Nonstationary stochastic rain type generation:  accounting for climate drivers

Benoit, Lionel; Vrac, Mathieu; Mariethoz, Gregoire

doi:https://doi.org/10.5194/hess-24-2841-2020

Articles | Volume 24, issue 5

https://doi.org/10.5194/hess-24-2841-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/hess-24-2841-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 24, issue 5

Research article

|

29 May 2020

Research article |

| 29 May 2020

Nonstationary stochastic rain type generation: accounting for climate drivers

Lionel Benoit, Mathieu Vrac, and Gregoire Mariethoz

Supplement

https://doi.org/10.5194/hess-24-2841-2020-supplement

Data sets

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

Rain type data over Thuringia for the period 2001–2017 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

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.