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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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HESS | Articles | Volume 24, issue 5
Hydrol. Earth Syst. Sci., 24, 2841–2854, 2020
https://doi.org/10.5194/hess-24-2841-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Hydrol. Earth Syst. Sci., 24, 2841–2854, 2020
https://doi.org/10.5194/hess-24-2841-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 29 May 2020

Research article | 29 May 2020

Nonstationary stochastic rain type generation: accounting for climate drivers

Lionel Benoit et al.

Data sets

Radolan radar products DWD &ndash; Deutcher Wetterdienst https://opendata.dwd.de/climate_environment/CDC/grids_germany/5_minutes/radolan

Rain type data over Thuringia for the period 2001&ndash;2017 L. Benoit https://github.com/LionelBenoit/Stochastic_Raintype_Generator/Raintype_data

Model code and software

Rain typing software L. Benoit https://github.com/LionelBenoit/Rain_typing

Stochastic rainfall generator software L. Benoit https://github.com/LionelBenoit/Stochastic_Raintype_Generator/codes

G2S: The GeoStatistical Server M. Gravey https://github.com/GAIA-UNIL/G2S

Publications Copernicus
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Short summary
At subdaily 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 and local convection). In this paper we explore a new method to simulate rain type time series conditional to meteorological covariates. Afterwards, we apply stochastic rain type simulation to the downscaling of precipitation of a regional climate model.
At subdaily resolution, rain intensity exhibits a strong variability in space and time due to...
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