Developing an improved global solar radiation map for Zimbabwe through correlating long-term ground- and satellite-based monthly clearness index values

Reliable knowledge of the spatio-temporal distribution of solar radiation is required for the informed design and deployment planning of solar energy delivery systems. In this paper an improved global solar radiation map for Zimbabwe is developed by merging ground-measured radiation data from a sparsely distributed station network, with less accurate satellite-measured data which have an almost continuous spatial coverage. Monthly clearness index values derived from ground-measured global radiation are correlated with those derived from satellite data to obtain a model for calibrating satellite-measured data at a specified grid interval. Two multiplicative factors are to then used to further correct the generated data; CFm to cater for the in-exactness of the regression fit and the other, IBCF to cater for the interpolation error. Contour maps of global solar radiation are then constructed using interpolation by the geo-statistical method of ordinary kriging. The accuracy of the maps in predicting observed (ground-measured) values was tested by evaluating error statistics; relative bias error (rBE), relative mean bias error (rMBE) and normalized root mean square error (NRMSE) in a “leave-one-out” cross-validation analysis. Results indicate that the maximum normalized root mean square error was 0.028 (about 3%), a significant improvement when compared to an earlier map, the H–G map with a normalized root mean square error (NRMSE) of 0.097. •We select clearness index as the parameter to use for calibrating satellite-measured solar data.•We develop linear regression equations for determining local global radiation from satellite data.•Accuracy of data is improved by introducing regression and interpolation correction factors.•We produce a global solar radiation map which is of improved accuracy compared to a predecessor map.•Accuracy of radiation map is checked by leave-one-out cross validation.