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@article{rs16203821,

title = {Retrieval of Solar Shortwave Irradiance from All-Sky Camera Images},

author = {Daniel González-Fernández and Roberto Román and David Mateos and Celia Herrero del Barrio and Victoria E. Cachorro and Gustavo Copes and Ricardo Sánchez and Rosa Delia García and Lionel Doppler and Sara Herrero-Anta and Juan Carlos Antuña-Sánchez and África Barreto and Ramiro González and Javier Gatón and Abel Calle and Carlos Toledano and Ángel Frutos},

url = {https://www.mdpi.com/2072-4292/16/20/3821},

doi = {10.3390/rs16203821},

issn = {2072-4292},

year  = {2024},

date = {2024-10-14},

urldate = {2024-01-01},

journal = {Remote Sensing},

volume = {16},

number = {20},

abstract = {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 (R2) 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 R2=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.},

keywords = {all-sky camera, Antarctica, cloud modification factor, convolutional neural network, shortwave global horizontal irradiance, sky images},

pubstate = {published},

tppubtype = {article}

}

@article{gonzalez2024CCNeural,

title = {A neural network to retrieve cloud cover from all-sky cameras: A case of study over Antarctica},

author = {Daniel González-Fernández and Roberto Román and Juan Carlos Antuña-Sánchez and Victoria E. Cachorro and Gustavo Copes and Sara Herrero-Anta and Celia Herrero del Barrio and África Barreto and Ramiro González and Ramón Ramos and Patricia Martín and David Mateos and Carlos Toledano and Abel Calle and Ángel Frutos},

url = {https://rmets.onlinelibrary.wiley.com/doi/abs/10.1002/qj.4834},

doi = {https://doi.org/10.1002/qj.4834},

year  = {2024},

date = {2024-08-28},

journal = {Quarterly Journal of the Royal Meteorological Society},

volume = {n/a},

number = {n/a},

abstract = {Abstract 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 MBE=0.007$$ mathrmMBE=0.007 $$?oktas and SD=0.674$$ mathrmSD=0.674 $$?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.},

keywords = {AI, all-sky camera, Antarctic, cloud cover, convolutional neural network, image identification},

pubstate = {published},

tppubtype = {article}

}