Towards Location-Specific Precipitation Projections Using Deep Neural Networks

• URL: http://arxiv.org/abs/2503.14095v1
• Date: Tue, 18 Mar 2025 10:12:17 GMT
• Title: Towards Location-Specific Precipitation Projections Using Deep Neural Networks
• Authors: Bipin Kumar, Bhvisy Kumar Yadav, Soumypdeep Mukhopadhyay, Rakshit Rohan, Bhupendra Bahadur Singh, Rajib Chattopadhyay, Nagraju Chilukoti, Atul Kumar Sahai,
• Abstract summary: This study presents a paradigm shift by leveraging Deep Neural Networks (DNNs) to surpass traditional methods like Kriging for station-specific precipitation approximation.<n>We propose two innovative NN architectures: one utilizing precipitation, elevation, and location, and another incorporating additional meteorological parameters like humidity, temperature, and wind speed.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate precipitation estimates at individual locations are crucial for weather forecasting and spatial analysis. This study presents a paradigm shift by leveraging Deep Neural Networks (DNNs) to surpass traditional methods like Kriging for station-specific precipitation approximation. We propose two innovative NN architectures: one utilizing precipitation, elevation, and location, and another incorporating additional meteorological parameters like humidity, temperature, and wind speed. Trained on a vast dataset (1980-2019), these models outperform Kriging across various evaluation metrics (correlation coefficient, root mean square error, bias, and skill score) on a five-year validation set. This compelling evidence demonstrates the transformative power of deep learning for spatial prediction, offering a robust and precise alternative for station-specific precipitation estimation.

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