Preprints
https://doi.org/10.5194/egusphere-2024-26
https://doi.org/10.5194/egusphere-2024-26
30 Jan 2024
 | 30 Jan 2024

Assessing rainfall radar errors with an inverse stochastic modelling framework

Amy Charlotte Green, Chris G. Kilsby, and András Bárdossy

Abstract. Weather radar is a crucial tool in rainfall estimation, 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 the 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, imposing path-integrated attenuation effects, a stochastic drop-size distribution field, along with 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 the real-world case. The structure of these errors is then investigated, with a focus on frequency and behaviour of ‘rainfall shadows’. Half of simulated weather radar images have at least 3 % of significant rainfall rates shadowed and 25 % had at least 45 km2 containing rainfall shadows, resulting in underestimation of 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 at generating realistic weather radar images visually, for a large range of event types.

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Amy Charlotte Green, Chris G. Kilsby, and András Bárdossy

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2024-26', Anonymous Referee #1, 25 Mar 2024
    • AC1: 'Reply on RC1', Amy Green, 23 Apr 2024
  • RC2: 'Comment on egusphere-2024-26', Anonymous Referee #2, 25 Mar 2024
    • AC2: 'Reply on RC2', Amy Green, 23 Apr 2024
    • AC3: 'Figure 16', Amy Green, 23 Apr 2024
    • AC4: 'Figure 17', Amy Green, 23 Apr 2024
Amy Charlotte Green, Chris G. Kilsby, and András Bárdossy
Amy Charlotte Green, Chris G. Kilsby, and András Bárdossy

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Short summary
Weather radar is a crucial tool in rainfall estimation, but radar rainfall estimates are subject to many error sources, with the true rainfall field unknown. A flexible model for simulating errors relating to the radar rainfall estimation process is implemented, inverting standard processing methods. This flexible and efficient model performs well at generating realistic weather radar images visually, for a large range of event types.