Supplement of "Algorithm for continual monitoring of fog life cycles based on geostationary satellite imagery as a basis for solar energy forecasting"

The file uploaded here is an animation that visually illustrates the outputs of the a newly developed machine learning based FLS (Fog and Low Stratus) detection algorithm for the SEVIRI (Spinning Enhanced Visible and InfraRed Imager) instrument onboard the MSG (Meteosat Second Generation) geo-stationary satellites over the 24hr cycle of the day for the day of 02/March/2021 and compares them with the corresponding raw channel values observed by SEVIRI. The proposed algorithm classifies each SEVIRI pixel as "clear-sky", "FLS", or "non-FLS-cloud" (identified with Khaki, Red, and Blue in the animation) based on the SEVIRI pixel values of BT12.0, BT8.7 - BT12.0, BT10.8 - BT12.0, and BT12.0 - BT13.4 plus the standard deviation of each of these variables in a spatial window sized 3x3 pixels with the central pixel being the target pixel. 

In this animation, the left-hand panel shows a false-color RGB image constructed based on the SEVIRI raw channel data with the red, green, and blue channels being BT12.0- BT13.4, BT8.7 - BT12.0, and BT10.8 - BT12.0, respectively. In this panel, the green color represents the high clouds, and the light and dark red colors represent the clear-sky and FLS, respectively. The right-hand panel of this animation also shows the outputs of the ML FLS detection algorithm developed in the present study.