A physics-inspired neural network for short-wave radiation parameterization
Abstract Radiation parameterization schemes are crucial components of weather and climate models, but they are also known to be computationally intensive. In recent decades, researchers have attempted to emulate these schemes using neural networks, with more attention to convolutional neural network...
| Published in: | Journal of Inverse and Ill-posed Problems |
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| Main Authors: | , , , , , |
| Other Authors: | |
| Format: | Article in Journal/Newspaper |
| Language: | English |
| Published: |
Walter de Gruyter GmbH
2024
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| Subjects: | |
| Online Access: | http://dx.doi.org/10.1515/jiip-2023-0075 https://www.degruyter.com/document/doi/10.1515/jiip-2023-0075/xml https://www.degruyter.com/document/doi/10.1515/jiip-2023-0075/pdf |
| Summary: | Abstract Radiation parameterization schemes are crucial components of weather and climate models, but they are also known to be computationally intensive. In recent decades, researchers have attempted to emulate these schemes using neural networks, with more attention to convolutional neural networks. However, in this paper, we explore the potential of recurrent neural networks (RNNs) for predicting solar heating rates. Our architecture was trained and tested using long-term hindcast data from the Pechora Sea region, with the conventional RRTMG scheme serving as a shortwave parameterization. Our findings show that the RNN offers rapid learning, fast inference, and excellent data fitting. We also present preliminary results demonstrating the use of RNNs for operational weather forecasting, which achieved a significant speedup in parameterization and reduced the overall forecast time by 40 %. |
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