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 Çukurova 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 Çukurova region. Thus, meteorological and geographical data (altitude, temperature, pressure, and humidity) were used in the input layer of the network for Çukurova 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.

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In this study, the method of Becker and Li was proposed for the estimation of monthly global land surface temperature values from meteorological satellite (NOAA-AVHRR) data. This study introduces generalized regression neural network for the estimation of solar radiation. In order to train the neural network, meteorological satellite and geographical data for the period from 2002 for short term (Adana) and 1998-2002 for long term (İzmir) in Turkey was used. Meteorological satellite and geographical data (latitude, longitude, altitude, month, and mean land surface temperature) are used in the input layer of the network. Solar radiation is the output. Root mean squared and correlation coefficient data between estimated and ground values are found with artificial neural networks values. These values have been found to be 0.0144 MJm 2 and 99.75% (short term) and 0.1381 MJm 2 and 99.26% (long term), respectively. In recent studies, there are some effective techniques about prediction solar radiation data, which is useful to the designers of solar energy systems. Nevertheless, there is no study about the prediction of solar radiation, which has used the artificial neural networks method with land surface temperature data provided from meteorological satellite data.

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