The State-of-the-Art Progress in Cloud Detection, Identification, and Tracking Approaches: A Systematic Review

A cloud is a mass of water vapor floating in the atmosphere. It is visible from the ground and can remain at a variable height for some time. Clouds are very important because their interaction with the rest of the atmosphere has a decisive influence on weather, for instance by sunlight occlusion or...

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Bibliographic Details
Published in:Energies (Basel) 2021-12, Vol.14 (23), p.8119, Article 8119
Main Authors: Sawant, Manisha, Shende, Mayur Kishor, Feijóo-Lorenzo, Andrés E., Bokde, Neeraj Dhanraj
Format: Article
Language:English
Subjects:
Atmosphere
Cameras
cloud detection
cloud tracking
Clouds
Digital cameras
Earth
Ice
Learning algorithms
Meteorological satellites
Occlusion
Precipitation
Rain
Remote sensing
renewable energy
Satellite imagery
Satellites
Sea level
Sensors
solar irradiance
Systematic review
Water vapor
Weather forecasting
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Summary:A cloud is a mass of water vapor floating in the atmosphere. It is visible from the ground and can remain at a variable height for some time. Clouds are very important because their interaction with the rest of the atmosphere has a decisive influence on weather, for instance by sunlight occlusion or by bringing rain. Weather denotes atmosphere behavior and is determinant in several human activities, such as agriculture or energy capture. Therefore, cloud detection is an important process about which several methods have been investigated and published in the literature. The aim of this paper is to review some of such proposals and the papers that have been analyzed and discussed can be, in general, classified into three types. The first one is devoted to the analysis and explanation of clouds and their types, and about existing imaging systems. Regarding cloud detection, dealt with in a second part, diverse methods have been analyzed, i.e., those based on the analysis of satellite images and those based on the analysis of images from cameras located on Earth. The last part is devoted to cloud forecast and tracking. Cloud detection from both systems rely on thresholding techniques and a few machine-learning algorithms. To compute the cloud motion vectors for cloud tracking, correlation-based methods are commonly used. A few machine-learning methods are also available in the literature for cloud tracking, and have been discussed in this paper too.
ISSN:1996-1073
1996-1073
DOI:10.3390/en14238119