Towards Location-Specific Precipitation Projections Using Deep Neural Networks
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908273222877184 |
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| author | Kumar, Bipin Yadav, Bhvisy Kumar Mukhopadhyay, Soumypdeep Rohan, Rakshit Singh, Bhupendra Bahadur Chattopadhyay, Rajib Chilukoti, Nagraju Sahai, Atul Kumar |
| author_facet | Kumar, Bipin Yadav, Bhvisy Kumar Mukhopadhyay, Soumypdeep Rohan, Rakshit Singh, Bhupendra Bahadur Chattopadhyay, Rajib Chilukoti, Nagraju Sahai, Atul Kumar |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_14095 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Towards Location-Specific Precipitation Projections Using Deep Neural Networks Kumar, Bipin Yadav, Bhvisy Kumar Mukhopadhyay, Soumypdeep Rohan, Rakshit Singh, Bhupendra Bahadur Chattopadhyay, Rajib Chilukoti, Nagraju Sahai, Atul Kumar Atmospheric and Oceanic Physics Machine Learning 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. |
| title | Towards Location-Specific Precipitation Projections Using Deep Neural Networks |
| topic | Atmospheric and Oceanic Physics Machine Learning |
| url | https://arxiv.org/abs/2503.14095 |