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

Abstract

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.

An Ensemble Approach for Intra-Hour Forecasting of Solar Resource

“…In principle, in compliance with their nature, the forecasting models can be divided into five large classes: classical statistics, machine learning, cloud-motion tracking, numerical weather prediction, and hybrid models. Classical statistical models [9], machine learning [10], and cloud-motion tracking models [11] were applied with success for intrahour forecasts. Accordingly, this study deals with models from these three classes.…”

Impact of Multi-Thresholds and Vector Correction for Tracking Precipitating Systems over the Amazon Basin

“…Algorithms like ForTraCC (Forecast and Tracking the Evolution of Cloud Clusters) [11] and TITAN (Thunderstorm Identification, Tracking, Analysis, and Nowcasting) [12] utilize techniques based on the boundary thresholds of rain cells to track their displacement and life cycle. In recent years, several algorithms based on techniques such as contour thresholds, centroid, spatial cross-correlation, etc., have been developed and used to identify, track, and forecast very short-term (i.e., nowcasting) large-intensity precipitation events [13]. In a similar way to the aforementioned algorithms, the tool created for this study also uses thresholding and tracking via overlapping object areas.…”