We present a new model based on a convolutional neural network (CNN) to predict daytime cloud cover (CC) from sky images captured by all‐sky cameras, which is called CNN‐CC. A total of 49,016 daytime sky images, recorded at different Spanish locations (Valladolid, La Palma, and Izaña) from two different all‐sky camera types, are manually classified into different CC (oktas) values by trained researchers. Subsequently, the images are randomly split into a training set and a test set to validate the model. The CC values predicted by the CNN‐CC model are compared with the observations made by trained people on the test set, which serve as reference. The predicted CC values closely match the reference values within 1 oktas in 99% of the cloud‐free and overcast cases. Moreover, this percentage is above 93% for the rest of partially cloudy cases. The mean bias error (MBE) and standard deviation (SD) of the differences between the predicted and reference CC values are calculated, resulting in oktas and oktas. The MBE and SD are also represented for different intervals of measured aerosol optical depth and Ångström exponent values, revealing that the performance of the CNN‐CC model does not depend on aerosol load or size. Once the model is validated, the CC obtained from a set of images captured every 5 min, from January 2018 to March 2022, at the Antarctic station of Marambio (Argentina) is compared against direct field observations of CC (not from images) taken at this location, which is not used in the training process. As a result, the model slightly underestimates the observations with an MBE of 0.3 oktas. The retrieved data are analyzed in detail. The monthly and annual CC values are calculated. Overcast conditions are the most frequent, accounting for 46.5% of all observations throughout the year, rising to 64.5% in January. The annual mean CC value at this location is 5.5 oktas, with a standard deviation of approximately 3.1 oktas. A similar analysis is conducted, separating data by hours, but no significant diurnal cycles are observed except for some isolated months.

Aerosol optical depth (AOD) at night-time has become a hot topic in recent years due to the development of new instruments recording accurate ground-based lunar irradiance measurements, and the development of calibration methods and extraterrestrial irradiance models adapted to lunar photometry. This study uses all daytime and night-time AOD data available at Valladolid (Spain) from October 2016 to March 2022 in order to analyze its behavior and the added contribution of night data. The annual, monthly and daily AOD evolution is studied comparing daytime and night-time values and checking the correlation between them. For this purpose, the daily averages are computed, showing an annual pattern, with low AOD values throughout the year (mean value of AOD at 440 nm: 0.122), where winter months have the lower and summer the higher values, as observed in previous studies. All these AOD values are modulated by frequent desert dust events over the Iberian Peninsula, with a strong influence on daily and monthly mean values in February and March, where the strongest desert outbreaks occurred. The added new data confirm these results and the good correlation between daytime and night-time data. Also, a complete daily evolution is shown, observing that AOD and Ångström exponent (AE) mean values vary by only ±0.02 in 24 h, with a maximum value at 06:00 UTC and minimum at 18:00 UTC for both parameters.

The lack of aerosol optical depth (AOD) data at night can be partially addressed through Moon photometer measurements or fully covered with star photometer observations. However, the limited availability and complexity of star photometers have motivated this study to use all-sky cameras to extract starlight signals and derive AOD at night using star photometry. For this purpose, eight all-sky cameras were configured and deployed in nine different locations to capture raw images with varying exposure times every 2 min during the night. This work proposes a novel methodology to extract the starlight signal from the raw data from all-sky cameras and convert it into AOD values. This process consists of the following steps: removing the background image, selecting the pixels, and extracting the signal for each star from a predefined list of 56 stars; performing in situ Langley calibration of the instruments and retrieving the total optical depth (TOD); calculating the effective wavelength for each camera channel; deriving the AOD by subtracting the gas contribution to TOD; and averaging, cloud-screening, and quality-assuring the AOD time series. The AOD time series obtained through this methodology are compared with independent AOD measurements from collocated Moon photometers in the nine locations. The obtained results show that the AOD values derived with the proposed method generally correlate with reference values, often achieving correlation coefficients (r) above 0.90. The AOD values retrieved using the cameras tend to overestimate the reference values by approximately 0.02 and exhibit a precision of around 0.03–0.04. The agreement between the two datasets varies with wavelength and decreases at high-latitude locations, likely due to the poorer performance of Langley calibration in these regions. AOD values align well with day-to-night transitions obtained by solar photometers, demonstrating their reliability. Despite the slight overestimation, the AOD values derived by this new method approximate the real values and provide coverage throughout the entire night, without requiring the presence of the Moon. Therefore, they serve to study and monitor the nocturnal evolution of AOD.

The present work proposes a new model based on a convolutional neural network (CNN) to retrieve solar shortwave (SW) irradiance via the estimation of the cloud modification factor (CMF) from daytime sky images captured by all-sky cameras; this model is named CNN-CMF. To this end, a total of 237,669 sky images paired with SW irradiance measurements obtained by using pyranometers were selected at the following three sites: Valladolid and Izaña, Spain, and Lindenberg, Germany. This dataset was randomly split into training and testing sets, with the latter excluded from the training model in order to validate it using the same locations. Subsequently, the test dataset was compared with the corresponding SW irradiance measurements obtained by the pyranometers in scatter density plots. The linear fit shows a high determination coefficient ($R^2$) of $0.99$. Statistical analyses based on the mean bias error (MBE) values and the standard deviation (SD) of the SW irradiance differences yield results close to $-2\%$ and $9\%$, respectively. The MBE indicates a slight underestimation of the CNN-CMF model compared to the measurement values. After its validation, model performance was evaluated at the Antarctic station of Marambio (Argentina), a location not used in the training process. A similar comparison between the model-predicted SW irradiance and pyranometer measurements yielded $R^2=0.95$, with an MBE of around $2\%$ and an SD of approximately $26\%$. Although the precision provided by the SD at the Marambio station is lower, the MBE shows that the model’s accuracy is similar to previous results but with a slight overestimation of the SW irradiance. Finally, the determination coefficient improved to $0.99$, and the MBE and SD are about $3\%$ and $11\%$, respectively, when the CNN-CMF model is used to estimate daily SW irradiation values.

This work introduces CAECENET, a new system capable of automatically retrieving columnar and vertically-resolved aerosol properties running the GRASP (Generalized Retrieval of Atmosphere and Surface Properties) algorithm using sun-sky photometer (aerosol optical depth, AOD; and sky radiance measurements) and ceilometer (range corrected signal; RCS) data as input. This method, so called GRASP_pac, is implemented in CAECENET, which assimilates sun-sky photometers data from CÆLIS database and ceilometer data from ICENET database (Iberian Ceilometer Network). CAECENET allows for continuous and near-real-time monitoring of both vertical and columnar aerosol properties. The main characteristics and workflow of CAECENET are explained in detail. This work also explores the potential of CAECENET to monitor and analyze the evolution of transported aerosol events on a regional scale by means of the distribution of CAECENET stations across the Iberian Peninsula. As an example, this paper analyzes, using the CAECENET products, the case of a Saharan dust outbreak that occurred between the 3rd and 5th of October 2022. This was an intense event, with AOD at 440 nm values around 0.5 in Madrid and Valladolid, and reaching 1.55 in Granada. Transport from the Canadian wildfires at the end of June 2023 is also studied. Despite the long-range transport of the smoke particles in this event, measured volume concentrations reached and surpassed 80 μm³/cm³ in some stations. The results obtained point to the utility of this CAECENET tool for analyzing changes in the height and speed of the event propagation, in the aerosol concentration, and how this affects the optical properties.

The A-LIFE (Absorbing aerosol layers in a changing climate: aging, lifetime, and dynamics) field experiment, conducted in Cyprus in April 2017, employed a wide range of ground-based and airborne instruments, including passive/active remote sensing and in-situ techniques. This study presents the columnar records obtained by sun photometry. Two sun/sky/lunar photometers, belonging to AERONET network, were strategically placed at two sites: Pafos and Limassol, 40 km apart. Aerosol optical and microphysical properties derived from direct sun and sky radiance measurements are analysed to determine an inventory of aerosol event days during the experiment, with mineral dust being present in nearly 75 % of days. Ångström exponent values obtained from 1020 and 1640 nm channels (0.5 for Saharan dust and 0.34 for Arabian dust) can be served as a classification criterion. Dust sources are key point for a well understanding of the size distribution and absorption power. According to columnar records, Saharan dust exhibited less absorbing particles. The columnar volume efficiency factor (linear fit between aerosol optical depth and total volume concentration) was proved as a reliable proxy for the identification of dust origin since Arabian and Saharan dusts exhibit different slopes: 1.28 and 1.68 µm2 µm−3, respectively. Analysing all the inversion products mixtures of mineral dust were mainly dominated by Arabian dust, while mixtures of fine and coarse aerosols showed no clear prevalence of dust origin. No significant presence of black carbon-rich aerosols was detected in the atmospheric column, as absorption Ångström exponent values ranged from 1.6 to 3 across aerosol types identified.