Temporal Statistical Downscaling of Precipitation and Temperature Forecasts Using a Stochastic Weather Generator  

作　　者：Yongku KIM Balaji RAJAGOPALAN GyuWon LEE 

机构地区：[1]Department of Statistics, Kyungpook National University [2]Department of Civil, Environmental and Architectural Engineering, University of Colorado [3]Department of Astronomy and Atmospheric Sciences, Center for Atmospheric Remote Sensing,Kyungpook National University

出　　处：《Advances in Atmospheric Sciences》2016年第2期175-183,共9页大气科学进展（英文版）

基　　金：supported by Korea Institute of Civil Engineering and Building Technology (Project name: 2015 Development of a micro raingauge using electromagnetic wave)

摘　　要：Statistical downscaling is based on the fact that the large-scale climatic state and regional/local physiographic features control the regional climate. In the present paper, a stochastic weather generator is applied to seasonal precipitation and temperature forecasts produced by the International Research Institute for Climate and Society （IRI）. In conjunction with the GLM （generalized linear modeling） weather generator, a resampling scheme is used to translate the uncertainty in the seasonal forecasts （the IRI format only specifies probabilities for three categories： below normal, near normal, and above normal） into the corresponding uncertainty for the daily weather statistics. The method is able to generate potentially useful shifts in the probability distributions of seasonally aggregated precipitation and minimum and maximum temperature, as well as more meaningful daily weather statistics for crop yields, such as the number of dry days and the amount of precipitation on wet days. The approach is extended to the case of climate change scenarios, treating a hypothetical return to a previously observed drier regime in the Pampas.Statistical downscaling is based on the fact that the large-scale climatic state and regional/local physiographic features control the regional climate. In the present paper, a stochastic weather generator is applied to seasonal precipitation and temperature forecasts produced by the International Research Institute for Climate and Society （IRI）. In conjunction with the GLM （generalized linear modeling） weather generator, a resampling scheme is used to translate the uncertainty in the seasonal forecasts （the IRI format only specifies probabilities for three categories： below normal, near normal, and above normal） into the corresponding uncertainty for the daily weather statistics. The method is able to generate potentially useful shifts in the probability distributions of seasonally aggregated precipitation and minimum and maximum temperature, as well as more meaningful daily weather statistics for crop yields, such as the number of dry days and the amount of precipitation on wet days. The approach is extended to the case of climate change scenarios, treating a hypothetical return to a previously observed drier regime in the Pampas.

关 键 词：generalized linear model seasonal projection stochastic weather generator temporal statistical downscaling 

分 类 号：P457.3[天文地球—大气科学及气象学] P426.6