Development of high spatial resolution rainfall data for Ghana

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International Journal of Climatology
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 frst 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 felds 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 signifcant correlations at 99% confdence 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 signifcant at 99% confdence level, low biases and rmse. In addition, the ratio of 90th – 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 offcially released, will be a useful product for climate impact and further rainfall validation studies in Ghana.
An article published by International Journal of Climatology and available at DOI: 10.1002/joc.5238
rainfall climatology, GMet v1.0, homogenization, minimum surface curvature, quantile matching, validation, clustering, Ghana
Int. J. Climatol. 38: 1201–1215 (2018)