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A New, Long-Lived, Jupiter Mesoscale Wave Observed at Visible Wavelengths.

Amy A SimonRicardo HuesoPeio IñurrigarroAgustín Sánchez-LavegaRaúl MoralesJuberíasRichard CosentinoLeigh N FletcherMichael H WongAndrew I HsuImke de PaterGlenn S OrtonFrançois ColasMarc DelcroixDamian PeachJosep-María Gómez-Forrellad
Published in: The Astronomical journal (2018)
Small-scale waves were observed along the boundary between Jupiter's North Equatorial Belt and North Tropical Zone, ~16.5° N planetographic latitude in Hubble Space Telescope data in 2012 and throughout 2015 to 2018, observable at all wavelengths from the UV to the near IR. At peak visibility, the waves have sufficient contrast (~10%) to be observed from ground-based telescopes. They have a typical wavelength of about 1.2° (1400 km), variable-length wave trains, and westward phase speeds of a few m/s or less. New analysis of Voyager 2 data shows similar wave trains over at least 300 hours. Some waves appear curved when over cyclones and anticyclones, but most are straight, but tilted, shifting in latitude as they pass vortices. Based on their wavelengths, phase speeds, and faint appearance at high-altitude sensitive passbands, the observed NEB waves are consistent with inertia-gravity waves at the 500-mbar pressure level, though formation altitude is not well constrained. Preliminary General Circulation Model simulations generate inertia-gravity waves from vortices interacting with the environment and can reproduce the observed wavelengths and orientations. Several mechanisms can generate these waves, and all may contribute: geostrophic adjustment of cyclones; cyclone/anticyclone interactions; wind interactions with obstructions or heat pulses from convection; or changing vertical wind shear. However, observations also show that the presence of vortices and/or regions of convection are not sufficient by themselves for wave formation, implying that a change in vertical structure may affect their stability, or that changes in haze properties may affect their visibility.
Keyphrases
  • magnetic resonance
  • high speed
  • big data
  • artificial intelligence
  • heat stress
  • deep learning