Abstract
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 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.
Highlights
Stochastic rainfall models are statistical models that aim at simulating realistic random rains
Results show that H1 tends to slightly underestimate the cumulative rain due to an underestimation of very high intensities. This underestimation of heavy rainfall is common to all the three cases and probably originates from the stochastic model itself, which is not designed to handle extreme rainfall due to the simple transform function selected in Eq (4). This could be improved by adopting a transform function accounting for extreme rainfall but at the price of a more complex parametrization, which is not regarded as essential here because the observed rain rates are mostly low to moderate, and only the 99th centile is affected by rain rate underestimation
This is due to the overestimation of moderate rain rates that compensates for the underestimation of extremely high values
Summary
Stochastic rainfall models are statistical models that aim at simulating realistic random rains. For this purpose, they generate rainfall simulations which reproduce, in a distributional sense, a set of key rainfall statistics derived from an observation dataset (Benoit and Mariethoz, 2017). The practical interest of stochastic rainfall models is notably to complement numerical weather models for the simulation of rainfall heterogeneity at fine scales, and to quantify the uncertainty associated with rainfall reconstructions. Numerical weather models face challenges for reproducing rainfall heterogeneity in space and time, in particular at fine scales (Bauer et al, 2015; Bony et al, 2015). The target is the transposition of observed rain statistics into synthetic rain simulations
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