The Ghana Meteorological Agency delineated Ghana’s geographical space into four agro-climatic zones namely the north, transition, forest and coastal zones. Since the demarcation in the 1960s, previous studies have rarely provided a more dis-aggregated agro-climatic zone map in tandem with contemporary climate change and variability. The continued use of this age-old classified zones is a disservice to the public. In this study, therefore, we evaluated the existing agro-climatic zone map of Ghana and reconstructed it to a more appropriate and dis-aggregated map that reflect current climate change and variability impact. This was achieved by quantifying the contrast in rainfall and temperature amount over a 30 year period for different climate windows and mapped out areas with similar rainfall and temperature regimes. Our findings revealed significant changes in the existing agro-climatic zones especially in terms of number, the boundary size and geographical orientation of the zones. The newly proposed map consist of five distinctive climate zones namely: the Sudan Savannah, Guinea Savannah, Transition, Forest and Coastal zones. The Sudan and Guinea Savannah zones showed a southerly expansion. The transition zone shriveled in size as the Guinea Savannah zone took over most of it, notably in the southeast. The forest zone shrank in size with a northwest shift while the coastal belt grew to encompass the whole coast of Ghana. These changes are strong evidence of climate change and possible food production changes. These findings are useful to agriculture sector in planning their activities, the health sector in predicting specific diseases caused by changes in weather and climate, Ghana Meteorological Agency for weather forecasting purposes, and the National Disaster Management in identifying disaster prone zones.

Cosmic ray neutron sensors (CRNS) are increasingly used to determine field-scale soil moisture (SM). Uncertainty of the CRNS-derived soil moisture strongly depends on the CRNS count rate subject to Poisson distribution. State-of-the-art CRNS signal processing averages neutron counts over many hours, thereby accounting for soil moisture temporal dynamics at the daily but not sub-daily time scale. This study demonstrates CRNS signal processing methods to improve the temporal accuracy of the signal in order to observe sub-daily changes in soil moisture and improve the signal-to-noise ratio overall. In particular, this study investigates the effectiveness of the Moving Average (MA), Median filter (MF), Savitzky–Golay (SG) filter, and Kalman filter (KF) to reduce neutron count error while ensuring that the temporal SM dynamics are as good as possible. The study uses synthetic data from four stations for measuring forest ecosystem–atmosphere relations in Africa (Gorigo) and Europe (SMEAR II (Station for Measuring Forest Ecosystem–Atmosphere Relations), Rollesbroich, and Conde) with different soil properties, land cover and climate. The results showed that smaller window sizes (12 h) for MA, MF and SG captured sharp changes closely. Longer window sizes were more beneficial in the case of moderate soil moisture variations during long time periods. For MA, MF and SG, optimal window sizes were identified and varied by count rate and climate, i.e., estimated temporal soil moisture dynamics by providing a compromise between monitoring sharp changes and reducing the effects of outliers. The optimal window for these filters and the Kalman filter always outperformed the standard procedure of simple 24-h averaging. The Kalman filter showed its highest robustness in uncertainty reduction at three different locations, and it maintained relevant sharp changes in the neutron counts without the need to identify the optimal window size. Importantly, standard corrections of CRNS before filtering improved soil moisture accuracy for all filters. We anticipate the improved signal-to-noise ratio to benefit CRNS applications such as detection of rain events at sub-daily resolution, provision of SM at the exact time of a satellite overpass, and irrigation applications.

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.

Accurate estimation of the barometric coefficient ($\beta$) is important for correcting pressure effects in soil moisture data from cosmic-ray neutron sensing (CRNS) due to the barometric effect. To evaluate estimation strategies for $\beta$, we compared analytical and empirical approaches using 71 CRNS and 46 neutron monitor (NM) stations across the United States, Europe, and globally. Our results show spatio-temporal variation in the barometric effect, with $\beta$ ranging from 0.66 to 0.82 %hPa for NM and from 0.63 to 0.80 %hPa for CRNS. These coefficients exhibit higher variability than previously published semi-analytical models. In addition, we found that the analytically determined $\beta$ values were systematically lower compared with empirical estimates, with stronger agreement between the two empirical methods ($r\approx 0.67$) than between empirical and analytical approaches. Furthermore, NM stations produced higher $\beta$ values than CRNS, indicating that differences in detector energy sensitivity affected the values of $\beta$. Principal Component Analysis (PCA) further showed that the analytical and empirical $\beta$ estimates clustered together, reflecting shared sensitivity to elevation. In contrast, soil moisture and atmospheric humidity projected nearly orthogonally to the $\beta$ vectors, indicating negligible influence, while cut-off rigidity contributed to a separate, inverse gradient. Analytical $\beta$ estimates were fully orthogonal to AH, while empirical methods showed only slight deviations beyond orthogonality. The barometric coefficient ($\beta$), therefore, varies with location, altitude, atmospheric conditions, and sensor type, highlighting the necessity of station-specific values for precise correction. Overall, our study emphasizes the need for atmospheric correction in CRNS measurements and introduces a method for deriving site- and sensor-specific $\beta$ values for accurate soil moisture estimation.

In this paper, we present an analysis of summertime atmospheric simulation (June–July 2016) for southern West Africa (sWA) using the RegCM 4.7.1 regional climate model to describe the atmospheric behaviour over the region, and also engage comparisons between the modelled data and observed upper air data acquired during the DACCIWA (Dynamics–Aerosol–Chemistry–Cloud Interactions in West Africa) field campaign period. First, assessments of relative humidity and zonal wind profiles were made for selected coastal and inland stations, to infer the relative vertical and temporal atmospheric differences for both locations. Thereafter, the model’s performance was evaluated, capturing an excessive wet bias in RH profiles of the model with accompanying reduced zonal winds at the Tropical Easterly Jet (TEJ) region and thus produces excessive upper tropospheric cloud liquid water content. Also, in the lower troposphere (particularly, the monsoon layer), RegCM 4.7.1 model captures adequate spatial differences in both RH and zonal wind profiles along the coast and inland. We judge this outcome to be a valuable contribution on the path to rendering RegCM4 a good tool for simulating atmospheric and climate dynamics in sWA.