气象资料的统计降尺度方法综述

doi:10.11867/j.issn.1001-8166.2011.08.0837

地球科学进展 ›› 2011, Vol. 26 ›› Issue (8): 837 -847. doi: 10.11867/j.issn.1001-8166.2011.08.0837

气象资料的统计降尺度方法综述

刘永和 ¹, 郭维栋 ², 冯锦明 ³, 张可欣 ⁴

1.河南理工大学资源环境学院，河南焦作454000; 2.南京大学气候与全球变化研究院，大气科学学院，江苏南京210093; 3.中国科学院东亚区域气候—环境重点实验室，全球变化东亚区域研究中心，中国科学院大气物理研究所，北京100029;
4.山东省临沂市气象局，山东临沂276004

收稿日期:2011-02-14 修回日期:2011-04-14 出版日期:2011-08-10
通讯作者: 刘永和 E-mail:sucksis@163.com
基金资助:
国家自然科学基金项目“汶川巨震对降水过程激发机制的初步控制”(编号：40975049)；国家自然科学基金重大国际（地区）合作研究项目“亚洲和北美半干旱区大气—植被—水相互作用的比较研究”(编号：40810059003)；淮河流域气象开放研究基金项目“沂沭河流域暴雨洪水预警及灾害评估业务化技术”(编号：HRM200904)资助.

A Summary of Methods for Statistical Downscaling of Meteorological Data

Liu Yonghe ¹, Guo Weidong ², Feng Jinming ³, Zhang Kexin ⁴

1.Institute of Resources and Environment, Henan Polytechnic University, Jiaozuo454000,China;2.ICGCR, School of Atmospheric Sciences, Nanjing University, Nanjing210093,China;3.Key Laboratory of Regional ClimateEnvironment Research for Temperate East Asia, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing100029,China;4.Linyi Meteorological Bureau, Linyi276004,China

Received:2011-02-14 Revised:2011-04-14 Online:2011-08-10 Published:2011-08-10

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统计降尺度是解决由气象模式输出的低分辨率资料到流域尺度资料转换的手段之一，已成为一个重要的研究领域。统计降尺度方法十分丰富，分为传递函数法、天气形势法和天气发生器3类，3类之间并无严格的界限。统计降尺度涉及到时间与空间降尺度、随机型与确定型降尺度、时间自相关与空间相关性以及面向格点与面向站点的降尺度这4个方面的属性与分类问题，各种具体方法在这些方面的表现有所不同。近年来，相似法、隐马尓可夫模型、广义线性模型、Poisson点过程以及乘性瀑布过程获得了较大的发展和应用，并诞生了各种非线性模型以及物理—统计模型等新方法，已有一些影响较大的统计降尺度模型软件。新的方法在不断涌现，其中非线性模型、气候情境随机模拟技术、短期预报资料降尺度技术以及结合物理机理的统计降尺度方法是未来的主要发展趋势。

关键词: 统计降尺度, 天气发生器, 天气分类, 传递函数, 非线性模型

As one of the means for bridging the gap of the data between low resolution data obtained from weather models and that needed in basin scale, statistical downscaling become a important field for study. The approaches for statistical downscaling are abundant and can be roughly divided into three groups: Transfer functions, weather-typing approaches and stochastic weather generators. The transfer functions may be linear methods, such as multivariate linear regression, canonic correlation, and Singular Value Decomposition, or nonlinear methods like artificial neural networks and support vector machine. Weather generators are designed initially for generating the missing weather data, but in downscaling applications it can be used as the output backend of other statistical downscaling techniques. The weather-typing approaches can be regarded as some variant of weather generators combining with some transfer functions or classifications. Thus, no strict boundaries exist among the three groups. The statistical downscaling problems have some attributes involved with temporal downscaling or spatial downscaling, stochastic downscaling or deterministic downscaling, temporal self-correlation and spatial correlation, point-site oriented or grids oriented. The differences of downscaling performance between all techniques are mainly related to these attributes. In recent years, the analog method, weather classifying, hidden Markov modeling, generalized linear models, Poisson clustered point process and the multiplicative cascade process based on multifractal theories are developed and used for statistical downscaling application, and many new nonlinear methods such as generalized additional models and physical-statistical methods are arising. Meanwhile, there is also some widely used model software available. Among all new emerging techniques, the nonlinear methods, stochastic simulation techniques of climate scenario, downscaling models for shortterm weather prediction and the statistical downscaling methods combining physical mechanism may become the main trend of study in the future.

Key words: Statistical downscaling, Weather generator, Weather typing, Transfer function, Nonlinear model

中图分类号:

P432+.1

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[1]	范丽军;符淙斌;陈德亮;. 统计降尺度法对未来区域气候变化情景预估的研究进展[J]. 地球科学进展, 2005, 20(3): 320-329.

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