Nonstationary probabilistic downscaling of extreme precipitation

Abstract

[1] 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.

Statistical adaptation of ALADIN RCM outputs over the French Alps – application to future climate and snow cover

“…However, an important issue concerning this kind of correction, which is common to many other downscaling studies (Martin et al, 1997;Kallache et al, 2011), is the question of its validity in the future, despite climate change. The underlying idea is that, since the relative frequency of weather types might change in a changing climate, the resulting weather variable distribution may change.…”

“…This approach is often used to correct RCM bias (e.g. Boé et al, 2007;Alpert et al, 2008;Kallache et al, 2011). Here, for each SAFRAN variable, a seasonal correction is applied to each quantile of the distribution according to the massif and elevation.…”

“…They are also based on the hypothesis that the statistical link existing between the spatial scales in the current climate will remain valid for future changed climatic conditions. Despite these limitations, statistical methods have been extensively used in the Alps to work on specific months (Frey-Buness et al, 1995), reconstruct recent temperature evolution (Kettle and Thompson, 2004), obtain precipitation scenarios (Schmidli et al, 2007) or study snow cover (Martin et al, 1997).…”

North American extreme temperature events and related large scale meteorological patterns: a review of statistical methods, dynamics, modeling, and trends

“…Ideally, quantile mapping for the extreme tails of the temperature distribution should use statistical methods based on extreme value theory (e.g., Coles 2001). Kallache et al (2011) proposed such a method, in which the quantile mapping involves fitting the Generalized Pareto distribution (GPD) to the upper (or lower) tail of the distribution. Although their application was to precipitation extremes, the technique should apply equally well to temperature extremes.…”

A bias-corrected CMIP5 dataset for Africa using the CDF-t method – a contribution to agricultural impact studies

“…The bias correction has been performed using the CDF-t method (Michelangeli et al, 2009), a method that has been widely used and validated for various variables and in various contexts (e.g. Kallache et al, 2011;Vrac et al, 2012;Lavaysse et al, 2012;Vautard et al, 2013;Vrac and Friederichs, 2015;Vrac et al, 2016), including tropical areas (Oettli et al, 2011;Vigaud et al, 2013), but not Africa. These corrections have been applied to 29 GCMs over the 1950-2005period and RCP2.6, RCP4.5, and RCP8.5 2006-2099 projections.…”