Cloud Cover Forecasting from METEOSAT Data

Abstract

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.

Afyonkarahisar Bölgesi Şartlarında Bulut Hareketlerinin Gökyüzü Sınıfları Tabanlı Tahmini

“…Bulut örtüsünün güneş ışınımı üzerine olan etkilerinin incelendiği birçok çalışmada da bulut hareketlerinin güneş ışınımı değişiminde anahtar unsur olduğu ortaya konulmuştur. [5,10,11].…”

Attenuation Factor Estimation of Direct Normal Irradiance Combining Sky Camera Images and Mathematical Models in an Inter-Tropical Area

“…R.Chauvin and J. Nou, forecasted the cloud cover for different sky conditions over 5-30 min with a spatial resolution of (1 km 2 ) using sky images and algorithm development [27,28]. F. Batlles and J. Montesinos established that detecting and classifying clouds as well as determining their trajectory are essential factors in forecasting cloud cover [29]. Likewise, they demonstrated that infrared channels are essential for cloud height assignment and the visible channel is necessary for cloud opacity determination; however, this was solely applied to short-term cloud forecasting using multispectral satellite imagery [29,30].…”

“…F. Batlles and J. Montesinos established that detecting and classifying clouds as well as determining their trajectory are essential factors in forecasting cloud cover [29]. Likewise, they demonstrated that infrared channels are essential for cloud height assignment and the visible channel is necessary for cloud opacity determination; however, this was solely applied to short-term cloud forecasting using multispectral satellite imagery [29,30]. It is clear that a wide range of different technologies and systems have been used to estimate the solar resource; nonetheless, most publications focus on solar resource assessment and not on estimating the attenuation factor.…”

Determination of Cloud Motion Applying the Lucas-Kanade Method to Sky Cam Imagery

“…The successful integration of solar electricity from CSP plants into the existing electricity supply requires electricity production forecasting of between 24-48 h to cover the current day and the day ahead [5,6]. Cloud cover has been widely studied in different places around the world, above all to examine the role of clouds in solar radiation measurements [7][8][9][10]. Research studies have applied digitized image analysis to satellite images and sky cameras to study solar radiation estimation and solar radiation attenuation caused by the aerosol column and clouds; this has resulted in estimates with 82% certainty for all sky types [10][11][12][13].…”