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    Estimation of the vapour pressure deficit using NOAA-AVHRR data
    (Taylor & Francis Ltd, 2013) Sahin, Mehmet; Yildiz, Bekir Yigit; Senkal, Ozan; Pestemalci, Vedat
    In this study, the calculation of vapour pressure deficit (VPD) using the National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer (NOAA/AVHRR) satellite data set is shown. Twenty-four NOAA/AVHRR data images were arranged and turned to account for both VPD and land surface temperature (LST), which was necessary to calculate the VPD. The most accurate LST values were obtained from the Ulivieri et al. split-window algorithm with a root mean square error (RMSE) of 2.7K, whereas the VPD values were retrieved with an RMSE of 6mb. Furthermore, the VPD value was calculated on an average monthly basis and its correlation coefficient was found to be 0.991, while the RMSE value was calculated to be 2.67mb. As a result, VPD can be used in studies that examine plants (germination, growth, and harvest), controlling illness outbreak, drought determination, and evapotranspiration.
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    Forecasting of Air Temperature Based on Remote Sensing
    (Gazi Univ, 2012) Sahin, Mehmet; Yildiz, Bekir Yigit; Senkal, Ozan; Pestemalci, Vedat
    The aim of this research is to forecast air temperature based on remote sensing data. So, land surface temperature and air temperature values which were measured by Republic of Turkey Ministry of Forestry and Water Affairs (Turkish State Meteorological Service) during the period 1995-2001 at seven stations (Adana, Ankara, Balikesir, Dzmir, Samsun, Sanliurfa, Van) were compared. The monthly land surface temperature and air temperature were used to have correlation coefficients over Turkey. An empirical method was obtained from equation of correlation coefficients. Separately, Price algorithm was used for the estimation of land surface temperature values to get air temperatures. Then as statistical, air temperature values, belongs to meteorological data in Turkey (26-45 degrees E and 36-42 degrees N) throughout 2002, were evaluated. The research results showed that accuracy of estimation of the air temperature changes from 2.453 degrees K to 2.825 degrees K by root mean square error.
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    Modelling and Remote Sensing of Land Surface Temperature in Turkey
    (Springer, 2012) Sahin, Mehmet; Yildiz, B. Yigit; Senkal, Ozan; Pestemalci, Vedat
    This study introduces artificial neural networks (ANNs) for the estimation of land surface temperature (LST) using meteorological and geographical data in Turkey (26-45A degrees E and 36-42A degrees N). A generalized regression neural network (GRNN) was used in the network. In order to train the neural network, meteorological and geographical data for the period from January 2002 to December 2002 for 10 stations (Adana, Afyon, Ankara, EskiAYehir, A degrees stanbul, A degrees zmir, Konya, Malatya, Rize, Sivas) spread over Turkey were used as training (six stations) and testing (four stations) data. Latitude, longitude, elevation and mean air temperature are used in the input layer of the network. Land surface temperature is the output. However, land surface temperature has been estimated as monthly mean by using NOAA-AVHRR satellite data in the thermal range over 10 stations in Turkey. The RMSE between the estimated and ground values for monthly mean with ANN temperature(LSTANN) and Becker and Li temperature(LSTB-L) method values have been found as 0.077 K and 0.091 K (training stations), 0.045 K and 0.003 K (testing stations), respectively.
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    Precipitable water modelling using artificial neural network in Cukurova region
    (Springer, 2012) Senkal, Ozan; Yildiz, B. Yigit; Sahin, Mehmet; Pestemalci, Vedat
    Precipitable water (PW) is an important atmospheric variable for climate system calculation. Local monthly mean PW values were measured by daily radiosonde observations for the time period from 1990 to 2006. Artificial neural network (ANN) method was applied for modeling and prediction of mean precipitable water data in Cukurova region, south of Turkey. We applied Levenberg-Marquardt (LM) learning algorithm and logistic sigmoid transfer function in the network. In order to train our neural network we used data of Adana station, which are assumed to give a general idea about the precipitable water of Cukurova region. Thus, meteorological and geographical data (altitude, temperature, pressure, and humidity) were used in the input layer of the network for Cukurova region. Precipitable water was the output. Correlation coefficient (R-2) between the predicted and measured values for monthly mean daily sum with LM method values was found to be 94.00% (training), 91.84% (testing), respectively. The findings revealed that the ANN-based prediction technique for estimating PW values is as effective as meteorological radiosonde observations. In addition, the results suggest that ANN method values be used so as to predict the precipitable water.