A neural network to retrieve cloud cover from all‐sky cameras: A case of study over Antarctica
https://doi.org/10.1002/qj.4834
Autores
Daniel González‐Fernández, Roberto Román, Juan Carlos Antuña‐Sánchez, Victoria E Cachorro, Gustavo Copes, Sara Herrero‐Anta, Celia Herrero del Barrio, África Barreto, Ramiro González, Ramón Ramos, Patricia Martín, David Mateos, Carlos Toledano, Abel Calle, Ángel de Frutos
Fecha de publicación 2024/08/28
Revista Quarterly Journal of the Royal Meteorological Society
Volumen 150
Número 764
Páginas 4631-4649
Editor John Wiley & Sons, Ltd.
Descripción
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 ±$$ \pm $$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 …