Abstract. At sub-daily resolution, rain intensity exhibits a strong variability in space and time, which is favorably modelled using stochastic approaches. This strong variability is further enhanced because of the diversity of processes that produce rain (e.g. frontal storms, mesoscale convective systems, local convection), which results in a multiplicity of space-time patterns embedded into rain fields, and in turn leads to non-stationarity of rain statistics. To account for this non-stationarity in the context of stochastic weather generators, and therefore preserve the climatological coherence of rain simulations, we propose to resort to rain types simulation. We explore two methods to simulate rain type time series conditional to meteorological covariates: a parametric approach based on a non-homogeneous semi-Markov chain, and a non-parametric approach based on multiple-point statistics. Both methods are tested by cross-validation using a 17-year long rain type time series defined over central Germany. Evaluation results indicate that the non-parametric approach better simulates the relationships between rain types and meteorological covariates. Indeed, the inherent simplifications in the parametric model do not allow fully resolving complex and non-linear interactions between the rainfall statistics and meteorological covariates. The proposed approach is applied to generate rain type time series conditional to meteorological covariates simulated by a Regional Climate Model under an RCP8.5 emission scenario. Results indicate that, by the end of the century, the distribution of rain types could be modified over the area of interest, with an increased frequency of convective- and frontal-like rains at the expense of more stratiform events.
Spectral Decompositions of Multiple Time Series: A Bayesian Non-parametric Approach