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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]

This article presents a land–atmosphere case‐study for a single day during monsoon onset, incorporating data from a research aircraft, satellite products and model outputs. The unique aircraft observations reveal temperature and humidity contrasts of up to 5 K and 4 g/kg in the planetary boundary layer induced by spatial variations in soil moisture. Both antecedent rain and irrigation were found to be drivers of this atmospheric variability. There is also evidence of soil moisture‐induced mesoscale circulations above some surfaces. This is the first time such responses have been observed in situ over India. Soil moisture‐driven temperature anomalies are larger than those found in previous observational studies in the African Sahel. Moreover, irrigation in the region is extensive, unlike in the Sahel, and has a similar atmospheric effect to antecedent rainfall. This implies that historical changes in irrigation practices are likely to have had an important influence on mesoscale processes within the Indian monsoon. We also examine evidence linking soil moisture and cloud formation. Above wetter soils we observed a suppression of shallow cloud, whilst the initiation of deep convection occurred mostly in areas affected by wet–dry soil moisture boundaries. To investigate the impact of soil moisture heterogeneity on large‐scale wind flow, three model depictions of the day are assessed: the European Centre for Medium‐Range Weather Forecasts ERA‐Interim and ERA5 reanalyses, and a high‐resolution (1.5 km) simulation generated using the Indian National Centre for Medium Range Weather Forecasting regional convection‐permitting Unified Model. We find evidence indicating surface flux uncertainties in the models lead to ∼3.5 hPa anomalies in the monsoon trough. This does affect the simulation of monsoon circulation and rainfall. Better representation of mesoscale land–atmosphere coupling is likely to improve the depiction of convection within weather and climate models over India. [ABSTRACT FROM AUTHOR]

Mixing processes in deep precipitating cumulus clouds are investigated by tracking Lagrangian particles in a large-eddy simulation. The trajectories of particles are reconstructed and the thermodynamic properties of cloud air are studied using mixing diagrams. The trajectory analysis shows that the in-cloud mixing is entirely dominated by lateral entrainment and that there is no significant vertical mixing by downdrafts originating from cloud top. Yet the thermodynamic properties of the particles are located close to a line in the mixing diagrams, which appears to be consistent with two-point vertical mixing. An attempt is made to resolve this paradox using the buoyancy-sorting model of Taylor and Baker, but it is found that this model does not provide a full explanation for the location of particles in the mixing diagram. However, it is shown that the mixing-line behavior can be well understood from a simple analytically solvable model that uses a range of different lateral entrainment rates. Two further factors that determine the location of particles in the mixing diagram are identified: the removal of noncloudy air and precipitation effects. Finally, a thermodynamic argument is given that explains the absence of coherent downdrafts descending from cloud top. [ABSTRACT FROM AUTHOR]