A multi-scale space-time hybrid weather generator

Long term time series of meteorologic variables are generally lacking and is especially the case at sub-daily temporal resolutions. These time series are needed for applications such as hydrological modelling of catchments and derived flood frequency analyses. Stochastic weather generators are one such solution and are able to generate long synthetic time series of arbitrary length.

This study explores the coupling of an hourly space-time rainfall model with a non-parametric K-NN resampling approach for non-rainfall climate variables such as temperature, humidity and global radiation. A daily gridded observational climate dataset is used for the resampling. As a last step, a simple disaggregation technique is applied to the resampled non-rainfall climate variables to achieve an hourly timestep.

To study the effectiveness and performance of the hybrid weather generator, synthetic time series for 400 mesoscale catchments within Germany consisting of 700 rainfall stations were generated and compared to observations. Results show that the hybrid weather generator adequately reproduces observed statistics for rainfall and the non-rainfall climate variables in addition to maintaining cross correlations between the climate variables.