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Surface water flooding (SWF) is a severe hazard associated with extreme convective rainfall, whose spatial and temporal sparsity belie 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 complementary 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, as well as 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The UK Flood Forecasting Centre, set up following the extensive flooding in 2007, is nearing its 10 year anniversary. High impact, low probability events have been a feature of the first 10 years of the Flood Forecasting Centre. Indeed, this has been the case for all natural sources of flooding, fluvial, surface water, coastal and groundwater. This period of flooding at historical extremes has included the wettest UK summer and winter on record, the most significant coastal flooding for a generation, the highest ever recorded groundwater levels and a conveyor of 12 storms which caused prolonged, widespread and severe flooding during the winter of 2013/14. Each event has shaped the development of the centre. Operations has influenced new science, which has in turn improved flood forecastsCollaboration and working between science and operations, and across disciplines is key to bridging and closing knowledge gaps. Our way of working has enabled us to rapidly improve the timeliness, accuracy and communication of flood forecasts. I will share some of our recent innovations and pipeline projects such as:• The Natural Hazards Partnership surface water hazard impact model (driven by the Met Office MOGREPS precipitation ensembles)• Object Orientated Nowcasting• Machine Learning Applications • Visualisation of crowd sourced data for rapid verification and potentially to issue short lead time warningsI will also highlight some of our current challenges that we would love to work with others to solve. [ABSTRACT FROM AUTHOR]