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

Lookup NU author(s): Dr Amy Green, Professor Chris Kilsby, Professor Andras BardossyORCiD

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


Abstract

© 2024 The Author(s). 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.


Publication metadata

Author(s): Green AC, Kilsby C, Bardossy A

Publication type: Article

Publication status: Published

Journal: Journal of Hydrology

Year: 2024

Volume: 630

Print publication date: 01/02/2024

Online publication date: 26/01/2024

Acceptance date: 26/11/2023

Date deposited: 19/02/2024

ISSN (print): 0022-1694

Publisher: Elsevier BV

URL: https://doi.org/10.1016/j.jhydrol.2024.130630

DOI: 10.1016/j.jhydrol.2024.130630

Data Access Statement: Code for generating simulated events is available on GitHub (https://github.com/amyycb/simradrain). Weather radar data used for event parameterisation is from the Met Office NIMROD system, openly available on the Centre for Environmental Data Analysis (CEDA) archive.


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Funding

Funder referenceFunder name
NE/M009009/1
NERC

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