Work

A Convolutional Neural Network for the Detection of Gravity Waves in Satellite Observations and Numerical Simulations

Year: 2025

Type: article

Abstract: Comparisons between observed and model‐resolved gravity waves (GWs) are crucial for evaluating general circulation model (GCM) simulation accuracy and understanding wave characteristics. However, observational noise often obscures waves, complicating such comparisons. To address this, we have developed a GW detection method using a convolutional neural network (CNN). The CNN is trained on Atmospheric Infrared Sounder (AIRS) temperatures with labels indicating wave presence based on Berthelemy et al. (2025, https://doi.org/10.5194/egusphere‐2025‐455 ). Their method detects noise‐induced pixel‐to‐pixel variations in horizontal wavelengths; in contrast, the CNN robustly identify waves even when applied to smoothly varying model data. Using this method, we compare stratospheric GWs in boreal winters between AIRS observations and a high‐top GW‐permitting GCM, Japanese Atmospheric GCM for Upper Atmosphere Research (JAGUAR). The results agree well and exhibit similar interannual variability, with discrepancies also identified, including a more zonally elongated distribution of tropical GWs in JAGUAR. This method is broadly applicable to the future use of satellites for guiding wave‐resolving atmospheric model development.

Source: Geophysical Research Letters

DOI: 10.1029/2025gl115683

Authors: Haruka Okui, Corwin J. Wright, Peter G. Berthelemy, Neil P. Hindley, Lars Hoffmann, Andrew P. Barnes

Institutions: University of Bath, The University of Tokyo, Forschungszentrum Jülich, Jülich Supercomputing Centre

Language: English

Topic: Ionosphere and magnetosphere dynamics

Subfield: Astronomy and Astrophysics

Field: Physics and Astronomy

Domain: Physical Sciences

SDG: Climate action

Open Access status: gold

Estimated APC paid: $3,190