DOI: 10.5194/hess-22-5919-2018
论文题名: Dealing with non-stationarity in sub-daily stochastic rainfall models
作者: Benoit L. ; Vrac M. ; Mariethoz G.
刊名: Hydrology and Earth System Sciences
ISSN: 1027-5606
出版年: 2018
卷: 22, 期: 11 起始页码: 5919
结束页码: 5933
语种: 英语
Scopus关键词: Meteorological radar
; Rain
; Stochastic systems
; High resolution
; Model parameters
; Non-stationarities
; Objective criteria
; Stochastic rainfalls
; Stochastic weather generator
; Synthetic rainfalls
; Unsupervised classification
; Stochastic models
; homogeneity
; parameterization
; precipitation assessment
; quantitative analysis
; rainfall
; satellite imagery
; standing wave
; statistical analysis
; stochasticity
; unsupervised classification
英文摘要: Understanding the stationarity properties of rainfall is critical when using stochastic weather generators. Rainfall stationarity means that the statistics being accounted for remain constant over a given period, which is required for both inferring model parameters and simulating synthetic rainfall. Despite its critical importance, the stationarity of precipitation statistics is often regarded as a subjective choice whose examination is left to the judgement of the modeller. It is therefore desirable to establish quantitative and objective criteria for defining stationary rain periods. To this end, we propose a methodology that automatically identifies rain types with homogeneous statistics. It is based on an unsupervised classification of the space-time-intensity structure of weather radar images. The transitions between rain types are interpreted as non-stationarities. Our method is particularly suited to deal with non-stationarity in the context of sub-daily stochastic rainfall models. Results of a synthetic case study show that the proposed approach is able to reliably identify synthetically generated rain types. The application of rain typing to real data indicates that non-stationarity can be significant within meteorological seasons, and even within a single storm. This highlights the need for a careful examination of the temporal stationarity of precipitation statistics when modelling rainfall at high resolution. © Author(s) 2018. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/163136
Appears in Collections: 气候变化与战略
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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
Recommended Citation:
Benoit L.,Vrac M.,Mariethoz G.. Dealing with non-stationarity in sub-daily stochastic rainfall models[J]. Hydrology and Earth System Sciences,2018-01-01,22(11)