In this work one year hourly solar radiation data are analyzed and modeled using a novel visualization method. Using a 2-D(Dimensional) surface fitting approach, the general behavior of the solar radiation in a year is modeled. By the help of the newly adopted visualization approach, a total of 9 analytical surface models are obtained and compared. The Gaussian surface model with proper model parameters is found to be the most accurate model among the tested analytical models for data characterization purposes. The accuracy of this surface model is tested and compared with a dynamic surface model obtained from a feed-forward Neural Network (NN). Analytical surface models and NN surface model are compared in the sense of Root, Mean Square Error (RMSE). It is obtained that the NN surface model gives better results with smaller RMSE values. However, unlike the specificity of the NN surface model, the analytical surface model provides a simple, intuitive and more generalized form that can be suitable for several geographical locations on earth.