Cloud cover forecasting from METEOSAT data

Solar thermoelectric energy has a great potential as an energy supplier in many countries around the world. Since clouds are the main cause to solar radiation blocking, short term cloud forecasting can help power plant operation and therefore improve benefits. Therefore, cloud detection, classification and motion vector determination are key to forecast sun obstruction by clouds. Geostationary satellites provide cloud information covering wide areas, allowing cloud forecast to be performed for several hours in advance. Herein, the methodology developed and tested in this study is based on multispectral tests and binary cross correlations followed by coherence and quality control tests over resulting motion vectors. The following methodology utilizes Meteosat Second Generation imagery. In addition, pyrheliometric data and a whole-sky camera have also been used to test the methodology results. Cloud classification in terms of opacity and height of cloud top is also performed. Results show an agreement above 90% between satellite detected and observed cloud cover for cloudless and overcast situations and over 75% for partially cloudy skies, whereas around the 86% of the motion vectors are well determined. This work represents the starting point for addressing the prediction of solar radiation to short time using satellite imagery. (C) 2014 The Authors. Published by Elsevier Ltd.