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.
Comparative Study of Different Stochastic Weather Generators for Long-Term Climate Data Simulation
