气候变化领域集成服务门户(CCPortal): Intercomparison of machine learning methods for statistical downscaling: the case of daily and extreme precipitation

CCPortal

DOI	10.1007/s00704-018-2613-3
	Intercomparison of machine learning methods for statistical downscaling: the case of daily and extreme precipitation
	Vandal, Thomas; Kodra, Evan; Ganguly, Auroop R.
发表日期	2019-07-01
ISSN	0177-798X
EISSN	1434-4483
卷号	137 期号:1-2 页码:557-570
英文摘要	Statistical downscaling of Global Climate Models (GCMs) allows researchers to study local climate change effects decades into the future. A wide range of statistical models have been applied to downscaling GCMs but recent advances in machine learning have
学科领域	Meteorology & Atmospheric Sciences
语种	英语
WOS记录号	WOS:000475737500041
来源期刊	THEORETICAL AND APPLIED CLIMATOLOGY
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/82908
作者单位	Northeastern Univ, 360 Huntington Ave, Boston, MA 02115 USA
推荐引用方式 GB/T 7714	Vandal, Thomas,Kodra, Evan,Ganguly, Auroop R.. Intercomparison of machine learning methods for statistical downscaling: the case of daily and extreme precipitation[J],2019,137(1-2):557-570.
APA	Vandal, Thomas,Kodra, Evan,&Ganguly, Auroop R..(2019).Intercomparison of machine learning methods for statistical downscaling: the case of daily and extreme precipitation.THEORETICAL AND APPLIED CLIMATOLOGY,137(1-2),557-570.
MLA	Vandal, Thomas,et al."Intercomparison of machine learning methods for statistical downscaling: the case of daily and extreme precipitation".THEORETICAL AND APPLIED CLIMATOLOGY 137.1-2(2019):557-570.