Reparametrizing rainfall generators with convective-permitting models to generate high-resolution rainfall for climate impact studies

A high-resolution rainfall data at a km and sub-hourly scales provides a powerful tool for hydrological risk assessment in the current and the future climate. Global circulation models or regional circulation models generally provide projections at much coarser space-time resolutions of 10-100 kilometres and daily to monthly. In the recent decade, convection-permitting models (CPM) have been developed and enable the projection at a kilometre and sub-hourly scales. CPMs, due to their very high computational demand, are still limited to a small number of ensemble simulations. This limits their skill in hydrology, where quantification of extremes and their variability is essential for risk assessment and design. In this project, we propose the combined use of CPMs with stochastic rainfall generators to simulate ensemble of climate change at hydrologically relevant scales.

To achieve this, we used the STREAP space-time stochastic rainfall generator, a 1 km resolution composite rain radar data and a 2.2km CPM dataset from the UK Met Office. For the mid-land region of the UK, we parameterised STREAP for the present climate using rainfall observations. CPM simulations were used to derive the change of STREAP parameters with a changing climate. These parameters describe the change in weather patterns, the rainfall intensification, and changes in the structure of rainfall. Our results show that by combining a physics-based model and a stochastic weather generator we can simulate robust ensemble of rainfall at a minimal computational cost while preserving all physical attributes from climate change projections.