Dealing with non-stationarity in sub-daily stochastic rainfall models

• Published in: Hydrology and earth system sciences Vol. 22; no. 11; pp. 5919 - 5933
• Main Authors: Benoit, Lionel, Vrac, Mathieu, Mariethoz, Gregoire
• Format: Journal Article
• Language: English
• Published: Katlenburg-Lindau Copernicus GmbH 19.11.2018; European Geosciences Union; Copernicus Publications
• Subjects: Applications; Atmospheric precipitations; Case studies; Computer simulation; Data processing; Environmental Sciences; Global Changes; Image classification; Machine Learning; Meteorological radar; Methodology; Modelling; Precipitation; Radar; Radar imaging; Rain; Rainfall; Rainfall models; Rainfall simulators; Statistical methods; Statistics; Stochasticity; Storms; Typing; Weather; Weather radar
• ISSN: 1607-7938; 1027-5606; 1607-7938
• DOI: 10.5194/hess-22-5919-2018

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.