A wide range of economic sectors in the Ghana, including agriculture, health care, and energy, heavily rely on climate data; as a result, having access to reliable climate data is crucial for research and economic growth yet rainfall gauge data in Ghana scarcely available, therefore, researchers tend to depend on satellite estimates for hydrological studies and impact assessments. However, biases in satellite rainfall estimates and the ability for these rainfall products to effectively capture rainfall indices poses major issues for researcher and various key stakeholders. In this study, CHIRPS-v2 rainfall estimates were bias corrected using four (4) different bias correction algorithms (Linear Scaling (LS), Local Intensity Scaling (LOCI), Quantile Mapping (QM) and Bias Correction and Spatial Disaggregation (BCSD) methods) using 28 selected stations across Ghana and spatio-temporally over the entire country. At the station level the Linear Scaling method produced the best results, although after correction no significant changes were observed especially on a daily scale, using the day to compute seasonal indices yielded improved results. Spatio-temporally, The BCSD approach outperformed the other bias corrective correction strategies, most likely because it can capture the development of the average rainfall while matching statistical moments. The rainfall seasonal indices were then calculated from bias corrected CHIRPS-v2 data and the spread and the distributing of the various indices were well represented. Moreover, the extreme rainfall analysis produced results consistent with gauge values measured at the same time duration. Bias correction was able to minimize the errors and uncertainties that existed within the daily CHIRPS-v2 dataset, making it more suitable to derive agro-advisories.

My immence gratitude to God for His guidance , mercies, provisions and grace that continues to abound in my life and which has sustained me throughout my years in school. I am eternally grateful for your presence with me in every step and decision I made-the ones that taught me bitter lessons and the ones that brought me joy. Thank you for the opprtunities that you opened up for me and the grace to fulfil my responsibilities. My supervisors deserve nothing less than my most sincere and heartfelt ...

Climate Change; Food Security; Meteorology and Climatology

Weather Forecasting; Food Security; Climate Change; Ghana

Africa; West Africa

Ghana

Eastern Africa Hub