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Lookup NU author(s): Dr Amy Green, Professor Chris Kilsby, Professor Andras BardossyORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
Copyright © 2024 Amy C. Green et al.Weather radar is a crucial tool for rainfall observation and forecasting, providing high-resolution estimates in both space and time. Despite this, radar rainfall estimates are subject to many error sources - including attenuation, ground clutter, beam blockage and drop-size distribution - with the true rainfall field unknown. A flexible stochastic model for simulating errors relating to the radar rainfall estimation process is implemented, inverting standard weather radar processing methods and imposing path-integrated attenuation effects, a stochastic drop-size-distribution field, and sampling and random errors. This can provide realistic weather radar images, of which we know the true rainfall field and the corrected "best-guess"rainfall field which would be obtained if they were observed in a real-world case. The structure of these errors is then investigated, with a focus on the frequency and behaviour of "rainfall shadows". Half of the simulated weather radar images have at least 3 % of their significant rainfall rates shadowed, and 25 % have at least 45 km2 containing rainfall shadows, resulting in underestimation of the potential impacts of flooding. A model framework for investigating the behaviour of errors relating to the radar rainfall estimation process is demonstrated, with the flexible and efficient tool performing well in generating realistic weather radar images visually for a large range of event types.
Author(s): Green AC, Kilsby C, Bardossy A
Publication type: Article
Publication status: Published
Journal: Hydrology and Earth System Sciences
Year: 2024
Volume: 28
Issue: 20
Pages: 4539-4558
Online publication date: 23/10/2024
Acceptance date: 29/08/2024
Date deposited: 18/11/2024
ISSN (print): 1027-5606
ISSN (electronic): 1607-7938
Publisher: Copernicus Publications
URL: https://doi.org/10.5194/hess-28-4539-2024
DOI: 10.5194/hess-28-4539-2024
Data Access Statement: The underlying software code is publicly available in RadErr available on GitHub (https://github.com/amyycb/ raderr) (Green, 2024). The underlying research data were generated using methods in Green et al. (2024) parameterized using data from the NIMROD dataset (http://catalogue.ceda.ac.uk/uuid/ 82adec1f896af6169112d09cc1174499/, Met Office, 2003).
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