Articles | Volume 24, issue 5
https://doi.org/10.5194/hess-24-2841-2020
https://doi.org/10.5194/hess-24-2841-2020
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

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Manuscript not accepted for further review
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Cited articles

Ailliot, P., Allard, D., Monbet, V., and Naveau, P.: Stochastic weather generators: an overview of weather type models, Journal de la société française de statistiques, 156, 101–113, 2015. a
Bárdossy, A. and Pegram, G. G. S.: Space-time conditional disaggregation of precipitation at high resolution via simulation, Water Resour. Res., 52, 920–937, https://doi.org/10.1002/2015WR018037, 2016. a
Bárdossy, A. and Plate, E. J.: Modelling daily rainfall using a semi-Markov representation of circulation pattern occurence, J. Hydrol., 122, 33–47, https://doi.org/10.1016/0022-1694(91)90170-M, 1991. a
Benoit, L.: Rain type data over Thuringia for the period 2001–2017, available at: https://github.com/LionelBenoit/Stochastic_Raintype_Generator/Raintype_data (last access: 27 May 2020), 2020a. a
Benoit, L.: Rain typing software, available at: https://github.com/LionelBenoit/Rain_typing (last access: 27 May 2020), 2020b. a
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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.