气候变化领域集成服务门户(CCPortal): An adaptive two-stage analog/regression model for probabilistic prediction of small-scale precipitation in France

CCPortal

DOI	10.5194/hess-22-265-2018
	An adaptive two-stage analog/regression model for probabilistic prediction of small-scale precipitation in France
	Chardon J.; Hingray B.; Favre A.-C.
发表日期	2018
ISSN	1027-5606
起始页码	265
结束页码	286
卷号	22 期号:1
英文摘要	Statistical downscaling models (SDMs) are often used to produce local weather scenarios from large-scale atmospheric information. SDMs include transfer functions which are based on a statistical link identified from observations between local weather and a set of large-scale predictors. As physical processes driving surface weather vary in time, the most relevant predictors and the regression link are likely to vary in time too. This is well known for precipitation for instance and the link is thus often estimated after some seasonal stratification of the data. In this study, we present a two-stage analog/regression model where the regression link is estimated from atmospheric analogs of the current prediction day. Atmospheric analogs are identified from fields of geopotential heights at 1000 and 500 hPa. For the regression stage, two generalized linear models are further used to model the probability of precipitation occurrence and the distribution of non-zero precipitation amounts, respectively. The two-stage model is evaluated for the probabilistic prediction of small-scale precipitation over France. It noticeably improves the skill of the prediction for both precipitation occurrence and amount. As the analog days vary from one prediction day to another, the atmospheric predictors selected in the regression stage and the value of the corresponding regression coefficients can vary from one prediction day to another. The model allows thus for a day-to-day adaptive and tailored downscaling. It can also reveal specific predictors for peculiar and non-frequent weather configurations. © Author(s) 2018.
语种	英语
scopus关键词	Forecasting; Probability distributions; Regression analysis; Scale (deposits); Generalized linear model; Geo-potential heights; Physical process; Probabilistic prediction; Regression coefficient; Statistical downscaling; Statistical links; Two stage model; Weather forecasting; downscaling; estimation method; precipitation assessment; prediction; probability; regression analysis; seasonal variation; France
来源期刊	Hydrology and Earth System Sciences
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/160152
作者单位	Chardon, J., Univ. Grenoble Alpes, CNRS, IRD, Grenoble INP, IGE, Grenoble, 38000, France; Hingray, B., Univ. Grenoble Alpes, CNRS, IRD, Grenoble INP, IGE, Grenoble, 38000, France; Favre, A.-C., Univ. Grenoble Alpes, CNRS, IRD, Grenoble INP, IGE, Grenoble, 38000, France
推荐引用方式 GB/T 7714	Chardon J.,Hingray B.,Favre A.-C.. An adaptive two-stage analog/regression model for probabilistic prediction of small-scale precipitation in France[J],2018,22(1).
APA	Chardon J.,Hingray B.,&Favre A.-C..(2018).An adaptive two-stage analog/regression model for probabilistic prediction of small-scale precipitation in France.Hydrology and Earth System Sciences,22(1).
MLA	Chardon J.,et al."An adaptive two-stage analog/regression model for probabilistic prediction of small-scale precipitation in France".Hydrology and Earth System Sciences 22.1(2018).