A family of fully automated digital whole sky imagers (WSIs) has been developed at the Marine Physical Laboratory over many years, for a variety of research and military applications. The most advanced of these, the day/night whole sky imagers (D/N WSIs), acquire digital imagery of the full sky down to the horizon under all conditions from full sunlight to starlight. Cloud algorithms process the imagery to automatically detect the locations of cloud for both day and night. The instruments can provide absolute radiance distribution over the full radiance range from starlight through daylight. The WSIs were fielded in 1984, followed by the D/N WSIs in 1992. These many years of experience and development have resulted in very capable instruments and algorithms that remain unique. This article discusses the history of the development of the D/N WSIs, system design, algorithms, and data products. The paper cites many reports with more detailed technical documentation. Further details of calibration, day and night algorithms, and cloud free line-of-sight results will be discussed in future articles.

Abstract. The calibrated ground-based sky imager developed in the Marine Physical Laboratory, the Whole Sky Imager (WSI), has been tested with data from the Atmospheric Radiation Measurement Program (ARM) at the Southern Great Plain site (SGP) to determine optical properties of the atmospheric aerosol. Different neural network-based models calculate the aerosol optical depth (AOD) for three wavelengths using the radiance extracted from the principal plane of sky images from the WSI as input parameters. The models use data from a CIMEL CE318 photometer for training and validation and the wavelengths used correspond to the closest wavelengths in both instruments. The spectral dependency of the AOD, characterized by theÅngström exponent α in the interval 440-870 nm, is also derived using the standard AERONET procedure and also with a neural network-based model using the values obtained with a CIMEL CE318. The deviations between the WSI derived AOD and the AOD retrieved by AERONET are within the nominal uncertainty assigned to the AERONET AOD calculation (±0.01), in 80% of the cases. The explanation of data variance by the model is over 92% in all cases. In the case of α, the deviation is within the uncertainty assigned to the AERONET α (±0.1) in 50% of the cases for the standard method and 84% for the neural network-based model. The explanation of data variance by the model is 63% for the standard method and 77% for the neural network-based model.

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