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.

Methane (CH₄) is a major and potent greenhouse gas (GHG), and its emissions from agricultural activities, particularly rice cultivation, are a significant concern for climate change. Due to the high demand for food security, driven by rapid population growth and national initiatives to reduce dependency on rice imports, rice cultivation is intensified in West Africa. However, its contribution to atmospheric CH₄ remains largely unknown. Here, for the first time, cutting-edge eddy covariance tower measurements were conducted parallelly in a rice field (Janga) and a reserve forest (Mole National Park), both located in the Guinea savanna region of West Africa. Using CH₄ measurement data from June to October 2023 (rice cultivation period), the dynamic interplay between methane emissions from rice cultivation and its potential mitigation through forest methane uptake was assessed. Our results show that the rice field acted as a net source of CH₄ at a rate of 2037 mgCH₄m⁻², whereas the most intense flooded period (August) accounted for 70% of the total emissions. On the other hand, the forest reserve acted as a sink, with a net uptake of −560 mgCH₄m⁻², and the highest uptake observed in October. Accounting for the global warming potential (GWP) of CH₄ over a 20 year period, the forest had a wet season negative GWP of −47.04 gCO₂eq, while the rice field emitted CH₄ of 171.36 gCO₂eq. This implies that under similar conditions during the measurement campaigns, the forest per square area needs approximately a factor of ∼4 to balance the positive radiative effect per square area of rice cultivated. This work emphasizes the need to integrate forests to compensate for methane released by rice cultivation in the semi-arid West African savannah region.

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.