Teufel, B and Carmo, F and Sushama, L and Sun, L and Khaliq, MN and Bélair, S and Shamseldin, A and Kumar, DN and Vaze, J (2023) Physics-informed deep learning framework to model intense precipitation events at super resolution. In: Geoscience Letters, 10 (1).
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Abstract
Physical modeling of precipitation at fine (sub-kilometer) spatial scales is computationally very expensive. This study develops a highly efficient framework for this task by coupling deep learning (DL) and physical modeling. This framework is developed and tested using regional climate simulations performed over a domain covering Montreal and adjoining regions, for the summers of 2015–2020, at 2.5 km and 250 m resolutions. The DL framework uses a recurrent approach and considers atmospheric physical processes, such as advection, to generate high-resolution information from low-resolution data, which enables it to recreate fine details and produce temporally consistent fields. The DL framework generates realistic high-resolution precipitation estimates, including intense short-duration precipitation events, which allows it to be applied in engineering problems, such as evaluating the climate resiliency of urban storm drainage systems. The results portray the value of the proposed DL framework, which can be extended to other resolutions, periods, and regions.
| Item Type: | Journal Article |
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| Publication: | Geoscience Letters |
| Publisher: | Springer Science and Business Media Deutschland GmbH |
| Additional Information: | The copyright of this article belongs to Springer Science and Business Media Deutschland GmbH. |
| Keywords: | Convection permitting model; Deep learning; Engineering scale; Intense precipitation; Regional climate model |
| Department/Centre: | Division of Mechanical Sciences > Civil Engineering |
| Date Deposited: | 14 Jun 2023 07:57 |
| Last Modified: | 14 Jun 2023 07:57 |
| URI: | https://eprints.iisc.ac.in/id/eprint/81870 |
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