RT info:eu-repo/semantics/article
T1 A neural network to retrieve cloud cover from all‐sky cameras: A case  of study over Antarctica
A1 González Fernández, Daniel
A1 Román Díez, Roberto
A1 Antuña Sánchez, Juan Carlos
A1 Cachorro Revilla, Victoria Eugenia
A1 Copes, Gustavo
A1 Herrero Anta, Sara
A1 Herrero del Barrio, Celia
A1 Barreto, África
A1 González Caton, Ramiro
A1 Ramos, Ramón
A1 Martín Sánchez, Patricia
A1 Mateos Villán, David
A1 Toledano Olmeda, Carlos
A1 Calle Montes, Abel
A1 Frutos Baraja, Ángel Máximo de
K1 AI
K1 all-sky camera
K1 Antarctic
K1 cloud cover
K1 convolutional neural network
K1 image identification
K1 2509 Meteorología
K1 1203.04 Inteligencia Artificial
AB 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 oktas and SD = 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.
PB Wiley
SN 0035-9009
YR 2024
FD 2024
LK https://uvadoc.uva.es/handle/10324/75160
UL https://uvadoc.uva.es/handle/10324/75160
LA eng
NO Quarterly Journal of the Royal Meteorological Society, 2024, vol. 150, n. 764, A, p. 4631-4649
NO Producción Científica
DS UVaDOC
RD 01-mar-2025