气候变化领域集成服务门户(CCPortal): Identify optimal predictors of statistical downscaling of summer daily precipitation in China from three-dimensional large-scale variables

CCPortal

DOI	10.1016/j.atmosres.2019.03.022
	Identify optimal predictors of statistical downscaling of summer daily precipitation in China from three-dimensional large-scale variables
	Liu Y.; Feng J.; Shao Y.; Li J.
发表日期	2019
ISSN	0169-8095
起始页码	99
结束页码	113
卷号	224
英文摘要	Statistical downscaling (SD) of daily precipitation is a challenging task, and the identification of predictors is crucial for constructing SD models. This study focuses on identifying SD predictors for summer (June–September) daily precipitation in China. Six large-scale variables (LSVs) in ERA-Interim reanalysis were used to select predictors for 177 sites. For each site, the predictor identification was conducted by searching the grid box having the best correlation to precipitation in a three-dimensional way: across different grid boxes and multiple pressure levels. The result indicates that correlations are often sensitive to the pressure levels. Adjacent sites share similar spatial patterns of correlations, indicating regionally different physical relations between LSVs and precipitation. The predictor selection reasonably reflects the regional circulations related to precipitation. Twelve candidate predictors were used to train generalized linear models by least absolute shrinkage and selection operator (LASSO) algorithm. The validation indicates the models have generally high performance, and also shows relatively poor performance for the sites in North China, Northwest China, and Yunnan when compared to that in the east of China. The downscaled outputs can roughly reflect the annual variations of summer total precipitation and rainy days. Two experiments on the stationarity assumption of the models under different climate conditions were conducted, indicating that no areas/sites were found significantly violated the stationarity assumption. This study presents guidance on how to select suitable predictors for downscaling daily precipitation in different areas of China. © 2019
英文关键词	Generalized linear models; Grid box selection; Nash-Sutcliffe efficiency; Predictor selection; Statistical downscaling
语种	英语
scopus关键词	Regression analysis; Daily precipitations; Generalized linear model; Grid-box; Least absolute shrinkage and selection operators; Predictor selections; Regional circulation; Statistical downscaling; Total precipitation; Climate models; annual variation; climate prediction; correlation; downscaling; precipitation assessment; summer; three-dimensional modeling; China
来源期刊	Atmospheric Research
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/162149
作者单位	School of Resources and Environment, Henan Polytechnic University, Jiaozuo, Henan, China; Key Laboratory of Regional Climate-Environment Research for Temperate East Asia, Institute Atmospheric Physics, Chinese Academy of Sciences, China; School of Hydrology and Water Resources, Nanjing University of Information Science & Technology, China
推荐引用方式 GB/T 7714	Liu Y.,Feng J.,Shao Y.,et al. Identify optimal predictors of statistical downscaling of summer daily precipitation in China from three-dimensional large-scale variables[J],2019,224.
APA	Liu Y.,Feng J.,Shao Y.,&Li J..(2019).Identify optimal predictors of statistical downscaling of summer daily precipitation in China from three-dimensional large-scale variables.Atmospheric Research,224.
MLA	Liu Y.,et al."Identify optimal predictors of statistical downscaling of summer daily precipitation in China from three-dimensional large-scale variables".Atmospheric Research 224(2019).