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@ARTICLE{Banyard:892286,
      author       = {Banyard, T. P. and Wright, C. J. and Hindley, N. P. and
                      Halloran, G. and Krisch, I. and Kaifler, B. and Hoffmann,
                      L.},
      title        = {{A}tmospheric {G}ravity {W}aves in {A}eolus {W}ind {L}idar
                      {O}bservations},
      journal      = {Geophysical research letters},
      volume       = {48},
      number       = {10},
      issn         = {1944-8007},
      address      = {Hoboken, NJ},
      publisher    = {Wiley},
      reportid     = {FZJ-2021-01996},
      pages        = {e2021GL092756},
      year         = {2021},
      abstract     = {Aeolus is the first Doppler wind lidar in space. It
                      provides unique high‐resolution measurements of horizontal
                      wind in the sparsely‐observed
                      upper‐troposphere/lower‐stratosphere (UTLS), with global
                      coverage. In this study, Aeolus’ ability to resolve
                      atmospheric gravity waves (GWs) is demonstrated. The
                      accurate representation of these small‐scale waves is
                      vital to properly simulate dynamics in global weather and
                      climate models. In a case study over the Andes, Aeolus GW
                      measurements show coherent phase structure from the surface
                      to the lower stratosphere, with wind perturbations > 10
                      ms−1, a vertical wavelength ∼8 km and an along‐track
                      horizontal wavelength ∼900 km. Good agreement is found
                      between Aeolus and colocated satellite, ground‐based lidar
                      and reanalysis data sets for this example. Our results show
                      that data from satellites of this type can provide unique
                      information on GW sources and propagation in the UTLS,
                      filling a key knowledge gap that underlies known major
                      deficiencies in weather and climate modelling.},
      cin          = {JSC},
      ddc          = {550},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {511 - Enabling Computational- $\&$ Data-Intensive Science
                      and Engineering (POF4-511)},
      pid          = {G:(DE-HGF)POF4-511},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000658600300035},
      doi          = {10.1029/2021GL092756},
      url          = {https://juser.fz-juelich.de/record/892286},
}