%0 Journal Article %1 kallache2011nonstationary %A Kallache, M. %A Vrac, M. %A Naveau, P. %A Michelangeli, P.-A. %D 2011 %J Journal of Geophysical Research: Atmospheres %K downscaling nonstationarity original precipitation %N D5 %P n/a--n/a %R 10.1029/2010JD014892 %T Nonstationary probabilistic downscaling of extreme precipitation %U http://dx.doi.org/10.1029/2010JD014892 %V 116 %X Reanalysis data and general circulation model outputs typically provide information at a coarse spatial resolution, which cannot directly be used for local impact studies. Downscaling methods have been developed to overcome this problem, and to obtain local-scale information from large-scale atmospheric variables. The deduction of local-scale extremes still is a challenge. Here a probabilistic downscaling approach is presented where the cumulative distribution functions (CDFs) of large- and local-scale extremes are linked by means of a transfer function. In this way, the CDF of the local-scale extremes is obtained for a projection period, and statistical characteristics, like return levels, are inferred. The input series are assumed to be distributed according to an extreme value distribution, the Generalized Pareto distribution (GPD). The GPD parameters are linked to further explanatory variables, hence defining a nonstationary model. The methodology (XCDF-t) results in a parametric CDF, which is as well a GPD. Realizations generated from this CDF provide confidence bands. The approach is applied to downscale National Centers for Environmental Prediction reanalysis precipitation in winter. Daily local precipitation at five stations in southern France is obtained. The calibration period 1951–1985 is used to infer precipitation over the validation period 1986–1999. The applicability of the approach is verified by using observations, quantile-quantile plots, and the continuous ranked probability score. The stationary XCDF-t approach shows good results and outperforms the nonparametric CDF-t approach or quantile mapping for some stations. The inclusion of covariate information improves results only sometimes; therefore, covariates have to be chosen with care.

@article{kallache2011nonstationary, abstract = {Reanalysis data and general circulation model outputs typically provide information at a coarse spatial resolution, which cannot directly be used for local impact studies. Downscaling methods have been developed to overcome this problem, and to obtain local-scale information from large-scale atmospheric variables. The deduction of local-scale extremes still is a challenge. Here a probabilistic downscaling approach is presented where the cumulative distribution functions (CDFs) of large- and local-scale extremes are linked by means of a transfer function. In this way, the CDF of the local-scale extremes is obtained for a projection period, and statistical characteristics, like return levels, are inferred. The input series are assumed to be distributed according to an extreme value distribution, the Generalized Pareto distribution (GPD). The GPD parameters are linked to further explanatory variables, hence defining a nonstationary model. The methodology (XCDF-t) results in a parametric CDF, which is as well a GPD. Realizations generated from this CDF provide confidence bands. The approach is applied to downscale National Centers for Environmental Prediction reanalysis precipitation in winter. Daily local precipitation at five stations in southern France is obtained. The calibration period 1951–1985 is used to infer precipitation over the validation period 1986–1999. The applicability of the approach is verified by using observations, quantile-quantile plots, and the continuous ranked probability score. The stationary XCDF-t approach shows good results and outperforms the nonparametric CDF-t approach or quantile mapping for some stations. The inclusion of covariate information improves results only sometimes; therefore, covariates have to be chosen with care.}, added-at = {2013-01-10T15:54:47.000+0100}, author = {Kallache, M. and Vrac, M. and Naveau, P. and Michelangeli, P.-A.}, biburl = {https://www.bibsonomy.org/bibtex/298f47dd93d07b1391e62e64feebb9240/marsianus}, description = {Nonstationary probabilistic downscaling of extreme precipitation - Kallache - 2011 - Journal of Geophysical Research: Atmospheres (1984–2012) - Wiley Online Library}, doi = {10.1029/2010JD014892}, interhash = {462d02e54c512971690ab226ea252868}, intrahash = {98f47dd93d07b1391e62e64feebb9240}, issn = {2156-2202}, journal = {Journal of Geophysical Research: Atmospheres}, keywords = {downscaling nonstationarity original precipitation}, number = {D5}, pages = {n/a--n/a}, timestamp = {2013-01-14T15:57:20.000+0100}, title = {Nonstationary probabilistic downscaling of extreme precipitation}, url = {http://dx.doi.org/10.1029/2010JD014892}, volume = 116, year = 2011 }