Authors:Kausik Bhattacharyya,Manabendra Maiti ,Salil Kumar Biswas,Md Anoarul Islam,Ayan Kanti Pradhan,Pradip Kumar Ghosh,Judhajit Sanyal,
Keywords:Radiometer,brightness temperature,microwave,propagation,rain,weather forecasting,
AbstractPrediction of rainfall is important in terms of the impact of a rain event on various systems such as communication systems. Traditional approaches used to predict rain events are often sensitive to fluctuations in the datasets on which the predictions are made. The present paper therefore develops a robust machine learning based technique for accurate short term rain forecasting, based on experimentally collected data. Ground based microwave radiometer allows continuous monitoring of ambient temperature, water vapour and liquid water, and other hydrometeors through measurement of radiometric brightness temperature at different frequencies in clear and cloudy weather conditions. The radiometric brightness temperature outputs at 23.834 and 30 GHz are used to establish a relation where data trends which are precursors to rain events can be identified using this parameter. Spline equations are modeled by partitioning the dataset. The predictability of the occurrence of precipitation and the rainfall intensity has been studied based on the rise of brightness temperature from clear to cloudy weather conditions. The rise of brightness temperature at 23.834 and 30 GHz show that the precursory variations of this parameter preceding rain events are observable from 29 to 47 minutes prior to precipitation depending upon the nature of rainfall patterns. The data collected empirically displays trends that are used in this paper to provide a clear forecast of short term precipitation. Spline regression based machine learning models incorporating monthly trends, proposed in this paper improve the accuracy of prediction of short term rain events.
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