气候变化领域集成服务门户(CCPortal): Nonstationary stochastic rain type generation: Accounting for climate drivers

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DOI	10.5194/hess-24-2841-2020
	Nonstationary stochastic rain type generation: Accounting for climate drivers
	Benoit L.; Vrac M.; Mariethoz G.
发表日期	2020
ISSN	1027-5606
起始页码	2841
结束页码	2854
卷号	24 期号:5
英文摘要	At subdaily resolution, rain intensity exhibits a strong variability in space and time, which is favorably modeled 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 and local convection), which results in a multiplicity of space time patterns embedded into rain fields and in turn leads to the nonstationarity of rain statistics. To account for this nonstationarity in the context of stochastic weather generators and therefore preserve the relationships between rainfall properties and climatic drivers, we propose to resort to rain type simulation. In this paper, we develop a new approach based on multiple-point statistics to simulate rain type time series conditional to meteorological covariates. The rain type simulation method is tested by a cross-validation procedure using a 17-year-long rain type time series defined over central Germany. Evaluation results indicate that the proposed approach successfully captures the relationships between rain types and meteorological covariates. This leads to a proper simulation of rain type occurrence, persistence and transitions. After validation, 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 (Representative Concentration Pathway) 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. © 2020 Author(s).
语种	英语
scopus关键词	Climate models; Embedded systems; Stochastic systems; Storms; Time series; Emission scenario; Evaluation results; Mesoscale Convective System; Multiple-point statistics; Non-stationarities; Regional climate modeling; Stochastic approach; Stochastic weather generator; Rain; carbon emission; climate modeling; concentration (composition); precipitation intensity; rainfall; regional climate; time series; weather forecasting; Germany
来源期刊	Hydrology and Earth System Sciences
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/159390
作者单位	Benoit, L., Institute of Earth Surface Dynamics (IDYST), University of Lausanne, Lausanne, Switzerland; Vrac, M., Laboratory for Sciences of Climate and Environment (LSCE-IPSL), CNRS/CEA/UVSQ, Orme des Merisiers, France; Mariethoz, G., Institute of Earth Surface Dynamics (IDYST), University of Lausanne, Lausanne, Switzerland
推荐引用方式 GB/T 7714	Benoit L.,Vrac M.,Mariethoz G.. Nonstationary stochastic rain type generation: Accounting for climate drivers[J],2020,24(5).
APA	Benoit L.,Vrac M.,&Mariethoz G..(2020).Nonstationary stochastic rain type generation: Accounting for climate drivers.Hydrology and Earth System Sciences,24(5).
MLA	Benoit L.,et al."Nonstationary stochastic rain type generation: Accounting for climate drivers".Hydrology and Earth System Sciences 24.5(2020).