| Autor: |
Maybee, Ben, Birch, Cathryn E., Böing, Steven J., Willis, Thomas, Speight, Linda, Porson, Aurore N., Pilling, Charlie, Shelton, Kay L., Trigg, Mark A. |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| ISSN: |
1684-9981 |
| Popis: |
Surface water flooding (SWF) is a severe hazard associated with extreme convective rainfall, whose spatial and temporal sparsity belies the significant impacts it has on populations and infrastructure. Forecasting the intense convective rainfall that causes most SWF on the temporal and spatial scales required for effective flood forecasting remains extremely challenging. National scale flood forecasts are currently issued for the UK and are well regarded amongst flood responders, but there is a need for complimentary enhanced regional information. Here we present a novel SWF forecasting method, FOREWARNS (Flood fOREcasts for surface WAter at a RegioNal Scale), that aims to fill this gap in forecast provision. FOREWARNS compares reasonable worst-case rainfall from a neighbourhood-processed, convection-permitting ensemble forecast system against pre-simulated flood scenarios, issuing a categorical forecast of SWF severity. We report findings from a workshop structured around three historical flood events in Northern England, in which forecast users indicated they found the forecasts helpful and would use FOREWARNS to complement national guidance for action planning in advance of anticipated events. We also present results from objective verification of forecasts for 82 recorded flood events in Northern England from 2013–2022, and for 725 daily forecasts spanning 2019–2022, using a combination of flood records and precipitation proxies. We demonstrate that FOREWARNS offers good skill in forecasting SWF risk, with high spatial hit rates and low temporal false alarm rates, confirming that user confidence is justified, and that FOREWARNS would be suitable for meeting the user requirements of an enhanced operational forecast. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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