院,成都 610225

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延伸期预报 随机误差 初始误差 参数误差 多元混沌耦合

国家自然科学基金资助项目高原与盆地暴雨旱涝灾害四川省重点实验室开放基金资助项目四川省科技厅重点研发资助项目成都市科技局重点资助项目

41505012SZKT20160032022YFS04822022-YF05-00620-SN

2024

10.12306/2023jms.0018

气象科学
江苏省气象学会

气象科学

CSTPCD
影响因子：0.925
ISSN：1009-0827
年,卷(期)：2024.44(4)