A model for the estimation of the daily global sunshine duration from meteorological geostationary satellite data

In this research a simple model was proposed for the estimation of daily global sunshine duration and constructing spatially continous sunshine duration map from meteorological geostationary satellite sensor data. First cloud cover index and then daily mean cloud cover index value for each pixel were computed using a time-series of Meteosat C3D visible type images. The statistical relationship between daily mean cloud cover index and measured bright sunshine duration values was tested and found to be linear. Using regression parameters daily sunshine duration of each pixel was calculated and then daily sunshine duration and monthly mean daily sunshine duration maps were constructed. It was shown that, by using the suggested model it is possible to calculate daily sunshine duration values over a large area where the sunshine duration data are not available and construct spatially continious long-term sunshine duration maps

Estimation of daily sunshine duration from terra and aqua Modis data

Some studies have shown that the estimation of global sunshine duration can be done with the help of geostationary satellites because they can record several images of the same location in a day. In this paper, images obtained from the MODIS (Moderate Resolution Imaging Spectroradiometer) sensors of polar orbiting satellites Aqua and Terra were used to estimate daily global sunshine duration for any region in Turkey. A new quadratic correlation between daily mean cloud cover index and relative sunshine duration was also introduced and compared with the linear correlation. Results have shown that polar orbiting satellites can be used for the estimation of sunshine duration. The quadratic model introduced here works better than the linear model especially for the winter months in which very low sunshine duration values were recorded at the ground stations for many days. © 2014 H. M. Kandirmaz and K. Kaba

Estimation of monthly sunshine duration in Turkey using artificial neural networks

This paper introduces an artificial neural network (ANN) approach for estimating monthly mean daily values of global sunshine duration (SD) for Turkey. Three different ANN models, namely, GRNN, MLP, and RBF, were used in the estimation processes. A climatic variable (cloud cover) and two geographical variables (day length and month) were used as input parameters in order to obtain monthly mean SD as output. The datasets of 34 stations which spread across Turkey were split into two parts. First part covering 21 years (1980-2000) was used for training and second part covering last six years (2001-2006) was used for testing. Statistical indicators have shown that, GRNN and MLP models produced better results than the RBF model and can be used safely for the estimation of monthly mean SD. Copyright © 2014 H. M. Kandirmaz et al

Estimation of daily sunshine duration using support vector machines

Although sunshine duration (SD) is one of the most frequently measured meteorological parameters, there is a lack of measurements in some parts of the world. Hence, it should be estimated accurately for areas where no reliable measurement is possible. The main objective of this study is to evaluate the potential of support vector machine (SVM) approach for estimating daily SD. For this purpose, three different kernels of SVM, such as linear, polynomial, and radial basis function (RBF), were used. Different combinations of five related meteorological parameters, namely cloud cover, maximum temperature (Tmax), minimum temperature (Tmin), relative humidity (RH), and wind speed (WS), and one astronomic parameter, day length, were considered as the inputs of the models, and the output was obtained as daily SD. Simulated values of the models were compared with ground measured values, and concluded that the usage of the SVM-RBF estimator with combination of all input attributes produced the best results. The coefficient of determination, root mean square error, and mean absolute error were found to be 0.8435, 1.5105 h, and 1.0771 h, respectively, for the pooled four-year daily data set of 14 stations in Turkey. It was also deduced that accuracy increased as the number of attributes increased and the major contribution to this came from RH as compared with Tmax, Tmin, and WS. This study has shown that the SVM methodology can be a good alternative for conventional and artificial neural network methods for estimating daily SD. © 2017 Taylor & Francis Group, LLC

Determination of the land surface temperature of the çukurova region using NOAA APT data

Many studies have indicated that the estimation of the land surface temperature using the NOAA satellite analog picture transmission (APT) images is an alternative and easy method compared with other classical methods. In the present work, land surface temperatures of the Çukurova Region in Turkey were estimated during the months of April and July 1998 by using NOAA APT data. DARTCOM hardware and Winsat Pro32 software were used to receive and rectify the APT data. These rectified APT images were used to calculate the surface temperature, and then the results were compared with the meteorological ground based measurements. Comparison of both sets of data indicated a correlation coefficient of 0.97. The rms error for the calculated temperature was evaluated as 1.2°C. A surface temperature map of the Çukurova Region was obtained for 12 April, 1998. As a result of this study, it was concluded that the land surface temperature can be determined by using the NOAA APT data with reasonable precision. © 2004 THE PHYSICAL SOCIETY OF THE REPUBLIC OF CHINA

Daily global solar radiation mapping of Turkey using Meteosat satellite data

Many studies have indicated that the estimation of solar irradiation at ground level using meteorological satellite data has been an alternative and easy method compared to classical methods. In the present work, the incident of solar radiation over Turkey has been estimated at ground level between July 1997 and December 1998. Statistical regressions between ground data and digital satellite data, measured in the visible band (0.4-1.1 µm) by Meteosat radiometer, have been determined and these regression parameters have been used to estimate solar radiation at ground level. This is the so-called statistical method, which uses a simple model because satellites measure only a few parameters among the many that govern radiative transfers. The visible image (C3D) data used in the present work was Meteosat Wefax type. While pursuing our studies the mean daily sum of global solar radiation over Turkey has been determined to be 18.44 MJ m-2 d-1 with a correlation coefficient of 0.96. The rms error for the mean daily sum has been evaluated as 1.92 MJ m-2 d-1. The monthly mean daily sum of solar radiation has been determined with an rms error of 1.82 MJ m-2 d-1 in two years. During this period the maximum value of the daily sum has been found to occur in June 1998 as 28.47 MJ m-2 d-1, whereas the minimum has been found to occur in December 1998 as 7.35 MJ m-2 d-1. The evaluation procedure, results and possible sources of error are suggested and possible ways of improving the method are described and discussed. © 2004 Taylor & Francis Ltd

Comparison of a new algorithm with the supervised classifications

In this study, a new classification algorithm in which only the selected pixels have been attempted to be classified (selected pixels classification: SPC) has been introduced and compared with the well known supervised classification methods such as maximum likelihood, minimum distance, nearest neighbour and condensed nearest neighbour. To examine the algorithm. Landsat Thematic Mapper (TM) data have been used to classify the crop cover in the selected region. It is clearly demonstrated that the SPC method has the higher accuracy with comparable CPU times