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Discussion papers | Copyright
https://doi.org/10.5194/hess-2018-273
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 28 May 2018

Research article | 28 May 2018

Review status
This discussion paper is a preprint. A revision of the manuscript is under review for the journal Hydrology and Earth System Sciences (HESS).

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

Lionel Benoit1, Mathieu Vrac2, and Gregoire Mariethoz1 Lionel Benoit et al.
  • 1Institute of Earth Surface Dynamics (IDYST), University of Lausanne, Lausanne, Switzerland
  • 2Laboratory for Sciences of Climate and Environment (LSCE-IPSL), CNRS/CEA/UVSQ, Orme des Merisiers, France

Abstract. 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 modeler. 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.

Lionel Benoit et al.
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Latest update: 17 Oct 2018
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Short summary
We propose a method for unsupervised classification of the space–time–intensity structure of weather radar images. The resulting classes are interpreted as rain types, i.e. pools of rain fields with homogeneous statistical properties. Such rain types can in turn be used to define stationary periods for further stochastic rainfall modelling. The application of rain typing to real data indicates that non-stationarity can be significant within meteorological seasons, and even within a single storm.
We propose a method for unsupervised classification of the space–time–intensity structure of...
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