This title appears in the Scientific Report :
2018
Please use the identifier:
http://dx.doi.org/10.5194/essd-10-857-2018 in citations.
Please use the identifier: http://hdl.handle.net/2128/20205 in citations.
GRACILE: a comprehensive climatology of atmospheric gravity wave parameters based on satellite limb soundings
GRACILE: a comprehensive climatology of atmospheric gravity wave parameters based on satellite limb soundings
Gravity waves are one of the main drivers of atmospheric dynamics. The spatial resolution of most global atmospheric models, however, is too coarse to properly resolve the small scales of gravity waves, which range from tens to a few thousand kilometers horizontally, and from below 1km to tens of ki...
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Personal Name(s): | Ern, Manfred (Corresponding author) |
---|---|
Trinh, Quang Thai / Preusse, Peter / Gille, John C. / Mlynczak, Martin G. / Russell III, James M. / Riese, Martin | |
Contributing Institute: |
Stratosphäre; IEK-7 |
Published in: | Earth system science data, 10 (2018) 2, S. 857 - 892 |
Imprint: |
Katlenburg-Lindau
Copernics Publications
2018
|
DOI: |
10.5194/essd-10-857-2018 |
Document Type: |
Journal Article |
Research Program: |
Composition and dynamics of the upper troposphere and middle atmosphere |
Link: |
OpenAccess OpenAccess |
Publikationsportal JuSER |
Please use the identifier: http://hdl.handle.net/2128/20205 in citations.
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520 | |a Gravity waves are one of the main drivers of atmospheric dynamics. The spatial resolution of most global atmospheric models, however, is too coarse to properly resolve the small scales of gravity waves, which range from tens to a few thousand kilometers horizontally, and from below 1km to tens of kilometers vertically. Gravity wave source processes involve even smaller scales. Therefore, general circulation models (GCMs) and chemistry climate models (CCMs) usually parametrize the effect of gravity waves on the global circulation. These parametrizations are very simplified. For this reason, comparisons with global observations of gravity waves are needed for an improvement of parametrizations and an alleviation of model biases. | ||
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