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A feasibility of six–hourly rainfall forecast over central India using model output and remote sensing data

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Rainfall forecast has prime importance in an agrarian country like India, wherein the agricultural production is solely dependent on monsoon rainfall. In this paper, an artificial neural network (ANN) technique is used to construct a non–linear mapping between output data from global forecast system (GFS) and rainfall from tropical rainfall measuring mission (TRMM) satellite measurements. The objective of the present study is to generate region–specific six–hourly quantitative rainfall forecast over central India using ANN and resilient propagation learning algorithm. Meteorological variables from the GFS model and precipitation product from TRMM multisatellite precipitation analysis (TMPA) are used as input data for training the network, which generate rainfall forecast for the next time step. The test was performed for central India during the summer monsoon period of 2010. In order to evaluate the potential of rainfall forecast skill over the studied region, the forecast precipitation has been intercompared with TMPA–3B42, and Kalpana–1 derived precipitation products and a statistical analysis was performed. The linear correlation between ANN forecast and TMPA–3B42 rainfall was 0.58, whereas it was 0.52 with Kalpana–1 derived precipitation estimates. The results show that the predicted precipitation by the present technique performs better than GFS model precipitation forecast, and the system indicates a potential for more accurate rainfall forecasting.

Keywords: rainfall forecasting, artificial neural networks, ANNs, multi–layer perceptron, global forecast system, GFS, hydrology, Central India, remote sensing, tropical rainfall measurement, precipitation prediction

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