Physics-informed neural networks for high-resolution weather reconstruction from sparse weather stations

• Published in: Open research Europe Vol. 4; p. 99
• Main Authors: Moreno Soto, Álvaro, Cervantes, Alejandro, Soler, Manuel
• Format: Journal Article
• Language: English
• Published: Belgium F1000 Research Limited 2024; F1000 Research Ltd
• Subjects: Artificial intelligence; eng; fluid flows; fluid mechanics; machine learning; physics-informed neural networks; weather reconstruction; fluid flows; weather reconstruction; fluid mechanics; machine learning; Artificial intelligence; physics-informed neural networks
• ISSN: 2732-5121; 2732-5121
• DOI: 10.12688/openreseurope.17388.1

Background The accurate provision of weather information holds immense significance to many disciplines. One example corresponds to the field of air traffic management, in which one basis for weather detection is set upon recordings from sparse weather stations on ground. The scarcity of data and their lack of precision poses significant challenges to achieve a detailed description of the atmosphere state at a certain moment in time. Methods In this article, we foster the use of physics-informed neural networks (PINNs), a type of machine learning (ML) architecture which embeds mathematically accurate physics models, to generate high-quality weather information subject to the regularization provided by the Navier-Stokes equations. Results The application of PINNs is oriented to the reconstruction of dense and precise wind and pressure fields in areas where only a few local measurements provided by weather stations are available. Our model does not only disclose and regularize such data, which are potentially corrupted by noise, but is also able to precisely compute wind and pressure in target areas. Conclusions The effect of time and spatial resolution over the capability of the PINN to accurately reconstruct fluid phenomena is thoroughly discussed through a parametric study, concluding that a proper tuning of the neural network’s loss function during training is of utmost importance.