A framework for space–time modelling of rainfall events for hydrological applications of weather radar

Abstract

High-resolution rainfall fields are a crucial tool for many hydrological and hydrodynamic applications, including flood forecasting and urban drainage design. The aim of this study is to explore and exploit the space–time properties of rainfall using Fast-Fourier transforms, to provide a new method for the generation of high-resolution synthetic rainfall grids. These fields have realistic spatio-temporal properties, parametrised using historical radar rainfall events, matching the resolution of weather radar data (1km, 5 min), for events with a duration of 0.5–6 h. Utilising spectral random field theory, simulated rainfall fields are generated with a prescribed correlation structure, anisotropy, advection and marginal rainfall rate proportions and distributions. A model for rainfall generation is demonstrated, with an enriched model parameter sampling architecture using meaningful event clustering, based on space–time event properties. This model framework performs well at recreating short-duration spatio-temporal rainfall events, both visually and statistically. The extension of a clustered rainfall model allows for larger-scale sampling of synthetic event parameters, with specific rainfall event types. There are numerous potential uses for this rainfall model, such as design storms or test cases for applications of radar rainfall estimates. These include but are not limited to nowcasting, numerical weather prediction, flash flood forecasting and machine learning model training data generation.