Numerical simulations of mesopause airglow (MA) fluctuations induced by tsunami-generated acoustic and gravity waves (TAGWs) are performed. Simulated tsunamis over realistic bathymetry are used to excite atmospheric waves at the surface level of a three-dimensional nonlinear and compressible neutral atmospheric model. The model incorporates the dynamics and chemistry of hydroxyl OH(3,1) MA under nighttime assumptions. We report case study results of eight recent large tsunami events and demonstrate that TAGW-induced MA fluctuations are readily detectable with modern ground- and space-based imagers, and may provide quantitative insight. The amplitudes of MA fluctuations reflect the evolution of ocean surface displacements, enhancing or decreasing accordingly, and revealing the tsunami's lobes and local wave focusing. The results suggest that MA observations have potential to supplement early-warning systems, providing information on spatial and temporal evolution of tsunami waves of ~10 centimeters and higher for the cases shown. They may find applications in tsunami tracking over large open ocean areas, as well as in the investigation or reconstruction of tsunami source characteristics.

Key points:

  • Three-dimensional numerical simulations of mesopause airglow responses to realistic tsunami-generated acoustic-gravity waves are performed
  • Large tsunamis can generate readily-detectable and quantifiable fluctuations in mesopause airglow via acoustic-gravity waves
  • Mesopause airglow observations may supplement tsunami tracking, early-warning systems and source process investigations

This collection hosts the data and animations associated with the journal article, Numerical modeling of tsunami-generated acoustic-gravity waves in mesopause airglow. The full-text manuscript is published in JGR Space Physics.

Article DOI: 10.1029/2022JA030301

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Submissions from 2022

 

01: Simulation results from the 2001 M8.3 Southern Peru Earthquake and Tsunami Case Study Description, P. A. Inchin, C. J. Heale, J B. Snively, and M. D. Zettergren

 

02: Simulation results from the 2004 M9.1 Andaman Islands Earthquake and Tsunami Case Study, P. A. Inchin, C. J. Heale, J. P. Snively, and M. D. Zettergren

 

03: Simulation results from the 2006 M7.7 Pangandaran Earthquake and Tsunami Case Study, P. A. Inchin, C. J. Heale, J. B. Snively, and M. D. Zettergren

 

04: Simulation results from the 2006 M8.3 Kuril Earthquake and Tsunami Case Study, P. A. Inchin, C. J. Heale, J. B. Snively, and M. D. Zettergren

 

05: Simulation results from the 2010 M8.8 Bio-Bio Earthquake and Tsunami Case Study, P. A. Inchin, C. J. Heale, J. B. Snively, and M. D. Zettergren

 

06: Simulation results from the 2021 M8.1 Kermadec Islands Earthquake and Tsunami Case Study, P. A. Inchin, C. J. Heale, J. B. Snively, and M. D. Zettergren

 

07: Simulation results from the 2011 M9.1 Tohoku-Oki Earthquake and Tsunami Case Study with Source Model 1, P. A. Inchin, C. J. Heale, J. B. Snively, and M. D. Zettergren

 

08: Simulation results from the 2011 M9.1 Tohoku-Oki Earthquake and Tsunami Case Study with Source Model 2, P. A. Inchin, C. J. Heale, J. B. Snively, and M. D. Zettergren

 

09: Simulation results from the 2011 M9.1 Tohoku-Oki Earthquake and Tsunami Case Study with Source Model 3, P. A. Inchin, C. J. Heale, J. B. Snively, and M. D. Zettergren

 

10: Simulation results from the 2011 M9.1 Tohoku-Oki Earthquake and Tsunami Case Study with Source Model 5, P. A. Inchin, C. J. Heale, J. B. Snively, and M. D. Zettergren

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Table: Spatial Extents of the Numerical Domains, P. A. Inchin, C. J. Heale, J. B. Snively, and M. D. Zettergren