The Evolution of Meteorological Satellite Cloud-Detection Methodologies for Atmospheric Parameter Retrievals

Abstract

The accurate detection of clouds is an important first step in the processing of remotely sensed satellite data analyses and subsequent cloud model predictions. While initial cloud retrieval technology began with the exploitation of one or two bands of satellite imagery, it has accelerated rapidly in recent years as sensor and retrieval technology, creating a new era in space observation exploration. Additionally, the initial emphasis in satellite retrieval technology focused on cloud detection for cloud forecast models, but more recently, cloud screening in satellite-acquired data is playing an increasingly critical role in the investigation of cloud-free data for the retrieval of soil moisture, vegetation cover, ocean color concentration and sea surface temperatures, as well as the environmental monitoring of a host of products, e.g., atmospheric aerosol data, to study the Earth’s atmospheric and climatic systems. With about 60% of the Earth covered by clouds, on average, it is necessary to accurately detect clouds in remote sensing data to screen cloud contaminate data in remote sensing analyses. In this review, the evolution of cloud-detection methodologies is highlighted with advancement in sensor hardware technology and machine learning algorithmic advances. The review takes into consideration the meteorological sensors usually used for atmospheric parameters estimation (thermodynamic profiles, aerosols, cloud microphysical parameters). Moreover, a discussion is presented on methods for obtaining the cloud-truth data needed to determine the accuracy of these cloud-detection approaches.