Cloud Cover Forecasting from METEOSAT Data

AbstractSolar 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

Effect of contrast on the perception of direction of a moving pattern

A series of experiments examining the effect of contrast on the perception of moving plaids was performed to test the hypothesis that the human visual system determines the direction of a moving plaid in a two-staged process: decomposition into component motion followed by application of the intersection-of-contraints rule. Although there is recent evidence that the first tenet of the hypothesis is correct, i.e., that plaid motion is initially decomposed into the motion of the individual grating components, the nature of the second-stage combination rule has not yet been established. It was found that when the gratings within the plaid are of different contrast the preceived direction is not predicted by the intersection-of-constraints rule. There is a strong (up to 20 deg) bias in the direction of the higher-constrast grating. A revised model, which incorporates a contrast-dependent weighting of perceived grating speed as observed for one-dimensional patterns, can quantitatively predict most of the results. The results are then discussed in the context of various models of human visual motion processing and of physiological responses of neurons in the primate visual system

Robust Vehicle Detection with Optical Flow

本論文ではオプティカルフローを用いて，精度の高い移動物体の認識を行う手法を提案する．オプティカルフローを求める手法のひとつに勾配法があるが，この方法は時空間における画像の濃度勾配と，オプティカルフロー速度を結びつける拘束式からフローベクトルを求めるものである．この拘束条件としてオプティカルフロー速度が画像全体でなめらかに変化することがよく用いられているが，この際,用いるパラメータの値は経験的に判断するか何度か計算を行って決定していた．本論文で提案する方法ではあらかじめ2つのパラメータの値を設定し，それぞれの値における計算結果を融合した処理を行うことで，フローベクトルの計算精度が向上した．本手法の有効性は実験によって確認された．We present a robust vehicle detection method with optical flow which calculates image velocity from spatiotemporal intensity derivatives. This method is based on the regularization technique which is reported by Horn and Schunk. In our proposed method the position where optical flow exists is calculated with a higher regularized parameter-value and the average flow vector within a vehicle is calculated on above positions by a smaller regularized parameter-value. The performance of the proposed method was clarified by the simulation

Prediction of satellite cloud patterns using spatial Fourier transforms.

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Meteorology, 1972.Bibliography: leaves 44-45.M.S

The state-of-the-art progress in cloud detection, identification, and tracking approaches: a systematic review

A cloud is a mass of water vapor floating in the atmosphere. It is visible from the ground and can remain at a variable height for some time. Clouds are very important because their interaction with the rest of the atmosphere has a decisive influence on weather, for instance by sunlight occlusion or by bringing rain. Weather denotes atmosphere behavior and is determinant in several human activities, such as agriculture or energy capture. Therefore, cloud detection is an important process about which several methods have been investigated and published in the literature. The aim of this paper is to review some of such proposals and the papers that have been analyzed and discussed can be, in general, classified into three types. The first one is devoted to the analysis and explanation of clouds and their types, and about existing imaging systems. Regarding cloud detection, dealt with in a second part, diverse methods have been analyzed, i.e., those based on the analysis of satellite images and those based on the analysis of images from cameras located on Earth. The last part is devoted to cloud forecast and tracking. Cloud detection from both systems rely on thresholding techniques and a few machine-learning algorithms. To compute the cloud motion vectors for cloud tracking, correlation-based methods are commonly used. A few machine-learning methods are also available in the literature for cloud tracking, and have been discussed in this paper too

Forecasting for concentrated solar thermal power plants in Australia

Up to 50% of electricity needs in Australia could be supplied by solar power. At these high levels of solar power generation, solar forecasting is necessary to manage the impact of solar variability. However, there has been little research on using solar forecasting in Australia. This study used modelling to investigate the benefits of using short-term and long-term solar forecasts to operate a concentrated solar thermal (CST) plant for a year at four sites that covered different climate zones within the Australian National Electricity Market. Using 1-hour ahead short-term forecasts increased net value by $0.90-$2.07 million for a CST plant with storage, and by $0.76-$3.10 million for a CST plant without storage. It also improved reliability by reducing the equivalent forced outage rate by 21-38 percentage points for a CST plant with storage, and by 16-42 percentage points for a CST plant without storage. Using 1-hour forecasts achieved 59%-94% of the net value achievable if the 48-hour forecast were perfect. At each site, the highest net value and reliability were achieved by a CST plant with storage and using 1-hour forecasts, thus a CST plant should have both storage and short-term forecasts. If only one can be used, then a CST plant with storage and without 1-hour forecasts achieves higher net value, whereas a CST plant without storage and with 1-hour forecasts achieves higher reliability. These results demonstrated that using short-term forecasts is beneficial for CST plants that operate in electricity markets that allow updated bids to be submitted at short-term time frames. The results can be used to estimate the return on investment in obtaining short-term forecasts for operating a CST plant. Furthermore, the research method can be adapted into a tool for estimating value to assist CST plant project planning