TY  - JOUR
AU  - Okui, Haruka
AU  - Wright, Corwin J.
AU  - Berthelemy, Peter G.
AU  - Hindley, Neil P.
AU  - Hoffmann, Lars
AU  - Barnes, Andrew P.
TI  - A Convolutional Neural Network for the Detection of Gravity Waves in Satellite Observations and Numerical Simulations
JO  - Geophysical research letters
VL  - 52
IS  - 11
SN  - 0094-8276
CY  - Hoboken, NJ
PB  - Wiley
M1  - FZJ-2025-02772
SP  - e2025GL115683
PY  - 2025
AB  - 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.
LB  - PUB:(DE-HGF)16
UR  - <Go to ISI:>//WOS:001504302600001
DO  - DOI:10.1029/2025GL115683
UR  - https://juser.fz-juelich.de/record/1043156
ER  -