Jacob Agyekum

Coastal erosion is a persistent and growing challenge along West African shorelines, posing serious risks to livelihoods, infrastructure, and coastal ecosystems. In Ghana, erosion along the eastern coastline is driven by strong wave action, sea-level rise associated with climate change, unregulated coastal development, and sediment retention resulting from dam construction. In response, a range of hard engineering interventions, such as sea walls, breakwaters, armour rock revetments, and groyne systems were implemented in 2017 to reduce shoreline retreat. However, systematic and cost-effective monitoring is required to assess the effectiveness and long-term impacts of these interventions. This study documents the development of a digital coastal erosion monitoring tool using the Digital Earth Africa (DE Africa) platform, focusing on Blekusu, a highly erosion-prone coastal community in eastern Ghana, where major sea-defense structures have been constructed. Relevant national stakeholders, including the Ghana Hydrological Authority, the Ghana Meteorological Agency (GMet), and the Water Research Institute (WRI), were identified through an IWMI–DE Africa stakeholder training workshop and engaged to co-create the coastal erosion use case. An ecosystem of DE Africa tools was used, including DE Africa Explorer, DE Africa Maps, and DE Africa Sandbox. The Explorer served as a unified interface to query and retrieve analysis‑ready coastline datasets and metadata (2010–2024), spanning both pre‑ and post‑intervention periods, derived from 30 m Landsat imagery and tidal modeling. The DE Africa Sandbox served as an integrated development environment for Python-based analysis, enabling users to access, process, and visualize coastline dynamics without relying on multiple external tools. Existing coastal erosion notebooks within the Sandbox were adapted, significantly reducing the learning curve and development time. Code sharing among team members facilitated collaborative development and aligned with open-access data-sharing principles. IWMI and DE Africa jointly reviewed the use case and developed online training materials that allow users to enroll, build capacity, and earn certification. Shoreline extraction and change-detection analyses revealed persistent accretion exceeding 30 m in several sections following the intervention, although localized stabilization was observed near engineered structures. These results demonstrate the reliability of DE Africa as a scalable, accessible, and cost-effective platform for coastal monitoring and support its integration into national coastal management and planning strategies. Continued investment in DE Africa and its integration into university curricula would further expand its impact across Africa.

Floods as a result of severe storms cause significant impacts on lives and properties. Therefore, timely and accurate forecasts of the storms will reduce the associated risks. In this study, we look at the characteristics of a storm on 28 June, 2018 in Kumasi from a rain gauge network and satellite data, and reanalysis data. The storm claimed at least 8 lives and displaced 293 people in Kumasi, Ghana. The ability of satellite and reanalysis data to capture the temporal variations of the storm was assessed using a high temporal resolution (accumulation per minute) rain gauge data. We employed the observation data from the Dynamics–Aerosol–Chemistry–Cloud Interactions in West Africa (DACCIWA) rain gauges to assess the storm's onset, duration, and cessation. Subsequently, the performance of the ERA5 reanalysis and Global Precipitation Measurement (GPM) satellite precipitation estimates in capturing the rainfall is assessed. Both GPM and the ERA5 had difficulty reproducing the hourly pattern of the rain. However, the GPM produced variability that is similar to the observed. Generally, the region of maximum rainfall was located in the southern parts of the study domain in ERA5, while GPM placed it in the northern parts. The study contributes a verification measure to improve weather forecasting in Ghana as part of the objectives of the GCRF African Science for Weather Information and Forecasting Techniques (SWIFT) project.

Understanding the environmental evolution of mesoscale convective systems (MCSs) is critical for forecasting weather in West Africa. This study investigated the thermodynamic and synoptic environments of MCSs over West Africa on 26 (storm 1) and 28 (storm 2) June 2018. Primary datasets used to assess the diurnal evolution of the storms were obtained from ERA5. The results showed a trapped gravity wave, enhanced by a well‐established African Easterly Jet and monsoon trough, was responsible for the initiation of storm 1. Both storms also initiated in the presence of several moist lower (925–850 hPa) to mid‐tropospheric (600 hPa) cyclonic and anticyclonic vortices, controlling inland moisture advection. The lower troposphere was moistened through moisture advection by the West African westerly jet for storm 1 and the nocturnal low‐level jet prior to initiation for storm 2. For both storms, the evolution of outgoing longwave radiation showed a consistent atmosphere of deep afternoon convection. Boundary layer height increased significantly during storm evolution to support the increasing ascent of warm air. Vegetation cover differences may have also likely aided the evolution of storm 2. The passage of gravity waves from decaying storms can aid forecasters to nowcast likely regions of afternoon convection with high accuracy. Under the GCRF African Science for Weather Information and Forecasting Techniques (SWIFT), these findings are crucial in fulfilling the project's aims of improving weather forecasting capability and communication over West Africa.

A selected number of global climate models (GCMs) from the fifth Coupled Model Intercomparison Project (CMIP5) were evaluated over the Volta Basin for precipitation. Biases in models were computed by taking the differences between the averages over the period (1950–2004) of the models and the observation, normalized by the average of the observed for the annual and seasonal timescales. The Community Earth System Model, version 1-Biogeochemistry (CESM1-BGC), the Community Climate System Model Version 4 (CCSM4), the Max Planck Institute Earth System Model, Medium Range (MPI-ESM-MR), the Norwegian Earth System Model (NorESM1-M), and the multimodel ensemble mean were able to simulate the observed climatological mean of the annual total precipitation well (average biases of 1.9% to 7.5%) and hence were selected for the seasonal and monthly timescales. Overall, all the models (CESM1-BGC, CCSM4, MPI-ESM-MR, and NorESM1-M) scored relatively low for correlation (<0.5) but simulated the observed temporal variability differently ranging from 1.0 to 3.0 for the seasonal total. For the annual cycle of the monthly total, the CESM1-BGC, the MPI-ESM-MR, and the NorESM1-M were able to simulate the peak of the observed rainy season well in the Soudano-Sahel, the Sahel, and the entire basin, respectively, while all the models had difficulty in simulating the bimodal pattern of the Guinea Coast. The ensemble mean shows high performance compared to the individual models in various timescales.

Seasonal predictions of precipitation, among others, are important to help mitigate the effects of drought and floods on agriculture, hydropower generation, disasters, and many more. This work seeks to obtain a suitable combination of physics schemes of the Weather Research and Forecasting (WRF) model for seasonal precipitation simulation over Ghana. Using the ERA-Interim reanalysis as forcing data, simulation experiments spanning eight months (from April to November) were performed for two different years: a dry year (2001) and a wet year (2008). A double nested approach was used with the outer domain at 50 km resolution covering West Africa and the inner domain covering Ghana at 10 km resolution. The results suggest that the WRF model generally overestimated the observed precipitation by a mean value between 3% and 64% for both years. Most of the scheme combinations overestimated (underestimated) precipitation over coastal (northern) zones of Ghana for both years but estimated precipitation reasonably well over forest and transitional zones. On the whole, the combination of WRF Single-Moment 6-Class Microphysics Scheme, Grell-Devenyi Ensemble Cumulus Scheme, and Asymmetric Convective Model Planetary Boundary Layer Scheme simulated the best temporal pattern and temporal variability with the least relative bias for both years and therefore is recommended for Ghana.