Journal cover Journal topic
Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
https://doi.org/10.5194/hess-2017-62
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Research article
15 Feb 2017
Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Hydrology and Earth System Sciences (HESS).
An adaptive two-stage analog/regression model for probabilistic prediction of local precipitation in France
Jérémy Chardon, Benoit Hingray, and Anne-Catherine Favre Univ. Grenoble Alpes, CNRS, IRD, Grenoble INP, IGE, F-38000 Grenoble, France
Abstract. Statistical Downscaling Methods (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 generating surface weather vary in time, the most relevant predictors and the regression link are likely to also vary in time. 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 hybrid model where the regression link is estimated from atmospheric analogs of the current prediction day. Atmospheric analogs are first identified from geopotential fields 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 hybrid model is evaluated for the probabilistic prediction of local precipitation over France. It noticeably improves the skill of the prediction for both precipitation occurrence and quantity. 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 vary from one prediction day to another. The hybrid approach allows thus for a day-to-day adaptive and tailored downscaling. It can also reveal specific predictors for peculiar and non-frequent weather configurations.

Citation: Chardon, J., Hingray, B., and Favre, A.-C.: An adaptive two-stage analog/regression model for probabilistic prediction of local precipitation in France, Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2017-62, in review, 2017.
Jérémy Chardon et al.
Jérémy Chardon et al.
Jérémy Chardon et al.

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Short summary
We present a hybrid Statistical Downscaling Model for the probabilistic prediction of local precipitation, where the downscaling statistical link is estimated from atmospheric circulation analogs of the current prediction day. The model allows for a day-to-day adaptive and tailored downscaling. It can reveal specific predictors for peculiar and non-frequent weather configurations. This approach noticeably improves the skill of the prediction for both precipitation occurrence and quantity.
We present a hybrid Statistical Downscaling Model for the probabilistic prediction of local...
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