2025/05/17 by James Jackaman, Jackaman, James, Oliver Sutton +1
Computer Science · Earth and Planetary Sciences · Physics and Astronomy · #Advanced Image Processing Techniques #FOS: Computer and information sciences #Machine Learning (cs.LG) #Meteorological Phenomena and Simulations #Model Reduction and Neural Networks
paper · pdf · doi:10.48550/arxiv.2505.12040
openalex publication_date 2025/05/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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