A method for mapping color sky images of convective fair-weather cumuli to a scalar irradiance metric is presented. While basic interpretations of irradiance from color sky imagery are sufficient for many irradiance forecasting applications, sky images containing convective fair-weather cumuli can often contain gray-colored regions. Gray regions of a cloud commonly relate cloud thickness, indicating a region that has a high potential to attenuate surface irradiance from the sun. However, existing irradiance metrics are not found to translate such gray regions in accordance with their importance to a clear-sky index for short-term forecasting. This method exploits the special structure of sky images in three-dimensional red-green-blue (RGB) Cartesian space. By applying principal component analysis and basic clustering operations, a planar discriminant for cloudy-to-clear-sky classification is found. Once the {cloudy, clear-sky} identity of all image pixels is determined, the cloudy and clear-sky pixel distributions are separately remapped in RGB space so that gray pixels score a higher optical depth value than white pixels, and clear-sky pixels are mapped to low optical depth values. The resulting clear-sky index is simply the grayscale interpretation of the remapped image. The method is described and demonstrated with two actual sky images, and errors for sequences of several hundred images are tabulated and discussed. Remapped images are shown to represent gray cloud regions with a relatively high cloud optical depth in comparison with white cloud regions. The proposed metric is shown, by comparison with experimentally measured irradiance time series, to produce a more accurate clear-sky index in comparison with other methods.

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