We propose a methodological approach to provide the accurate and calibrated measurements of sky radiance and broadband solar irradiance using the High Dynamic Range (HDR) images of a sky-camera. This approach is based on a detailed instrumental characterization of a SONA sky-camera in terms of image acquisition and processing, as well as geometric and radiometric calibrations. As a result, a 1 min time resolution database of geometrically and radiometrically calibrated HDR images has been created and has been available since February 2020, with daily updates. An extensive validation of our radiometric retrievals has been performed in all sky conditions. Our results show a very good agreement with the independent measurements of the AERONET almucantar for sky radiance and pyranometers for broadband retrievals. The SONA sky radiance shows a difference of an RMBD < 10% while the broadband diffuse radiation shows differences of 2% and 5% over a horizontal plane and arbitrarily oriented surfaces, respectively. These results support the developed methodology and allow us to glimpse the great potential of sky-cameras to carry out accurate measurements of sky radiance and solar radiation components. Thus, the remote sensing techniques described here will undoubtedly be of great help for solar and atmospheric research.

We propose a methodology to derive the aerosol optical depth (AOD) and Angstrom exponent (AE) from calibrated images of an all-sky camera. It is based on a machine learning (ML) approach that establishes a relationship between AERONET measurements of AOD and AE and different signals derived from the principal plane radiance measured by an all-sky camera at three RGB channels. Gaussian process regression (GPR) has been chosen as machine learning method and applied to four models that differ in the input choice: RGB individual signals to predict spectral AOD; red signal only to predict spectral AOD and AE; blue-to-red ratio (BRR) signals to predict spectral AOD and AE; red signals to predict spectral AOD and AE at once. The novelty of our approach mostly relies on obtaining a cloud-screened and smoothed signal that enhances the aerosol features contained in the principal plane radiance and can be applied in partially cloudy conditions. In addition, a quality assurance criterion for the prediction has been also suggested, which significantly improves our results. When applied, our results are very satisfactory for all the models and almost all predictions are close to real values within ±0.02 for AOD and ±0.2 for AE, whereas the MAE is less than 0.005. They show an excellent agreement with AERONET measurements, with correlation coefficients over 0.92. Moreover, more than 87% of our predictions lie within the AERONET uncertainties (±0.01 for AOD, ±0.1 for AE) for all the output parameters of the best model. All the models offer a high degree of numerical stability with negligible sensitivities to the training data, atmospheric conditions and instrumental issues. All this supports the strength and efficiency of our models and the potential of our predictions. The optimum performance shown by our proposed methodology indicates that a well-calibrated all-sky camera can be routinely used to accurately derive aerosol properties. Together, all this makes the all-sky cameras ideal for aerosol research and this work may represent a significant contribution to the aerosol monitoring.