Cloud Analysis Method Research Articles

Comparison of a Split-window and a Multi-spectral Cloud Classification for MODIS Observations

Results of the split-window cloud retrieval method and the new Meteosat Second Generation cloud analysis method (MSG/CLA), have been compared for MODIS data over the west Atlantic Ocean. Very good agreement is obtained for the classification of optically thick ice and water clouds. Differences are found for thin cirrus, thin water clouds and at cloud edges. These differences are explained by the fact that MSG/CLA also uses spectral channels of 3.9, 6.2, and 8.7 mm in addition to the split-window, which provides information over and above the split-window observations. Some of the disagreement at cloud edges is interpreted as inter-channel miss-alignment. The analysis in this study also confirms that an optically thin water cloud can be correctly classified by the MSG/CLA method.

The histogram of the brightness distribution of clouds in high‐resolution remotely sensed images

A modeling algorithm for the brightness histogram of stratocumulus, cumulus, and fair‐weather‐cumulus clouds (in short, “convective clouds”) is proposed. The algorithm is based on the similarity between the beta probability density function, and the convective cloud brightness histogram. On assuming that the image brightness histogram's right wing (the high values in the solar spectral range) is free of background contribution, the cloud histogram may be extracted. The algorithm gives new information on the number of cloudy pixels in an image, using the same data as used for conventional cloud analysis methods. This information improves the estimation of cloud fraction of the image and aids in the selection of thresholds for cloud‐background separation. It provides a new perspective on the data that will be helpful when using conventional methods for cloud detection and classification.