Physical and statistical approaches for cloud identification using Meteosat Second Generation-Spinning Enhanced Visible and Infrared Imager data

In this paper a cloud detection algorithm applied to the MSG-SEVIRI (Metcosat Second Generation-Spinning Enhanced Visible and Infrared Imager) data is described. In order to obtain a good performance in cloud detection, physical, statistical and temporal approaches have been used. In the statistical algorithm, the spectral and textural features of the MSG-SEVIRI images have been used as input, while, in the physical tests, a set of dynamic thresholds has been used. The physical algorithm does not use real time ancillary data- such as sea surface temperature map and NWP temperature and humidity profiles. A further test is applied to that pixels having low confidence to be clear or cloudy. This test takes advantage of the best MSG-SEVIRI temporal resolution and it applies the K-Nearest Neighbour classifier to the spectral and textural features calculated in "temporal" boxes 3 x 3 pixels, defined "temporal" because their elements belong to three subsequent MSG-SEVIRI images. The MACSP (cloud MAsk Coupling of Statistical and Physical methods) algorithm has been validated against the MODIS cloud mask and compared with CPR (Cloud Profiling Radar) and SAFNWC cloud masks. The outcomes show that the MACSP detects 91.8% of the total number of the pixels used for validation against MODIS cloud mask correctly, while the SAFNWC cloud mask detects 89.2% of them correctly. In particular, the MACSP classifies as cloudy 8.8% of the pixels classified by the MODIS cloud mask as clear, while the SAFNWC cloud mask classifies as cloudy 12.1% of them. The MACSP detects 91.2% of the cloudy CPR pixels and 90.8% of the cloud-free CPR pixels, considered for comparison, correctly. On the other hand, the SAFNWC and CPR cloud masks agree in the detection of 90.7% of the cloudy pixels and of 90.2% of the cloud-free pixels. (C) 2008 Elsevier Inc. All rights reserved.