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Flash flooding from intense rainfall frequently results in major damage and loss of life across Africa. In the Sahel, automatic prediction and warning systems for these events, driven by Mesoscale Convective Systems (MCSs), are limited, and Numerical Weather Prediction (NWP) forecasts continue to have little skill. The ground observation network is also sparse, and very few operational meteorological radars exist to facilitate conventional nowcasting approaches. Focusing on the western Sahel, we present a novel approach for producing probabilistic nowcasts of convective activity out to six hours ahead, using the current location of observed convection. Convective parts of the MCS, associated with extreme and heavy precipitation, are identified from 16 years of Meteosat Second Generation thermal–infrared cloud-top temperature data, and an offline database of location-conditioned probabilities calculated. From this database, real-time nowcasts can be quickly produced with minimal calculation. The nowcasts give the probability of convection occurring within a square neighbourhood surrounding each grid point, accounting for the inherent unpredictability of convection at small scales. Compared to a climatological reference, formal verification approaches show the nowcasts to be skilful at predicting convective activity over the study region, for all times of day and out to the six-hour lead time considered. The nowcasts are also skilful at capturing extreme 24-hour rain gauge accumulations over Dakar, Senegal. The nowcast skill peaks in the afternoon, with a minimum in the evening. We find that the optimum neighbourhood size varies with lead time, from 10 km at the nowcast origin to around 100 km at a six-hour lead time. This simple and skilful nowcasting method could be highly valuable for operational warnings across West Africa and other regions with long-lived thunderstorms, and help to reduce the impacts from heavy rainfall and flooding.

The intensity of cosmic ray neutrons is inversely correlated with the amount of water present in the surrounding environment. This effect is already employed by around 50 neutron sensors in the COSMOS‐UK network to provide daily estimates of soil moisture across the UK. Here, these same sensors are used to automatically provide estimates of Snow Water Equivalent (SWE). Lying snow is typically ephemeral and of shallow depth for most parts of the UK. Moreover, soil moisture is usually high and variable, which acts to increase uncertainties in the SWE estimate. Nevertheless, even under such challenging conditions, both above ground and buried cosmic ray neutron sensors are still able to produce potentially useful SWE estimates. Triple collocation analysis suggests typical uncertainties of less than around 4mm under UK snow conditions.

During surface water flooding events, emergency responders require detailed information on the risks posed in order to provide an appropriate and effective response. Few early warning systems quantitatively estimate the risk and impacts of surface water flooding. Improvements in computational processing capability, availability of new datasets and developments in forecasting models means that the forecasting information currently being supplied by the Flood Forecasting Centre can be improved upon through the application of a timely, impact‐based model. This article presents a novel approach to collating receptor datasets into a pre‐calculated Impact Library for use in a Hazard Impact Model (HIM) that will operate using real‐time probabilistic rainfall and surface runoff forecasts for England and Wales. The HIM provides an approach suitable for modelling flood impacts. Initial results are presented for a case study covering the 2012 floods in the North East of England. Information generated by the HIM provides additional benefits beyond current methods. Features include operator access to 1 km 15 min spatial–temporal data, analysis of individual impact criteria and modular refinement of the Impact Library to suit different situations. The HIM has been developed in partnership via the Natural Hazards Partnership.

Surface water flooding (SWF) is a recurrent hazard that affects lives and livelihoods. Climate change is projected to change the frequency of extreme rainfall events that can lead to SWF. Increasingly, data from Regional Climate Models (RCMs) are being used to investigate the potential water‐related impacts of climate change; such assessments often focus on broad‐scale fluvial flooding and the use of coarse resolution (>12 km) RCMs. However, high‐resolution (<4 km) convection‐permitting RCMs are now becoming available that allow impact assessments of more localised SWF to be made. At the same time, there has been an increasing demand for more robust and timely real‐time forecast and alert information on SWF. In the UK, a real‐time SWF Hazard Impact Model framework has been developed. The system uses 1‐km gridded surface runoff estimates from a hydrological model to simulate the SWF hazard. These are linked to detailed inundation model outputs through an Impact Library to assess impacts on property, people, transport, and infrastructure for four severity levels. Here, a set of high‐resolution (1.5 km and 12 km) RCM data has been used as input to a grid‐based hydrological model over southern Britain to simulate Current (1996–2009) and Future (~2100s; RCP8.5) surface runoff. Counts of threshold‐exceedance for surface runoff and precipitation (at 1‐, 3‐ and 6‐hr durations) are analysed. Results show that the percentage increases in surface runoff extremes, are less than those of precipitation extremes. The higher‐resolution RCM simulates the largest percentage increases, which occur in winter, and the winter exceedance counts are greater than summer exceedance counts. For property impacts, the largest percentage increases are also in winter; however, it is the 12‐km RCM output that leads to the largest percentage increase in impacts. The added‐value of high‐resolution climate model data for hydrological modelling is from capturing the more intense convective storms in s

Existing surface water flood forecasting methods in Scotland are based on indicative depth-duration rainfall thresholds with limited understanding of the likelihood of inundation or associated impacts. There is a requirement to develop innovative risk-based solutions to improve surface water forecasting capabilities in support of improved flood resilience in urban centres. A new model was developed for Glasgow linking 24 hour ensemble rainfall predictions from MOGREPS-UK with static flood risk maps through the Grid-to-Grid hydrological model. The model was used operationally by the Scottish Flood Forecasting Service during the 2014 Commonwealth Games to provide bespoke surface water flooding guidance to responders. The operational trial demonstrated the benefits of being able to provide targeted information on real-time surface water flood risk. It also identified the high staff resource requirement to support the service due to the greater uncertainty in surface water flood forecasting compared to established fluvial and coastal methods.

On the 8 January 2005 the city of Carlisle in northwest England was severely flooded following two days of almost continuous rain over the nearby hills. Orographic enhancement of the rain through the seeder-feeder mechanism led to the very high rainfall totals. This paper shows the impact of running the Met Office Unified Model (UM) with a grid spacing of 4 and 1 km compared to the 12 km available at the time of the event. These forecasts, and forecasts from the Nimrod nowcasting system, were fed into the Probability Distributed Model (PDM) to predict river flow at the outlets of two catchments important for flood warning. The results show the benefit of increased resolution in the UM, the benefit of coupling the high-resolution rainfall forecasts to the PDM and the improvement in timeliness of flood warning that might have been possible.