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Discussion papers
https://doi.org/10.5194/hess-2019-562
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-2019-562
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 25 Oct 2019

Submitted as: research article | 25 Oct 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Hydrology and Earth System Sciences (HESS).

Accounting for rain type non-stationarity in sub-daily stochastic weather generators

Lionel Benoit1, Mathieu Vrac2, and Gregoire Mariethoz1 Lionel Benoit et al.
  • 1Institute of Earth Surface Dynamics (IDYST), University of Lausanne, Lausanne, Switzerland
  • 2Laboratory for Sciences of Climate and Environment (LSCE-IPSL), CNRS/CEA/UVSQ, Orme des Merisiers, France

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.

Lionel Benoit et al.
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Short summary
At sub-daily resolution, rain intensity exhibits a strong variability in space and time due to the diversity of processes that produce rain (e.g. frontal storms, mesoscale convective systems, local convection). In this paper we explore two methods to simulate rain type time series conditional to meteorological covariates. Afterwards, we apply stochastic rain type simulation to the downscaling of Regional Climate Model precipitation.
At sub-daily resolution, rain intensity exhibits a strong variability in space and time due to...
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