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

<p>The file uploaded here is an animation that visually illustrates the outputs of the a newly developed machine learning based FLS (<strong>F</strong>og and <strong>L</strong>ow <strong>S</strong>tratus) detection algorithm for the SEVIRI (<strong>S</strong>pinning <strong>E</strong>nhanced <strong>V</strong>isible and <strong>I</strong>nfra<strong>R</strong>ed <strong>I</strong>mager) instrument onboard the MSG (<strong>M</strong>eteosat <strong>S</strong>econd <strong>G</strong>eneration) geo-stationary satellites over the 24hr cycle of the day for the day of <strong>02/March/2021</strong> 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&nbsp;- BT12.0, BT10.8&nbsp;- BT12.0, and BT12.0&nbsp;- 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.&nbsp;</p><p><br>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&nbsp;- BT12.0, and BT10.8&nbsp;- 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.</p>