There is a great demand to improve predictions of high‐impact weather across the African continent. This is because of the high frequency of intense convective storms that often produce severe flooding, strong winds and lightning, combined with the vulnerability of people, infrastructure and businesses to such hazards. The skill of numerical weather prediction over Africa is still low, even for lead times of less than 24 hours. Therefore, there is a particular need to deliver nowcasting of events as they occur. However, there remains a widespread lack of provision of nowcasting across Africa and virtually no use of automated nowcasting systems or tools. This limits the ability of national meteorological services to issue warnings and therefore potentially prevent the loss of life and significant financial losses. Coverage by meteorological radars is still very limited, but geostationary satellites provide regular high resolution data of the often large and long‐lived storms. As such, there is an opportunity to improve satellite‐based nowcasting capability in Africa. Work being undertaken as part of the Global Challenges Research Fund African SWIFT (Science for Weather Information and Forecasting Techniques) project is starting to improve the nowcasting of African convective systems and so the ability to provide timely warnings of extreme weather providing a wide range of benefits.
Haboob occurrence strongly impacts the annual variability of airborne desert dust in North Africa with more dust raised from erodible surfaces in the early summer (monsoon) season when deep convective storms are common but soil moisture and vegetation cover are low. On 27 June 2018, a large dust storm is initiated in North Africa associated with an intensive westward dust transport. Far away from emission sources, dust is transported over the Atlantic for the long distance. Dust plume is emitted by a strong surface wind and becomes a type of haboob when it merges with the southeastward deep convective system in central Mali at 0200 UTC (27 June). We use satellite observations to describe and estimate the dust mass concentration during the event. Approximately 93% of emitted dust is removed from the atmosphere between sources (10°N–25°N; 1°W–8°E) and the African coast (6°N–21°N; 16°W–10°W). The convective cold pool has induced large economic and healthy damages, and death of animals in the northeastern side of Senegal. ERA5 reanalysis have shown that the convective mesoscale impacts strongly the climatological location of the Saharan heat low (SHL).
<p>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.</p><p>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 &#8211; this recognises that MCS evolution favour drier land.</p><p>A flood knowledge database, compiled with local partners, allows estimation of the flood risk over Dakar given the identified probability of convective activity. The flood hazard is estimated from the probabilistic convective-activity nowcast when combined with information on the historical relationship between convective activity and precipitation totals. Information on the antecedent conditions can also be included, with a higher level of hazard associated with recent rainfall and already-wet conditions. Flood vulnerability is estimated at the local scale from post-event analysis of the 2009 flood events along with information from recent modelling studies and flood-alleviation measures. The combined information from nowcasts of convective-activity and flood-risk is visualised through an interactive desktop GUI and an online portal. Operational trials over the 2020 and 2021 rainy seasons, and during intensive nowcasting testbeds with researchers and forecasters, has shown the utility of these new nowcast products to support Impact-based Forecasting.</p>
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.