DOI: 10.5194/hess-22-265-2018
Scopus记录号: 2-s2.0-85040635938
论文题名: An adaptive two-stage analog/regression model for probabilistic prediction of small-scale precipitation in France
作者: Chardon J ; , Hingray B ; , Favre A ; -C
刊名: Hydrology and Earth System Sciences
ISSN: 10275606
出版年: 2018
卷: 22, 期: 1 起始页码: 265
结束页码: 286
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
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. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/79430
Appears in Collections: 气候变化事实与影响
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作者单位: Univ. Grenoble Alpes, CNRS, IRD, Grenoble INP, IGE, Grenoble, France
Recommended Citation:
Chardon J,, Hingray B,, Favre A,et al. An adaptive two-stage analog/regression model for probabilistic prediction of small-scale precipitation in France[J]. Hydrology and Earth System Sciences,2018-01-01,22(1)