An Artificial Neural Network based approach for estimation of rain intensity from spectral moments of a Doppler Weather Radar |
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| Authors: | Devajyoti Dutta Sanjay Sharma G.K. Sen B.A.M. Kannan S. Venketswarlu R.M. Gairola J. Das G. Viswanathan |
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| Affiliation: | 1. Department of Physics, Kohima Science College, Jotsoma, Nagaland 797002, India;2. School of Oceanographic Studies, Jadavpur University, Kolkata 700032, India;3. Mausam Bhavan, India Meteorological Department, New Delhi 110003, India;4. Cyclone Warning Centre, India Meteorological Department, Visakhapattanam 530023, India;5. Meteorology and Oceanography Division, Space Application Center, Ahmadabad 380015, India;6. Ex-Indian Statistical Institute, Kolkata 700108, India;g Ex-ISRO, Radar Development Cell, Bangalore 560058, India |
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| Abstract: | By using a Doppler Weather Radar (DWR) at Shriharikota (13.66°N & 80.23°E), an Artificial Neural Network (ANN) based technique is proposed to improve the accuracy of rain intensity estimation. Three spectral moments of a Doppler spectra are utilized as an input data to an ANN. Rain intensity, as measured by the tipping bucket rain gauges around the DWR station, are considered as a target values for the given inputs. Rain intensity as estimated by the developed ANN model is validated by the rain gauges measurements. With the help of a developed technique, reasonable improvement in the estimation of rain intensity is observed. By using the developed technique, root mean square error and bias are reduced in the range of 34–18% and 17–3% respectively, compared to Z–R approach. |
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| Keywords: | Spectral moments Rain intensity Artificial Neural Network Doppler Weather Radar Rain gauges |
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