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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.

•PREDICTOR (PREDICTing flooding impacts from cOnvective Rainfall) has been developed to improve the approach to forecasting the impacts of surface water flooding. PREDICTOR is a next generation decision-support tool that utilises the latest Met Office convective precipitation ensemble forecasting capabilities and Scotland’s National Flood Risk Assessment (NFRA) flood maps. •The Impact-based Forecasting (IbF) approach of PREDICTOR combines the likelihood ("the chance") of flood-producing rainfall (from the Met Office ensemble forecasts) and the potential impact (from NFRA) to produce "Flood Risk" forecasts. The precipitation forecast product used is the Best Short Range (BSR) ensemble from the Met Office (MOGREPS-UK). 15-minute precipitation accumulations are available, extending out to ~32 hours and issued 4 times a day with 24 ensemble members. The NFRA surface water flooding maps have been generated using design rainfall inputs from the Flood Estimation Handbook (FEH) plus outputs from a number of different flood modelling studies, and used to consider property and road impacts. •Neighbourhood or ‘in-vicinity’ post processing of precipitation forecasts is performed to calculate exceedance probability (or ensemble confidence) of the forecast rainfall that would lead to surface water flooding impacts. This is calculated on a 10km grid basis across Scotland to provide individual gridded risk assessments of the likelihood and impact of flooding. The web-based system has been successfully used by SEPA forecasters during 2023 and in partnership with Transport Scotland to assess the value of predicting the risk on the trunk road network.

Intraseasonal soil moisture variability has the potential to feed back onto the West Africa monsoon circulation through its influence on surface turbulent fluxes and planetary boundary-layer characteristics. Using satellite observations and an atmospheric reanalysis, we investigate intraseasonal soil moisture-atmosphere feedbacks triggered by large-scale dynamics within the West African monsoon. Surprisingly, even though the surface response across the Sahel to strong convection is short-lived (days) and precipitation accumulations are spatially and temporally heterogeneous, a coherent regional-scale surface response to intraseasonal variability is observed. This surface response then feeds back onto the West African monsoon circulation. For example, during a dry intraseasonal event, Sahelian surface soil moisture significantly decreases, which elevates surface temperatures by 1.5°C and shifts the monsoon circulation southward by approximately 1.5° latitude. During a wet event meanwhile, the surface moistens and cools which leads to a northward monsoon shift. Alongside a low-level wind response, the African Easterly Jet (AEJ) also responds to surface changes due to variations in the meridional temperature gradient. For example, an increased temperature gradient during a dry event intensifies and shifts the AEJ southward. The combined response of low-level monsoon westerlies and the AEJ impacts low-level shear and characteristics of strong convection. Elevated low-level shear during a dry event promotes an intensification of deep convection across southern West Africa. This study provides new insight into the sensitivity of the West African monsoon circulation to intraseasonal soil moisture feedbacks and encourages similar research in other regions. An improved understanding and model representation of soil moisture-atmosphere feedbacks has the potential to improve forecasts beyond daily timescales and enhance early warning systems.

In tropical convective climates, where numerical weather prediction of rainfall has high uncertainty, nowcasting provides essential alerts of extreme events several hours ahead. In principle, short-term prediction of intense convective storms could benefit from knowledge of the slowly-evolving land surface state in regions where soil moisture controls surface fluxes. Here we explore how near-real time (NRT) satellite observations of the land surface and convective clouds can be combined to aid early warning of severe weather in the Sahel on time scales of up to 12 hours. Using Land Surface Temperature (LST) as a proxy for soil moisture deficit, we characterise the state of the surface energy balance in NRT. We identify the most convectively-active parts of Mesoscale Convective Systems (MCSs) from spatial filtering of cloud-top temperature imagery. We find that predictive skill provided by LST data is maximised early in the rainy season, when soils are drier and vegetation less developed. Land-based skill in predicting intense convection extends well beyond the afternoon, with strong positive correlations between daytime LST and MCS activity persisting as far as the following morning in more arid conditions. For a Forecasting Testbed event during September 2021, we developed a simple technique to translate LST data into NRT maps quantifying the likelihood of convection based solely on land state. We used these maps in combination with convective features to nowcast the tracks of existing MCSs, and predict likely new initiation locations. This is the first time to our knowledge that nowcasting tools based principally on land observations have been developed. The strong sensitivity of Sahelian MCSs to soil moisture, in combination with MCS life times of typically 6-18 hours, opens up the opportunity for nowcasting of hazardous weather well beyond what is possible from atmospheric observations alone, and could be applied elsewhere in the semi-arid tropics.

•Flash flooding from intense rainfall frequently results in major damage and loss of life across Africa. Over the Sahel, intense rainfall from Mesoscale Convective Systems (MCSs) is the main driver of flash floods, with recent research showing that these have tripled in frequency over the last 35 years. This climate-change signal, combined with rapid urban expansion in the region, suggests that the socio-economic impacts of flash flooding will become more frequent and severe. Appropriate disaster preparedness, response, and resilience measures are required to manage this increasing risk. •The NFLICS (Nowcasting FLood Impacts of Convective storms in the Sahel) project has co-developed a prototype early warning system for Senegal, incorporating nowcasting of heavy rainfall likelihood and flood risk from MCSs at city and sub-national scales. This system uses remote sensed satellite data and has been developed in partnership with the national meteorological agency (ANACIM) to operate quickly in real-time. To identify convective activity, wavelet analysis is applied to Meteosat data on cloud-top temperature for historical periods (2004 to 2019) and for the start-time of a nowcast. Data on historical convective activity, conditioned on the present location and timing of observed convection, are used to produce probabilistic forecasts of convective activity out to six hours ahead. Verification against the convective activity analysis and the 24-hour raingauge accumulations over Dakar suggests that these probabilistic nowcasts provide useful information on the occurrence of convective activity. The highest skill (compared to nowcasts based solely on climatology) is obtained when the probability of convection is estimated over spatial scales between 100 and 200km, depending on the forecast lead-time considered. Furthermore, recent advances have included incorporation of land surface temperature anomalies to modify nowcast probabilities – this recognises that MCS evolution fa

This document is the Final Report for the “Rainfall and River Flow Ensemble Verification: Phase 2” (EnsVerP2) project forming part of the project “Improving confidence in Flood Guidance through verification of rainfall and river flow ensembles”. Forecasting the weather and floods is a challenging task and inherently uncertain. Acknowledging and accounting for the uncertainty in precipitation and flood forecasts has become increasingly important. This has partly been driven by the move of warning and guidance services to risk-based approaches that combine the likelihood of flooding with its potential impact on society and the environment. In the UK, such risk-based approaches underpin the National Severe Weather Warning Service delivered by the Met Office, and the Flood Guidance Statements produced by the Flood Forecasting Centre (FFC) and Scottish Flood Forecasting Service (SFFS). A standard approach to accounting for forecast uncertainty is to use ensemble methods. For a number of years, FFC and SFFS have used precipitation ensembles coupled with the national Grid-to-Grid (G2G) model of river flow to underpin the Flood Guidance Statement. However, the performance of the overall end-to-end ensemble precipitation and river flow forecasting system is currently not verified routinely. This ensemble verification information and evidence is essential: its absence can limit end-user confidence and inhibit full exploitation for flood-risk guidance. In addition, the local flood forecasting systems - used by the Environment Agency (EA), Scottish Environment Protection Agency (SEPA) and Natural Resources Wales (NRW) - are planned to transition to ensemble forecasting and will have similar requirements for verification information. A first step in addressing this operational gap has been to bring together existing expertise in meteorological and hydrological model performance assessment to design and develop a new, holistic Ensemble Verification Framework. Then to consider how

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.