Improving regional weather forecasts with neural interpolation
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Abstract (arXiv)
In this paper we design a neural interpolation operator to improve the boundary data for regional weather models, which is a challenging problem as we are required to map multi-scale dynamics between grid resolutions. In particular, we expose a methodology for approaching the problem through the study of a simplified model, with a view to generalise the results in this work to the dynamical core of regional weather models. Our approach will exploit a combination of techniques from image super-resolution with convolutional neural networks (CNNs) and residual networks, in addition to building the flow of atmospheric dynamics into the neural network
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 12 of its 38 references that are in the map. First posted on arXiv 17 May 2025.
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12 of 38 references.
References · 12
- 2015Deep Residual Learning for Image Recognitioncited by 52,096
- 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
- 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,859
- 2014Image Super-Resolution Using Deep Convolutional Networkscited by 1,224
- 2019Deep Learning for Image Super-resolution: A Surveycited by 369
- 2022GraphCast: Learning skillful medium-range global weather forecastingcited by 267
- 2020U-Net and its variants for medical image segmentation: theory and applicationscited by 169
- 2021Symplectic Learning for Hamiltonian Neural Networkscited by 35
- 2020Structure preserving deep learningcited by 31
- 2023Exactly conservative physics-informed neural networks and deep operator networks for dynamical systemscited by 13
- 2022Dynamical systems' based neural networkscited by 10
- 2023Predictions Based on Pixel Data: Insights from PDEs and Finite Differencescited by 2
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