基于Lorenz模型的集合预报与单一预报的比较研究  

Comparative study of Lorenz model based ensemble forecasting and single forecasting

作　　者：梁丁 顾斌[1,3] 丁瑞强 李建平[4] 钟权加 Liang Ding1）2） Gu Bin1）s） Ding Rui-Qiang2）t Li Jian-Ping4） Zhong Quan-Jia2） 1）(Institute of Space Weather Research, Nanjing University of Information Science and Technology, Nanjing 210044, China） 2） （State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics （LASC, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China） 3） （College of Physics and Optoelectronic Engineering, Nanjing University of Information Science and Technolog~ Nanjing 210044, China） 4） （College of Global Change and Earth System Science （GCESS, Beijing Normal University, Beijing 100875, China)

机构地区：[1]南京信息工程大学空间天气研究所,南京210044 [2]中国科学院大气物理研究所,大气科学和地球流体力学数值模拟国家重点实验室,北京100029 [3]南京信息工程大学物理与光电工程学院,南京210044 [4]北京师范大学全球变化与地球系统科学研究院,北京100875

出　　处：《物理学报》2018年第7期60-68,共9页Acta Physica Sinica

基　　金：国家自然科学基金优秀青年科学基金(批准号：41522502)、国家科技支撑计划(批准号：2015BAC03800)和“全球变化与海气相互作用”专项(批准号：GASI-IPOVAI-06)资助的课题.

摘　　要：根据非线性局部Lyapunov向量方法和增长模繁殖方法,选取Lorenz63模型和Lorenz96模型的不同状态为例,对集合预报与单一预报的预报技巧开展了对比研究.结果表明:与单一预报比较,集合预报的均方根误差和型异常相关有明显改善,随预报时间推移,改善效果越显著,且集合平均优于单一预报的实验个例数逐渐增多.就概率分布(f)而言,单一预报状态的f与真实状态基本一致,不随时间变化;而集合平均预报状态的f则随时间呈现出值域变窄、峰值变大的特点.表明随预报时间的延长,单一预报状态为混沌吸引子上的随机状态,而集合平均预报状态为吸引子子集上的随机状态,这可能是集合平均误差小于单一预报的原因.In the past two decades, the ensemble forecasting has gained considerable attention. The atmosphere is a chaotic system, and a small error in the initial conditions will result in an enormous forecast uncertainty with time. It is impossible to precisely predict the future state of the atmosphere by a single （or control） forecasting. The ensemble forecasting is a feasible method to reduce the forecast uncertainty and to provide the reliability information about forecast. Many studies showed that because of the nonlinear filtering effect, the ensemble forecastiug is more skillful than the single forecasting according to the statistical average over a large number of numerical experimental cases. However, the forecast skill can vary widely from day to day according to the specific synoptic events. The dependence of the ensemble forecasting on specific event has not been fully addressed in previous studies. Therefore, the performances of the ensemble forecasting in specific experimental cases should be further studied, which is important for improving the forecast skill in weather and climate events. In this paper, the nonlinear local Lyapunov vectors （NLLVs）, which indicate orthogonal directions in phase space with different perturbation growth rates, are introduced to generate the initial perturbations for the ensemble forecasting. The NLLVs span the fast-growing perturbation subspace efficiently, and thus may grasp more components in analysis errors than other ensemble methods. Meanwhile, the bred growing mode （BGM） method, which indicates the fastest growing perturbation mode, is also used for the ensemble forecasting. Based on the NLLV and BGM methods, the forecast performances of the ensemble forecasting and single forecasting are compared in the Lorenz63 and Lorenz96 models for specific experimental cases. Additionally, two practical measures, namely the root mean square error （RMSE） and pattern anomaly correlation （PAC）, are used to assess the performances of the ensemble forecasting. The

关 键 词：非线性局部Lyapunov向量 集合预报 单一预报 Lorenz模型 

分 类 号：P456[天文地球—大气科学及气象学]