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We used five years of global solar radiation data to estimate the monthly average of daily global solar irradiation on a horizontal surface based on a single parameter, sunshine hours, using the artificial neural network method. The station under the study is located in Kampala, Uganda at a latitude of 0.19 • N, a longitude of 32.34 • E, and an altitude of 1200 m above sea level. The five-year data was split into two parts in 2003-2006 and 2007-2008; the first part was used for training, and the latter was used for testing the neural network. Amongst the models tested, the feed-forward back-propagation network with one hidden layer (65 neurons) and with the tangent sigmoid as the transfer function emerged as the more appropriate model. Results obtained using the proposed model showed good agreement between the estimated and actual values of global solar irradiation. A correlation coefficient of 0.963 was obtained with a mean bias error of 0.055 MJ/m 2 and a root mean square error of 0.521 MJ/m 2 . The single-parameter ANN model shows promise for estimating global solar irradiation at places where monitoring stations are not established and stations where we have one common parameter (sunshine hours).

Lake Victoria, Africa's largest freshwater lake, suffers greatly from negative changes in biomass of species of fish and also from severe eutrophication. The continuing deterioration of Lake Victoria's ecological functions has great long-term consequences for the ecosystem benefits it provides to the countries bordering its shores. However, knowledge about temporal and spatial variations of optical properties and how they relate to lake constituents is important for a number of reasons such as remote sensing, modeling of underwater light fields, and long-term monitoring of lake waters. Based on statistical analysis of data from optical measurements taken during half a year of weekly cruises in Murchison Bay, Lake Victoria, we present a three-component model for the absorption and a two-component model for the scattering of light in the UV and the visible regions of the solar spectrum along with tests of their ranges of validity. The three-component input to the model for absorption is the chlorophyll-a (Chl-a), total suspended materials concentrations, and yellow substance absorption, while the two-component input to the model for scattering is the Chl-a concentration and total suspended materials.

The Ozone Monitoring Instrument (OMI) overpass solar ultraviolet (UV) indices have been validated against the ground-based UV indices derived from Norwegian Institute for Air Research UV measurements in Kampala (0.31° N, 32.58° E, 1200 m), Uganda for the period between 2005 and 2014. An excessive use of old cars, which would imply a high loading of absorbing aerosols, could cause the OMI retrieval algorithm to overestimate the surface UV irradiances. The UV index values were found to follow a seasonal pattern with maximum values in March and October. Under all-sky conditions, the OMI retrieval algorithm was found to overestimate the UV index values with a mean bias of about 28%. When only days with radiation modification factor greater than or equal to 65%, 70%, 75%, and 80% were considered, the mean bias between ground-based and OMI overpass UV index values was reduced to 8%, 5%, 3%, and 1%, respectively. The overestimation of the UV index by the OMI retrieval algorithm was found to be mainly due to clouds and aerosols.

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