17 Feb 2023
 | 17 Feb 2023
Status: this preprint is currently under review for the journal HESS.

A semi-parametric hourly space-time weather generator

Ross Pidoto and Uwe Haberlandt

Abstract. Long continuous time series of meteorological variables (i.e. rainfall, temperature and radiation) are required for applications such as derived flood frequency analyses. Observed time series are however generally too short, too sparse in space, or incomplete, especially at the sub-daily timestep.

Stochastic weather generators overcome this problem by generating time series of arbitrary length. This study presents a major revision to an existing space-time hourly rainfall model based on a point alternating renewal process, now coupled to a k-NN resampling model for conditioned simulation of non-rainfall climate variables.

The point based rainfall model is extended into space by the resampling of simulated rainfall events using a simulated annealing optimisation approach. Large station networks (N > 50) are now able to be modelled with no significant loss in the spatial dependence structure.

Modelling of non-rainfall climate variables, i.e. temperature, humidity and radiation, is achieved using a non-parametric knearest neighbour (k-NN) resampling approach, coupled to the space-time rainfall model via rainfall state. As input, a gridded daily observational dataset (HYRAS) was used. A final disaggregation step was then performed on all non-rainfall climate variables to achieve an hourly output temporal resolution.

The proposed weather generator was tested on 400 catchments of varying size (50–20,000 km²) across Germany, comprising 699 sub-daily rainfall recording stations. Results indicate no major loss of model performance with increasing catchment size, and a generally good reproduction of observed climate and rainfall statistics.

Ross Pidoto and Uwe Haberlandt

Status: open (until 28 Apr 2023)

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Ross Pidoto and Uwe Haberlandt

Ross Pidoto and Uwe Haberlandt


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Short summary
Long continuous time series of meteorological variables (i.e. rainfall, temperature) are required for the modelling of floods. Observed time series are generally too short or not available. Weather generators are models that reproduce observed weather time series. This study extends an existing station based rainfall model into space by enforcing observed spatial rainfall characteristics. To model other variables (i.e. temperuatre), the model is then coupled to a simple resampling approach.