Drought remains a phenomenal disaster of critical concerns in West Africa, particularly within the Niger River Basin, due to its insidious, multifaceted, and long-lasting nature. Its continuous severe impacts on communities, combined with the limitations of existing univariate index-based monitoring methods, worsen the challenge. This paper introduces and evaluates a Hybrid Drought Resilience Empirical Model (DREM) that integrates meteorological, agricultural, and hydrological indicators to improve their concurrent monitoring and early warning for effective decision-making in the region. Using reanalysis hydrometeorological data (1980–2016) and community vulnerability records, results show that the DREM-based composite index detects drought earlier than the Standardized Precipitation Index (SPI), with stronger alignment to soil moisture and streamflow variations. The model identifies drought onset when thresholds range from −0.26 to −1.19 over three consecutive months, depending on location, and signals drought termination when thresholds rise between −0.08 and −0.82. The study concludes that the DREM-based composite index provides a more reliable and integrated framework for early drought detection and decision-making across the Niger River Basin, and hence, has proven to be a suitable drought monitor for stakeholders in the Niger Basin which can be relied upon and trusted with high confidence.

High-frequency meteorological observations from automatic weather stations (AWS) are frequently affected by substantial data gaps in data-sparse regions such as West Africa, limiting their usability for climate analysis and decision-making. This study presents a machine learning-based framework for reconstructing missing hourly observations by integrating in situ AWS measurements with ERA5-Land reanalysis fields and Global Precipitation Measurement (GPM) satellite-derived products. The framework was applied to a network of over 50 AWS across 10 West African countries over the period 2017–2025, targeting seven meteorological variables: air temperature, relative humidity, global solar radiation, atmospheric pressure, precipitation, wind speed, and wind direction. Gradient boosting models (XGBoost, LightGBM, and CatBoost) were trained following a station-wise and variable-wise strategy, yielding over 300 variable-specific models. Detailed quantitative results are reported for four representative stations spanning distinct agro-climatic zones (Sahelian, Sudanian, coastal, and humid tropical). Air temperature and atmospheric pressure exhibit the highest reconstruction skill, with R2 values typically exceeding 0.90, while relative humidity and global solar radiation achieve R2 between 0.80 and 0.92. Precipitation and wind speed showed lower reconstruction skill than thermodynamic variables, reflecting their intermittency and sensitivity to local-scale processes. Wind direction, evaluated separately using circular statistics after recombination into degrees, exhibited the largest angular errors, highlighting the difficulty of reconstructing directional variability from large-scale predictors alone.

West Africa is a data-poor region, and long-term hydrometeorological field experiments are very limited but are essential for a better understanding of climate change and land use change impacts in this vulnerable region. This study provides a detailed overview of WASCAL hydrometeorological observatory, which was established in 2013 in the Sudan savanna of Burkina Faso and Ghana. This region is characterized by strong land use changes due to a rapid increase of agricultural land. The observatory is therefore designed to study the effects of land use changes on land-atmosphere exchange processes and other terrestrial land surface processes and characteristics. It consists of a network of state-of-the-art hydro-meteorological measurement equipment (e.g., automatic weather stations, agrometeorological stations) complemented by innovative devices such as cosmic ray neutron sensors for improved soil moisture monitoring. A unique component of the observatory is a micrometeorological experiment using eddy covariance towers implemented at five contrasting land use sites to study the impacts of land use change on water, energy, and greenhouse gas fluxes. The datasets of the WASCAL observatory are needed as key information for the development and evaluation of land surface models, hydrological models, and improved regional climate models and other environmental modelling approaches and products. In this presentation, we provide a detailed overview of the current development of the WASCAL observatory. In addition, selected results from the inter-twined field, remote sensing, and RCM modeling studies are presented.

West Africa is undergoing rapid agricultural intensification driven by population growth, leading to significant anthropogenic land use and land cover change (LCC), including both deforestation and afforestation. These changes can profoundly affect the regional climate system by altering the surface energy balance, moisture fluxes, and atmospheric circulation, potentially exacerbating the vulnerability of human, ecological, and economic systems. Despite the ability of climate models to simulate LCC impacts, considerable uncertainties remain, particularly in simulations of precipitation and temperature responses. This study provides the first multidisciplinary systematic review of LCC impacts in West Africa. Data from 26 selected publications were eventually synthesized from an initial pool of nearly 6000 studies. Results indicate that deforestation generally contributes to regional warming, with significant historical temperature increases of +0.26 ± 0.12 °C and projected increases of +0.88 ± 0.25 °C under the future scenarios. Conversely, afforestation could have significantly cooled the climate, lowering temperatures by −0.24 ± 0.14 °C historically and −0.22 ± 0.14 °C in future scenarios, without even accounting for carbon sequestration. Deforestation decreases regional precipitation by 80 ± 58 mm yr ^−1 historically and −55 ± 102 mm yr ^−1 in future scenarios, while large-scale afforestation could substantially reduce droughts with increased precipitation, averaging +40 ± 67 mm yr ^−1 historically and 80 ± 58 mm yr ^−1 in future scenarios. These results emphasize the need to integrate LCC-induced climate effects into land-based mitigation strategies, climate policy, and assessment frameworks.

Competition between crops and livestock farming systems escalates due to changing land use patterns driven by climate change in the Sahel of West Africa’s, particularly the Volta and Niger River basins. This study demonstrates the practical implications of sustainable intensification pathways under different climate realizations that illustrate the synergies of the crop-livestock-climate nexus in reducing the negative impacts of climatic extreme events in the Sahel of West Africa. Integrated crop-livestock experimental designs were conducted during the 2018–2022 rainy seasons across Burkina Faso, Ghana, Mali, and Niger and covering 80 pilot farms across these countries. These pilot sites were grouped into intensive and extensive sets. The intensive pilot sites implemented Fisher block experiments under natural conditions, with multiple treatments involving the use of digestate from biodigesters. The extensive sites employed a randomized complete block design with treatment involving compost from pits/surfaces. To support these experimental designs, a customized agroclimatic information package was provided to the farmers in the pilot sites. The package included sub-seasonal-to-seasonal forecasts and agricultural advisory of how the climate information can be used efficiently (i.e., Technical itinerary). Results indicate that amendments significantly impacted soil nutrient levels, with compost from pits exhibiting superior carbon storage despite recorded weather extremes. Organic fertilization increases nitrogen content, compensating for plant nitrogen exports. Furthermore, digestate-based and pit compost effectively enhance soil fertility in terms of carbon, phosphorus, and nitrogen. Crop production also showed marked improvements, particularly in treatments receiving organic amendments and micro-doses of chemical fertilizers, although variations between sites were evident. Hence, two basic intensification pathways were identified that emphasize using crop residues, composts, and agroclimatic information advisory systems, presenting scalable solutions for sustainable agricultural development and climate resilience in the Sahel region. Integrating biodigester technology, composting, and micro-dosing practices provides short and medium-term benefits, including improved soil health, enhanced water retention, and greater resilience to climate extremes. This sustainable approach is scalable and also addresses waste management and emission reduction, aligning with climate-smart practices. To promote such mixed farming systems agricultural policies must include awareness campaigns about these pathways, subsidies for the biodigester technology, and technical training to farmers in the Sahel region.

This study analyzed aerosol optical properties in West Africa, a region significantly influenced by the Sahara Desert's extensive dust emissions. Key aerosol optical characteristics, such as Aerosol Optical Depth (AOD), Angstrom exponent (α), and particle size distributions, were examined using ground-based radiometric data from five West African Aerosol Robotic Network (AERONET) sites—Cape Verde, Banizoumbou, Dakar, and Ilorin—collected between 2004 and 2009. Results indicate distinct primary and secondary AOD peaks during the dry season, with Cape Verde peaking in June. The Angstrom exponent exhibited seasonal variation, with minimum values associated with dust storms at all locations. However, Ilorin consistently showed higher α values, even during dust events, likely due to a mix of biomass burning and dust. An inverse relationship between AOD and α was observed at all sites except Ilorin. Additionally, a small fraction of α values above 1.5 were observed in Banizoumbou, Dakar, and Ilorin, with none recorded in Cape Verde. Ilorin also recorded only 21

This study presents PlanteSaine, a novel mobile application powered by Artificial Intelligence (AI) models explicitly designed for maize, tomato, and onion farmers in Burkina Faso. Agriculture in Burkina Faso, like many developing nations, faces substantial challenges from plant pests and diseases, posing threats to both food security and economic stability. PlanteSaine addresses these challenges by offering a comprehensive solution that provides farmers with real-time identification of pests and diseases. Farmers capture images of affected plants with their smartphones, and PlanteSaine’s AI system analyzes these images to provide accurate diagnoses. The application’s offline functionality ensures accessibility even in remote areas with limited Internet connectivity, while its messaging feature facilitates communication with agricultural authorities for guidance and support. Additionally, PlanteSaine includes an emergency alert mechanism to notify farmers about pest and disease outbreaks, enhancing their preparedness to deal with these threats. An AI-driven framework, featuring an image feature extraction phase with EfficientNetB3 and an artificial neural network (ANN) classifier, was developed and integrated into PlanteSaine. The evaluation of PlanteSaine demonstrates its superior performance compared to baseline models, showcasing its effectiveness in accurately detecting diseases and pests across maize, tomato, and onion crops. Overall, this study highlights the potential of PlanteSaine to revolutionize agricultural technology in Burkina Faso and beyond. Leveraging AI and mobile computing, PlanteSaine provides farmers with accessible and reliable pest and disease management tools, ultimately contributing to sustainable farming practices and enhancing food security. The success of PlanteSaine underscores the importance of interdisciplinary approaches in addressing pressing challenges in global agriculture

Heatwaves and droughts increasingly impact public health and societal system in a world subject to global warming. Several studies reported these phenomena all around the world, but there is a dearth of research specifically in West Africa. This study fills that gap by comparing heatwave/heat stress and drought occurrence in three climate zones (Guinea, Sudan and Sahel) of West Africa from 1981 to 2020. The analysis focuses on the comparison of station and gridded datasets. The Cumulative Excess Heat (CumHeat) and the Universal Thermal Climate Index (UTCI) are considered for heatwaves. For drought, the Standardized Precipitation (Evapotranspiration) Index SPI (SPEI) are used at 3‐, 6‐ and 12‐month scales. Both heatwave and drought characteristics are investigated as well as their co‐occurrence (D‐HW). The investigation reveals a good correlation between station and gridded datasets for drought indices. While station data records fewer and less intense heatwave, gridded data indicates longer‐lasting heat extremes. The study also demonstrates a strong agreement between the UTCI computed from the Rayman model and ERA5‐HEAT dataset, despite timing discrepancies, especially along the Guinea coast. The Sahel region is found to endure higher heat stress levels, with increasing intensity of heatwaves over time. Notably, the study uncovers an increasing frequency of compound D‐HW in all zones, especially the Sudan and Sahel zones, offering new insights into the climatic challenges faced by West Africa. These findings emphasize the critical need for improved planning and early warning systems (EWS) to mitigate the impacts of these climate extremes ecosystems and human health.

Complex physical processes that are inherent to rainfall lead to the challenging task of its prediction. To contribute to the improvement of rainfall prediction, artificial neural network (ANN) models were developed using a multilayer perceptron (MLP) approach to predict monthly rainfall 2 months in advance for six geographically diverse weather stations across the Benin Republic. For this purpose, 12 lagged values of atmospheric data were used as predictors. The models were trained using data from 1959 to 2017 and tested for 4 years (2018–2021). The proposed method was compared to long short-term memory (LSTM) and climatology forecasts (CFs). The prediction performance was evaluated using five statistical measures: root mean square error, mean absolute error, mean absolute percentage error, coefficient of determination, and Nash–Sutcliffe efficiency (NSE) coefficient. Furthermore, Taylor diagrams, violin plots, box error, and Kruskal–Wallis test were used to assess the robustness of the model’s forecast. The results revealed that MLP gives better results than LSTM and CF. The NSE obtained with the MLP, LSTM, and CF models during the test period ranges from 0.373 to 0.885, 0.297 to 0.875, and 0.335 to 0.845, respectively, depending on the weather station. Rainfall predictability was more accurate, with 0.512 improvement in NSE using MLP at higher latitudes across the country, showing the effect of geographic regions on prediction model results. In summary, this research has revealed the potential of ANN techniques in predicting monthly rainfall 2 months ahead, supplying valuable insights for decision-makers in the Republic of Benin.

The number of solar power plants has increased in West Africa in recent years. Reliable reanalysis data and short-term forecasting of solar irradiance from numerical weather prediction models could provide an economic advantage for the planning and operation of solar power plants, especially in data-poor regions such as West Africa. This study presents a detailed assessment of different shortwave (SW) radiation schemes from the Weather Research and Forecasting (WRF) Model option Solar (WRF-Solar), with appropriate configurations for different atmospheric conditions in Ghana and the southern part of Burkina Faso. We applied two 1-way nested domains (D1 = 15 km and D2 = 3 km) to investigate four different SW schemes, namely, the Community Atmosphere Model, Dudhia, RRTMG, Goddard, and RRTMG without aerosol and with aerosol inputs (RRTMG_AERO). The simulation results were validated using hourly measurements from different automatic weather stations established in the study region in recent years. The results show that the RRTMG_AERO_D01 generally outperforms the other SW radiation schemes to simulate global horizontal irradiance under all-sky condition [RMSE = 235 W m-2 (19%); MAE = 172 W m-2 (14%)] and also under cloudy skies. Moreover, RRTMG_AERO_D01 shows the best performance on a seasonal scale. Both the RRTMG_AERO and Dudhia experiments indicate a good performance under clear skies. However, the sensitivity study of different SW radiation schemes in the WRF-Solar model suggests that RRTMG_AERO gives better results. Therefore, it is recommended that it be used for solar irradiance forecasts over Ghana and the southern part of Burkina Faso.

Estimates of global horizontal irradiance (GHI) from reanalysis and satellite-based data are the most important information for the design and monitoring of PV systems in Africa, but their quality is unknown due to the lack of in situ measurements. In this study, we evaluate the performance of hourly GHI from state-of-the-art reanalysis and satellite-based products (ERA5, MERRA-2, CAMS, and SARAH-2) with 37 quality-controlled in situ measurements from novel meteorological networks established in Burkina Faso and Ghana under different weather conditions for the year 2020. The effects of clouds and aerosols are also considered in the analysis by using common performance measures for the main quality attributes and a new overall performance value for the joint assessment. The results show that satellite data performs better than reanalysis data under different atmospheric conditions. Nevertheless, both data sources exhibit significant bias of more than 150 W/m2 in terms of RMSE under cloudy skies compared to clear skies. The new measure of overall performance clearly shows that the hourly GHI derived from CAMS and SARAH-2 could serve as viable alternative data for assessing solar energy in the different climatic zones of West Africa.

The significance of quantifying the interaction of other non-dust particles with solar radiation cannot be overemphasized. This paper presents the radiative forcing aerosol effects of some non-dust particles over four different climatic zones of West Africa. Aerosol radiative effects on solar radiation require accurate analysis of optical and radiative properties. Radiative forcing was determined by anthropogenic, dust, marine, and non-dust aerosols governed by their size distribution and concentration. A consistent increase in daily AOD values was observed with decreasing angstrom exponent. Results showed that high negative forcing was experienced in the Savanna and Guinea zones which can be attributed to the addition of black carbon and organic matter aerosols to the heavily deposited dust in the atmosphere. Non-dust and anthropogenic aerosols were found to be major contributors to the high atmospheric absorption. The result also shows that the observed variations in the aerosol properties indicate an increase in the surface cooling in the early days of February. Therefore, a larger quantity of anthropogenic and non-dust aerosols, apart from the predominant dust, could cause and boost the radiative forcing of aerosols over West Africa.

This paper aims to review existing energy-sector and hydrogen-energy-related legal, policy, and strategy documents in the ECOWAS region. To achieve this aim, current renewable-energy-related laws, acts of parliament, executive orders, presidential decrees, administrative orders, and memoranda were analyzed. The study shows that ECOWAS countries have strived to design consistent legal instruments regarding renewable energy in developing comprehensive legislation and bylaws to consolidate it and to encourage investments in renewable energy. Despite all these countries having a legislative basis for regulating renewable energy, there are still weaknesses that revolve around the law and policy regarding its possible application in green hydrogen production and use. The central conclusion of this review paper is that ECOWAS member states presently have no official hydrogen policies nor bylaws in place. The hydrogen rise presents a challenge and opportunity for members to play an important role in the fast-growing global hydrogen market. Therefore, these countries need to reform their regulatory frameworks and align their policies by introducing green hydrogen production in order to accomplish their green economy transition for the future and to boost the continent’s sustainable development.

With the ongoing global warming, the occurrence and amplitude of extreme weather events have increased over the West African Sahel. The increasing frequency of heavy rain events, can negatively affect the lowland crops’ growth and production. Two-season field experiments were conducted near Ouagadougou (Burkina Faso) to test the effects of temporary flooding and surface water stagnation on maize ( Zea mays L. ) growth and productivity. The treatments were organized into a split-split plot design. Three factors were monitored, including aboveground flooding levels (i.e., 0 cm, 2–3 cm, and 7–8 cm), flooding duration (i.e., three days and six days), and growth stages (i.e., six-leaf stage (V6), tasseling stage (VT) and milky stage (R3)). Optimal crop management was practiced to Obatanpa cultivar planted during the rainy season and flooding was induced by over-irrigation. The results show that three days and six days of flooding, reduced grain yield by at least 35% when they occurred at the tasseling stage. Only 4–6 days of flooding reduced grain yield by 21% at the six-leaf stage. Further scrutiny, using the stress day index (SDI), revealed that the penalty on yield increases exponentially under flooding conditions as the value of the stress day index increases. Considering the new characteristics of the rainfall regime in the West African Sahel, dominated by a high frequency of heavy rain events and wet spells, temporary floods, and water stagnation are tremendously contributing to yield loss of on-farm maize. As the region’s climate changes, we hypothesize that excess water stress will become the next cause of food insecurity in the area.

This chapter compares six gridded products over West Africa: Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS); Tropical Rainfall Measuring Mission (TRMM); Global Precipitation Climatology Project (GPCP); Global Precipitation Climatology Center (GPCC); Climate Prediction Center (CPC); and CPC MORPHing technique (CMORPH), in order to assess uncertainties of daily precipitation characteristics. These gridded precipitation products are intercompared and further compared to actual gauge measurements from selected stations. We analyze some sector-specific climate indices derived from ClimPACT2, an R package for calculating climate extremes for sector applications. Results show that the gridded products display substantial systematic differences in the mean rainfall, mostly in sector-specific indices such as frequency of wet days, precipitation intensity, as well as consecutive wet and dry days. GPCC shows mostly higher maximum length of wet spell but has less precipitation intensity relative to CMORPH. Some of the gridded observations capture the monsoon rain belt evolution and show a good representation of the daily precipitation statistics within the range of the gauge dataset. Relative to gauges, CHIRPS, TRMM, GPCP, and GPCC produced reasonable estimates, but CPC and CMORPH show large difference in all seasons. Apart from CPC and CMORPH, the gridded products compare favorably well and thus make them suitable for impact studies to support adaptation policies that will enhance resilience against future extreme events. Due to the difference in the gridded observations, it is, however, necessary to be careful when drawing conclusions for planning interventions in climate-sensitive sectors, such as, health, water resources and hydrology, agriculture, and food security.

Expected changes in climate are likely to affect the energy production from wind. This study investigates projected changes in wind energy production under a changing climate over West Africa using the ensemble of regional climate models. Wind power output (Pout) is estimated using the ensemble of seven regional climate models participating in the Coordinated Regional Climate Downscaling Experiment (CORDEX) project. The ensemble mean of near-surface wind speed from the model simulations was compared with re-analysis datasets from ERA-5 between 1980 and 2005. While some biases were observed in the model performance over West Africa, the model ensemble provided a good representation of wind speed characteristics over the study region. The result shows that there is a potential reduction in energy produced up to 12% as a negative change is simulated in Pout in the near future (2021–2050) when compared to the reference period of 1971–2000. There is however a possible increment (about 24–30%) in power production in the far future (2071–2100) over most region of West Africa. In terms of variations of seasonal changes in Pout, there is more decline (− 8%) observed during the winter months of December to February (DJF) as compared to the summer season of June to August (JJA). The Sahel zone averaged wind power production up to 1.4 MW, while the Guinea and Savannah zones simulated lesser wind power output. In the Guinea region, an increasing inter-annual variability trend of up to 80% is estimated in Nigeria, Ghana, etc. towards the end of the century (2071–2100) as compared to historical years of 1971–2000. Our results provide a guide to government agencies and policymakers towards massive investment in harnessing wind energy in the future.

Climate services favor adopting strategies to increase agricultural productivity, enhance sustainable development, and adapt to unavoidable climate variability and change. However, for climates services to be effective, they must be accessible and suitable to user needs. This study investigated the effects of customized climate services (CCS) on land and labor productivity. Portraying the case of CCS delivered in the districts of Bolgatanga (Northern Ghana), Dano and Ouahigouya (western and northern Burkina Faso) in West Africa, it used: i) historical panel data of daily rainfall, yields, agricultural input, and output prices; ii) cost statements of farm operations and iii) other survey data from beneficiaries of on-farm demonstrations (pilot sites). Different results were found across farmers on the demonstrator sites, with Dano and Bolgatanga recording the best land and labor productivity. Strong and positive effects were observed in Dano, where land productivity increased by 200% and labor productivity doubled despite consecutive pluviometric extremes such as heavy rain events and prolonged dry spells in the 2017 and 2018 cropping seasons. Further investigation showed that CCS was particularly favorable to land and labor productivity of farmers who were committed to the advisory given by the CCS providers. Therefore, as perishable goods, the success of CCS applications would require thorough co-production, delivery, and monitoring for their effectiveness in improving land and labor productivity for agriculture in semi-arid regions of West Africa.

AbstractPoor waste management and uncontrolled waste disposal cause pollution and blocked drainage facilities, leading to flooding and water stagnation, increasing the risk of diseases, and eroding local progresses toward achieving the Sustainable Development Goals (SDGs). Recycling and repurposing liquid and solid waste for urban and periurban agriculture, green spaces, and green energy on the other hand benefit social and ecosystem resilience and can contribute to SDG11. This chapter considers the uneven progress in Burkina Faso where the transgressive behavior of garbage producers leads to illegal garbage dumping, equipment obsolescence, insufficient maintenance, and the lack of support from authorities. We show how the creation of a participatory Multi-Stakeholder Platform (MSP) can lead to better collection, recycling, and repurposing of wastes. The results showed that (1) the practice of liquid and solid waste management involves several interacting stakeholders, (2) these interactions generate complex problems hardly resolved by a single (few) stakeholder(s), and (3) an MSP is a good approach toward solving these complex problems.

The regional climate as it is now and in the future will put pressure on investments in sub-Saharan Africa in water resource management, fisheries, and other crop and livestock production systems. Changes in oceanic characteristics across the Atlantic Ocean will result in remarkable vulnerability of coastal ecology, littorals, and mangroves in the middle of the twenty-first century and beyond. In line with the countries' objectives of creating a green economy that allows reduced greenhouse gas emissions, improved resource efficiency, and prevention of biodiversity loss, we identify the most pressing needs for adaptation and the best adaptation choices that are also clean and affordable. According to empirical data from the field and customized model simulation designs, the cost of these adaptation measures will likely decrease and benefit sustainable green growth in agriculture, water resource management, and coastal ecosystems, as hydroclimatic hazards such as pluviometric and thermal extremes become more common in West Africa. Most of these adaptation options are local and need to be scaled up and operationalized for sustainable development. Governmental sovereign wealth funds, investments from the private sector, and funding from global climate funds can be used to operationalize these adaptation measures. Effective legislation, knowledge transfer, and pertinent collaborations are necessary for their success.

Global horizontal irradiance (GHI) is the primary driver for photovoltaic (PV) technology. For PV system design and monitoring, hourly and sub-hourly GHI from reanalysis and satellite-based data are frequently used, especially in data-poor regions like Sub-Saharan Africa. However, the use of these datasets is uncertain and need to be assessed in detail. In this study, we evaluated the performance of state-of-the-art reanalysis and satellite-based datasets (ERA5, CAMS, MERRA-2 and SARAH-2) with in situ measurements of hourly GHI for the main climatic zones (Guinea, Savannah, and Sahel) in West Africa. The in situ measurements come from novel regional and national meteorological networks consisting of 51 automatic weather stations in Burkina Faso and Ghana. The performance assessment was done for the year 2020 for different weather conditions (cloudy, clear and all sky). The effects of clouds and aerosols were also investigated. Moreover, a new overall performance measure is introduced for joint evaluation of different standard measures, such as the root-mean-square error and the index of agreement. The results show that the data from SARAH-2 performs best under cloudy-sky conditions, while ERA5 performs worse under all atmospheric conditions. The low performance under cloudy skies for all datasets is the result of a large bias observed during the Harmattan period, when the region has a high concentration of aerosols. The average diurnal variation of GHI shows good agreement between the in situ measurements, the satellite and the reanalysis data under clear and all-sky conditions, but an overestimation under cloudy skies at some stations. The new overall performance value clearly indicates hourly GHI from SARAH-2 is the best alternative for assessing solar energy in West Africa.