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Algorithm for detecting clear sky images

Author(s): Pawar, Prathamesh Vijay | Advisor(s): Kleissl, Jan | Abstract: Many solar forecast algorithms based on ground based sky imagery applythe red-blue ratio (RBR) method to classify image pixels as clear or cloudy, bycomparing the current image with the corresponding image from a clear skylibrary (CSL). The CSL needs to be updated regularly due to change in clear skyconditions over time caused by aerosols and imager dome properties. This clearsky library is typically created by visually scrutinizing daily sky videos andselecting appropriate clear sky periods. This practice takes a significant amount oftime and manual intervention can result in human errors. To avoid this, anautomated CSL algorithm (ACSL) was developed which filters each image for clearsky features such as maximum green pixel brightness, average RBR, and redchannel difference. The relative root mean square error (RMSE) between theimage RBR of the manually created CSL and the one created using ACSL forNovember 2013 at UC San Diego was observed to be less than 5% over the rangeof solar zenith angles and it was found to be more representative of clearconditions than its manual counterpart.