基于统计降尺度方法的陕西省月气温预测分析

doi:10.12118/j.issn.1000-6060.2018.06.05

干旱区地理 ›› 2018, Vol. 41 ›› Issue (6): 1178-1183.doi: 10.12118/j.issn.1000-6060.2018.06.05

基于统计降尺度方法的陕西省月气温预测分析

魏娜^1,2, 贺晨昕³, 刘佩佩⁴

1 西北大学城市与环境学院, 陕西省地表系统与环境承载力重点实验室, 陕西西安 710027;
2 陕西省气候中心, 陕西西安 710015;
3 云南农业大学, 云南昆明 650201;
4 陕西省安康市气象局气象台, 陕西西安 725600

收稿日期:2018-08-27 修回日期:2018-10-15 出版日期:2018-11-25
作者简介:魏娜(1981-),女,高级工程师,研究方向为气候预测与气候变化.E-mail:77230366@qq.com
基金资助:
陕西省科技自然研究基础项目（2017JM4027）；陕西省气象局预报员专项项目（2015Y-22）

Predictive analysis of monthly temperature in Shaanxi Province based on statistical downscaling method

WEI Na^1,2, HE Chen-xin³, LIU Pei-pei⁴

1 Institute of Earth Surface System and Hazards, College of Urban and Environmental Sciences, Northwest University, Xi'an 710127, Shaanxi China;
2 Shaanxi Climate Center, Xi'an 710015, Shaanxi, China;
3 Yunnan Agricultural University, Kunming 650201, Yunnan, China;
4 Ankang Meteorological Bureau, Ankang 725600, Shaanxi, China

Received:2018-08-27 Revised:2018-10-15 Online:2018-11-25

摘要/Abstract

摘要： 从短期气候预测的实际出发，针对月尺度的气温分县预测，使用逐步回归和主成分分析（即经验正交函数）的统计降尺度方法，利用地面观测站的气温资料、美国国家环境预报中心和美国大气科学研究中心的大尺度气候变量（NCEP/NCAR）和国家气候中心月动力延伸预报模式资料（DERF），对1982-2015年陕西省96个县区的1月和7月气温进行预测，建立统计降尺度模型，并采用交叉检验方法检验模型的预测效果，表明基于经验正交函数和逐步回归的统计降尺度方法在陕西省1月和7月气温的预测中是合理可用的。全省96个县区1月份预测值与观测值距平符号一致率大于60%达到了50个县区，7月份大于60%达到了60个县区。预测值可以较好的预测出气温变化趋势，但预测值变化幅度明显小于观测值。

关键词: 月气温, 统计降尺度, 气候变化, 预报模型

Abstract: At present,general circulation models (GCMs) can represent the main features of the global atmospheric circulation reasonably well,but their capability in reproducing regional scale climatic details is rather limited owing to their low-resolution.As a result,there is a need to develop tools of downscaling GCM predictions of climate change to regional and local scales.Statistical downscaling are the main tools for downscaling.Statistical downscaling method is to build relation between large-scale climate elements (atmospheric circulation index) and regional climate (temperature,precipitation).Then,this relationship is tested by independent observation data.Finally,this relation is applied in the large-scale climate model to output information and predict the change trend of regional climate.The dynamic extended range forecast (DERF) has good prediction capability which provided the monthly average of ensemble forecast.Many studies have been made to statistically downscale large scale climatic information to regional level in East China by use of DERF's data.This paper focuses on the research of statistical downscaling method and its application over Shaanxi Province,China.Statistical downscaling method based on stepwise linear regressions and common empirical orthogonal functions (EOF) is applied in this paper.It has two predictors in this research.One is the monthly temperature data obtained at 96 observation stations in January and July from 1982 to 2015,which is provided by Shaanxi Meteorological Administration.The other is climatic predictors which are derived from the NCEP/NCAR reanalysis data in January and July from 1961 to 2015,and the dynamic extended range forecast (DERF) product from National Climate Center of China were applied to predict temperature by using the forecast model and compared with the observed temperature.It is built prediction model between the large scale climate predictor and the observed temperature by use of statistical downscaling.The main results of this research can be summarized in the following items:EOF as for large scale climate main modal method and sea level pressure and altitude difference field as predictors are feasible;In January,the consistent anomaly symbol rate of simulated values and observed values greater than 60% reached 50 counties of the province's 96 counties,and the rate of most of the northern Shanbei,Guanzhong and the southern Shannan has reached 70% or so.In July,the consistent anomaly symbol rate of simulated values and observed values greater than 60% reached 60 counties of the province's 96 counties,and the symbol rate of Western Guanzhong and the southern Shannan reached 75% or so.

Key words: monthly temperature, statistical downscaling, climate change, prediction model

中图分类号:

P457.3

魏娜, 贺晨昕, 刘佩佩. 基于统计降尺度方法的陕西省月气温预测分析[J]. 干旱区地理, 2018, 41(6): 1178-1183.

WEI Na, HE Chen-xin, LIU Pei-pei. Predictive analysis of monthly temperature in Shaanxi Province based on statistical downscaling method[J]. 干旱区地理, 2018, 41(6): 1178-1183.

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