| 数值天气预报和气候预测可预报性研究的若干动力学方法 |
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| 引用本文： | 段晚锁,丁瑞强,周菲凡.数值天气预报和气候预测可预报性研究的若干动力学方法[J].气候与环境研究,2013,18(4):524-538. |
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| 作者姓名： | 段晚锁 丁瑞强 周菲凡 |
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| 作者单位： | 1. 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
2. 中国科学院大气物理研究所云降水物理和强风暴实验室,北京,100029 |
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| 基金项目： | 科技部创新方法工作专项项目2008IM020500,国家自然科学基金项目41105038 |
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| 摘 要： | 简要回顾了数值天气预报和气候预测可预报性研究的若干动力学方法，包括用于研究第一类可预报性问题的线性奇异向量（LSV）和条件非线性最优初始扰动（CNOP-I）方法，以及Lyapunov指数和非线性局部Lyapunov指数方法。前两种方法用于研究预报或预测的预报误差问题，可以用于估计天气预报和气候预测的最大预报误差，而且根据导致最大预报误差的初始误差结构的信息，这两种方法可以用于确定预报或预测的初值敏感区。应该指出的是，LSV是基于线性化模式，对于描述非线性大气和海洋的运动具有局限性。因而，对于非线性模式，应该选择使用CNOP-I估计最大预报误差。Lyapunov指数和非线性局部Lyapunov指数可以用于研究第一类可预报性问题中的预报时限问题，前者是基于线性模式，不能解释非线性对预报时限的影响，而非线性局部Lyapunov指数方法则考虑了非线性的影响，能够较好地估计实际天气和气候的预报时限。第二类可预报性问题的研究方法相对较少，本文仅介绍了由我国科学家提出的关于模式参数扰动的条件非线性最优参数扰动（CNOP-P）方法，该方法可以用于寻找到对预报有最大影响的参数扰动，并可以进一步确定哪些参数最应该利用观测资料进行校准。另一方面，通过对比CNOP-I和CNOP-P对预报误差的影响，可以判断导致预报不确定性的主要误差因子，进而指导人们着力改进模式或者初始场。
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| 关 键 词： | 天气 气候 可预报性 最优扰动 非线性局部Lyapunov指数 |
| 收稿时间： | 2012/1/11 0:00:00 |
| 修稿时间： | 2013/3/14 0:00:00 |
Several Dynamical Methods Used in Predictability Studies for Numerical Weather Forecasts and Climate Prediction |
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| DUAN Wansuo,DING Ruiqiang and ZHOU Feifan.Several Dynamical Methods Used in Predictability Studies for Numerical Weather Forecasts and Climate Prediction[J].Climatic and Environmental Research,2013,18(4):524-538. |
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| Authors: | DUAN Wansuo DING Ruiqiang and ZHOU Feifan |
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| Institution: | State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029;State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029;Laboratory of Cloud-Precipitation Physics and Severe Storms, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029 |
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| Abstract: | Several dynamical methods used in predictability studies for numerical weather forecasting and climate prediction are briefly introduced. For the first type, the methods of linear singular vector (LSV), conditional nonlinear optimal initial perturbation (CNOP-I), Lyapunov exponent, and nonlinear local Lyapunov exponent (NLLE) are reviewed. The LSV and CNOP-I have been used to estimate maximal forecast errors and to identify sensitive areas in the initial stages of weather and climate prediction. Because the former method is based on a linear model and has limitations in determining nonlinear atmospheric and oceanic motions, the latter is recommended for use in nonlinear models. The Lyapunov exponent and NLLE have been used to study predictable time issues. The former is based on linear models; therefore, it cannot be used to explore nonlinear effects. However, the latter considers these effects and can be used to more accurately estimate maximal predictable time in actual weather and climate prediction. For the second type of predictability study, this paper reviews only the method of conditional nonlinear optimal parameter perturbation (CNOP-P). The CNOP-P can be used to search parameter perturbations that largely affect the forecasts and to determine those that should be verified by observation. A comparison of forecast errors brought by CNOP-I and CNOP-P can be used to determine whether the model or initial state should first be improved. |
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| Keywords: | Weather Climate Predictability Optimal perturbation Nonlinear local Lyapunov exponent |
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