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This paper explores the observation made by the Earth Probe Total Ozone Mapping Spectrometer EPTOMS to analyse the predictability of daily total ozone concentration over Kolkata, India. Latitude, longitude, aerosol index, reflectivity, sulphur dioxide index and total ozone concentration of a given day have been used as independent variables to predict total ozone concentration of the next day. Artificial neural network in the forms of a multilayer perceptron, generalized feed forward neural network, a radial basis function network and a modular neural network have been trained to generate predictive models. Performances of the models in the test cases have been judged with the help of four statistical parameters. Finally the models have been compared with multiple linear regression and the potential of generalized feed forward neural network has been established over the other proposed models. Copyright © 2008 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

The present article reports studies to identify a nonlinear methodology to forecast the time series of average summermonsoon rainfall over India. Three advanced backpropagation neural network learning rules namely, momentum learning, conjugate gradient descent CGD learning, and Levenberg–Marquardt LM learning, and a statistical methodology in the form of asymptotic regression are implemented for this purpose. Monsoon rainfall data pertaining to the years from 1871 to 1999 are explored. After a thorough skill comparison using statistical procedures the study reports the potential of CGD as a learning algorithm for the backpropagation neural network to predict the said time series. Copyright © 2008 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

This paper reports the development of a neurocomputingbased model for estimating the potential evapotranspiration over Gangetic West Bengal, India during the summer monsoon months of June, July and August. An artificial neural network is implemented in the form of multilayer perceptron to generate the model. Three weather variables, surface temperature, vapour pressure and rainfall are used as the independent variables in generating the model. The performance of the model is judged statistically against nonlinear regression in the form of asymptotic regression. The study reveals that an artificial neural network is more efficient than the regression approach to estimate the potential evapotranspiration in the summer monsoon months. Furthermore, it is established that the artificial neural network and nonlinear regression have almost equal efficiency in the previously mentioned estimation in the month of June. However, in July and August the higher values of correlation and Willmotts indices, and lower values of estimation error, indicate that the artificial neural network is more reliable than the nonlinear regression approach. Since evapotranspiration is one of the basic components of the hydrological cycle and is essential for estimating irrigation water requirement, an efficient estimation procedure may help in agrometeorological modelling and irrigation scheduling in the summer monsoon months, which are of high importance for agriculture in the study zone. Copyright © 2009 Royal Meteorological Society [ABSTRACT FROM AUTHOR]