Like many other African countries, Ghana's rain gauge networks are rapidly deteriorating, making it challenging to obtain real-time rainfall estimates. In recent years, significant progress has been made in the development and availability of real-time satellite precipitation products (SPPs). SPPs may complement or substitute gauge data, enabling better real-time forecasting of stream flows, among other things. However, SPPs still have significant biases that must be corrected before the rainfall estimates can be used for any hydrologic application, such as real-time or seasonal forecasting. The daily satellite-based rainfall estimate (CHIRPS-v2) data were bias-corrected using the Bias Correction and Spatial Disaggregation (BSCD) approach. The study further investigated how bias correction of daily satellite-based rainfall estimates affects the identification of seasonality and extreme rainfall indices in Ghana. The results revealed that the seasonal and annual rainfall patterns in the region were better represented after the bias correction of the CHIRPS-v2 data. We observed that, before bias correction, the cessation dates in the country's southwest and upper middle regions were slightly different. However, they matched those of the gauge well after bias correction. The novelty of this study is that, in addition to improving rainfall using CHIRPS data, it also enhances the identification of seasonality indices. The paper suggests the BCSD approach for correcting rainfall estimates from other algorithms using long-term historical records indicative of the rainfall variability area under consideration.
Abstract The European Union (EU)-funded project Dynamics–Aerosol–Chemistry–Cloud Interactions in West Africa (DACCIWA) investigates the relationship between weather, climate, and air pollution in southern West Africa—an area with rapid population growth, urbanization, and an increase in anthropogenic aerosol emissions. The air over this region contains a unique mixture of natural and anthropogenic gases, liquid droplets, and particles, emitted in an environment in which multilayer clouds frequently form. These exert a large influence on the local weather and climate, mainly owing to their impact on radiation, the surface energy balance, and thus the diurnal cycle of the atmospheric boundary layer. In June and July 2016, DACCIWA organized a major international field campaign in Ivory Coast, Ghana, Togo, Benin, and Nigeria. Three supersites in Kumasi, Savè, and Ile-Ife conducted permanent measurements and 15 intensive observation periods. Three European aircraft together flew 50 research flights between 27 June and 16 July 2016, for a total of 155 h. DACCIWA scientists launched weather balloons several times a day across the region (772 in total), measured urban emissions, and evaluated health data. The main objective was to build robust statistics of atmospheric composition, dynamics, and low-level cloud properties in various chemical landscapes to investigate their mutual interactions. This article presents an overview of the DACCIWA field campaign activities as well as some first research highlights. The rich data obtained during the campaign will be made available to the scientific community and help to advance scientific understanding, modeling, and monitoring of the atmosphere over southern West Africa.
Malaria is a major public health challenge in Ghana and adversely affects the productivity and economy of the country. Although malaria is climate driven, there are limited studies linking climate variability and disease transmission across the various agro-ecological zones in Ghana. We used the VECTRI (vector-borne disease community model of the International Centre for Theoretical Physics, Trieste) model with a new surface hydrology scheme to investigate the spatio-temporal variability in malaria transmission patterns over the four agro-ecological zones in Ghana. The model is driven using temperature and rainfall datasets obtained from the GMet (Ghana Meteorological Agency) synoptic stations between 1981 and 2010. In addition, the potential of the VECTRI model to simulate seasonal pattern of local scale malaria incidence is assessed. The model results reveal that the simulated malaria transmission follows rainfall peaks with a two-month time lag. Furthermore, malaria transmission ranges from eight to twelve months, with minimum transmission occurring between February and April. The results further reveal that the intra- and inter-agro-ecological variability in terms of intensity and duration of malaria transmission are predominantly controlled by rainfall. The VECTRI simulated EIR (Entomological Inoculation Rate) tends to agree with values obtained from field surveys across the country. Furthermore, despite being a regional model, VECTRI demonstrates useful skill in reproducing monthly variations in reported malaria cases from Emena hospital (a peri urban town located within Kumasi metropolis). Although further refinements in this surface hydrology scheme may improve VECTRI performance, VECTRI still possesses the potential to provide useful information for malaria control in the tropics.
SCIAMACHY on ENVISAT offers with its limb solar backscatter, solar and lunar occultation measurement modes the opportunity to continue long term data sets of stratospheric O3 and NO2 profiles started with SAGE and HALOE. Data from SCIAMACHY is available since autumn 2002. Currently the mission will continue at least until 2010 and an extension until 2013 is currently under investigation at ESA. Stratospheric profiles derived from occultation measurements have a good data quality, but are limited in their coverage. Stratospheric profiles from the limb measurement mode allow global coverage of the stratosphere within 3 days. Nevertheless, until 2007 the limb data products of SCIAMACHY were heavily affected in their quality by large uncertainties in the knowledge of the tangent height. This was substantially improved in 2007 by implementing improved mis-alignment parameters in the operational processing chain at ESA. Consequently, the error in the limb data products is no longer dominated by the impact of the tangent height error and algorithms and the products are systematically improving.
Accurate quantification of land use/cover change (LULCC) is important for efficient environmental management, especially in regions that are extremely affected by climate variability and continuous population growth such as West Africa. In this context, accurate LULC classification and statistically sound change area estimates are essential for a better understanding of LULCC processes. This study aimed at comparing mono-temporal and multi-temporal LULC classifications as well as their combination with ancillary data and to determine LULCC across the heterogeneous landscape of southwest Burkina Faso using accurate classification results. Landsat data (1999, 2006 and 2011) and ancillary data served as input features for the random forest classifier algorithm. Five LULC classes were identified: woodland, mixed vegetation, bare surface, water and agricultural area. A reference database was established using different sources including high-resolution images, aerial photo and field data. LULCC and LULC classification accuracies, area and area uncertainty were computed based on the method of adjusted error matrices. The results revealed that multi-temporal classification significantly outperformed those solely based on mono-temporal data in the study area. However, combining mono-temporal imagery and ancillary data for LULC classification had the same accuracy level as multi-temporal classification which is an indication that this combination is an efficient alternative to multi-temporal classification in the study region, where cloud free images are rare. The LULCC map obtained had an overall accuracy of 92%. Natural vegetation loss was estimated to be 17.9% ± 2.5% between 1999 and 2011. The study area experienced an increase in agricultural area and bare surface at the expense of woodland and mixed vegetation, which attests to the ongoing deforestation. These results can serve as means of regional and global land cover products validation, as they provide a new validated data set with uncertainty estimates in heterogeneous ecosystems prone to classification errors.
"The Ewiem Nimdie Summer School Series in Ghana: Capacity Building in Meteorological Education and Research, Lessons Learned, and Future Prospects" published on May 2012 by American Meteorological Society.
To understand surface energy exchange processes over the semiarid regions in West Africa, numerical simulations of surface energy and water balances were carried out using a one-dimensional multilayer atmosphere-SOil-VEGetation (SOLVEG) model for selected days of the dry and rainy seasons over a savanna grassland ecosystem in Sumbrungu in the Upper East region of Ghana. The measured Bowen ratio was used to partition the residual energy into the observed sensible heat flux (H) and latent heat flux (LE) in order to investigate the impact of the surface energy closure on model performance. The results showed that the model overall reproduced the diurnal changes in the observed energy fluxes, especially the net radiation (Rn), compared to half-hourly eddy covariance flux measurements, for the study periods. The performance measure in terms of the correlation coefficient (R), centred root mean square error (RMSE), and normalized standard deviation ( σ ) between the simulated H and LE and their corresponding uncorrected observed values ranged between R = 0.63–0.99 and 0.83–0.94, RMSE = 0.88–1.25 and 0.88–1.92, and σ = 0.95–2.23 and 0.13–2.82 for the dry and rainy periods respectively, indicating a moderate to good model performance. The partitioning of H and LE by SOLVEG was generally in agreement with the observations during the dry period but showed clear discrepancies during the rainy period, particularly after rainfall events. Further sensitivity tests over longer simulation periods (e.g., 1 year) are required to improve model performance and to investigate seasonal exchanges of surface energy fluxes over the West African Savanna ecosystems in more details.
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