Abstract Comparisons between observed and modelâresolved 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/egusphereâ2025â455). Their method detects noiseâinduced pixelâtoâpixel 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 highâtop GWâpermitting 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 waveâresolving atmospheric model development.
Observations from the Little Bunker
Last updated: August 18, 2025 04:13 PM
Atmospheric COâ
425.4 ppm
+18.4
ppm/year
Atmospheric CHâ
1934.91 ppb
+9.11
ppb/year
Global Temperature
+1.48 °C
Peak: +1.73°C
World Population
8.1 billion
+67
million/year