Development of high spatial resolution rainfall data for Ghana. (25th August 2017)

• Record Type: Journal Article
• Authors: Aryee, J. N. A.; Amekudzi, L. K.; Quansah, E.; Klutse, N. A. B.; Atiah, W. A.; Yorke, C.
• Is Part Of: International journal of climatology. Volume 38:Number 3(2018)
• Page Start: 1201
• Page End: 1215
• Publication Date: 2017-08-25
• Subjects: rainfall climatology -- GMet v1.0 -- homogenization -- minimum surface curvature -- quantile matching -- validation -- clustering -- Ghana; Climatology -- Periodicals; Climat -- Périodiques; Climatologie -- Périodiques; 551.605
• DOI: 10.1002/joc.5238
• Languages: English
• ISSNs: 0899-8418

Abstract

ABSTRACT: Various sectors of the country's economy – agriculture, health, energy, among others – largely depend on climate information, hence availability of quality climate data is very essential for climate‐impact studies in these sectors. In this paper, a monthly rainfall database (GMet v1.0) has been developed at a 0.5 ° × 0.5 ° spatial resolution, from 113 Ghana Meteorological Agency (GMet) gauge network distributed across the four agro‐ecological zones of Ghana, and spanning a 23‐year period (1990–2012). The datasets were first homogenized with quantile‐matching adjustments and thereafter, gridded at a spatial resolution of 0.5 ° × 0.5 ° using Minimum Surface Curvature with tensioning parameter, allowing for comprehensive spatial fields assessment on the developed dataset. Afterwards, point‐pixel validation was performed using GMet v1.0 against gauge data from stations that were earlier excluded due to large datagaps. This proved the reliability of GMet v1.0, with high and statistically significant correlations at 99% confidence level, and relatively low biases and rmse. Furthermore, GMet v1.0 was compared with GPCC and TRMM rainfall estimates, with both products found to adequately mimick GMet v1.0, with high correlations which are significant at 99% confidence level, low biases and rmse. In addition, the ratio of 90 th – percentile provided fairly similar capture of extremes by both TRMM and GPCC, in relation to GMet v1.0. Finally, based on annual rainfall totals and monthly variability, k ‐means cluster analysis was performed on GMet v1.0, which delineated the country into four distinct climatic zones. The developed rainfall data, when officially released, will be a useful product for climate impact and further rainfall validation studies in Ghana. Abstract : The unavailability of quality countrywide datasets serves as limitation to climate‐impact assessments. Here, a countrywide monthly precipitation data (GMet v1.0) has been provided at a spatial resolution of 0.5° × 0.5°. The dataset has been validated with ground truth to ensure good performance.