本文關(guān)鍵詞：氣象資料的統(tǒng)計(jì)降尺度方法綜述，由筆耕文化傳播整理發(fā)布。

氣象資料的統(tǒng)計(jì)降尺度方法綜述

A Summary of Methods for Statistical Downscaling of Meteorological Data

[1] [2] [3] [4]

Liu Yonghe,Guo Weidong,Feng Jinming,Zhang Kexin（1.Institute of Resources and Environment,Henan Polytechnic University,Jiaozuo 454000,China;2.ICGCR,School of Atmospheric Sciences,Nanjing

[1]河南理工大學(xué)資源環(huán)境學(xué)院,河南焦作454000; [2]南京大學(xué)氣候與全球變化研究院,大氣科學(xué)學(xué)院,江蘇南京210093; [3]中國(guó)科學(xué)院東亞區(qū)域氣候-環(huán)境重點(diǎn)實(shí)驗(yàn)室,全球變化東亞區(qū)域研究中心,中國(guó)科學(xué)院大氣物理研究所,北京100029; [4]山東省臨沂市氣象局,山東臨沂276004

文章摘要：統(tǒng)計(jì)降尺度是解決由氣象模式輸出的低分辨率資料到流域尺度資料轉(zhuǎn)換的手段之一,已成為一個(gè)重要的研究領(lǐng)域。統(tǒng)計(jì)降尺度方法十分豐富,分為傳遞函數(shù)法、天氣形勢(shì)法和天氣發(fā)生器3類,3類之間并無(wú)嚴(yán)格的界限。統(tǒng)計(jì)降尺度涉及到時(shí)間與空間降尺度、隨機(jī)型與確定型降尺度、時(shí)間自相關(guān)與空間相關(guān)性以及面向格點(diǎn)與面向站點(diǎn)的降尺度這4個(gè)方面的屬性與分類問(wèn)題,各種具體方法在這些方面的表現(xiàn)有所不同。近年來(lái),相似法、隱馬尓可夫模型、廣義線性模型、Poisson點(diǎn)過(guò)程以及乘性瀑布過(guò)程獲得了較大的發(fā)展和應(yīng)用,并誕生了各種非線性模型以及物理—統(tǒng)計(jì)模型等新方法,已有一些影響較大的統(tǒng)計(jì)降尺度模型軟件。新的方法在不斷涌現(xiàn),其中非線性模型、氣候情境隨機(jī)模擬技術(shù)、短期預(yù)報(bào)資料降尺度技術(shù)以及結(jié)合物理機(jī)理的統(tǒng)計(jì)降尺度方法是未來(lái)的主要發(fā)展趨勢(shì)。

Abstr：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 soft

文章關(guān)鍵詞：

Keyword：:Statistical downscaling Weather generator Weather typing Transfer function Nonlinear model.

相關(guān)文章：

[1] 區(qū)域氣候變化統(tǒng)計(jì)降尺度研究進(jìn)展   《生態(tài)學(xué)報(bào)》  2011年09期
[2] 2種降尺度方法在太湖流域的應(yīng)用對(duì)比   《氣象科學(xué)》  2011年02期
[3] 統(tǒng)計(jì)降尺度方法在北京月尺度預(yù)測(cè)中的應(yīng)用   《氣象》  2011年06期

課題項(xiàng)目：國(guó)家自然科學(xué)基金項(xiàng)目“汶川巨震對(duì)降水過(guò)程激發(fā)機(jī)制的初步控制”（編號(hào)：40975049）;國(guó)家自然科學(xué)基金重大國(guó)際（地區(qū)）合作研究項(xiàng)目“亞洲和北美半干旱區(qū)大氣—植被—水相互作用的比較研究”（編號(hào)：40810059003）; 淮河流域氣象開(kāi)放研究基金項(xiàng)目“沂沭河流域暴雨洪水預(yù)警及災(zāi)害評(píng)估業(yè)務(wù)化技術(shù)”（編號(hào)：HRM200904）資助

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  本文關(guān)鍵詞：氣象資料的統(tǒng)計(jì)降尺度方法綜述，，由筆耕文化傳播整理發(fā)布。