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CloudNet: Ground‐Based Cloud Classification With Deep Convolutional Neural Network
Author(s) -
Zhang Jinglin,
Liu Pu,
Zhang Feng,
Song Qianqian
Publication year - 2018
Publication title -
geophysical research letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.007
H-Index - 273
eISSN - 1944-8007
pISSN - 0094-8276
DOI - 10.1029/2018gl077787
Subject(s) - cloud computing , cirrus , meteorology , convolutional neural network , data set , environmental science , cloud top , computer science , set (abstract data type) , remote sensing , machine learning , artificial intelligence , geography , programming language , operating system
Clouds have an enormous influence on the Earth's energy balance, climate, and weather. Cloud types have different cloud radiative effects, which is an essential indicator of the cloud effect on radiation. Therefore, identifying the cloud type is important in meteorology. In this letter, we propose a new convolutional neural network model, called CloudNet, for accurate ground‐based meteorological cloud classification. We build a ground‐based cloud data set, called Cirrus Cumulus Stratus Nimbus, which consists of 11 categories under meteorological standards. The total number of cloud images is three times that of the previous database. In particular, it is the first time that contrails, a type of cloud generated by human activity, have been taken into account in the ground‐based cloud classification, making the Cirrus Cumulus Stratus Nimbus data set more discriminative and comprehensive than existing ground‐based cloud databases. The evaluation of a large number of experiments demonstrates that the proposed CloudNet model could achieve good performance in meteorological cloud classification.