Hauptseite > Publikationsdatenbank > Atmospheric Gravity Waves in Aeolus Wind Lidar Observations > print |
001 | 892286 | ||
005 | 20210628150953.0 | ||
024 | 7 | _ | |a 10.1029/2021GL092756 |2 doi |
024 | 7 | _ | |a 0094-8276 |2 ISSN |
024 | 7 | _ | |a 1944-8007 |2 ISSN |
024 | 7 | _ | |a 2128/27859 |2 Handle |
024 | 7 | _ | |a altmetric:104992087 |2 altmetric |
024 | 7 | _ | |a WOS:000658600300035 |2 WOS |
037 | _ | _ | |a FZJ-2021-01996 |
041 | _ | _ | |a English |
082 | _ | _ | |a 550 |
100 | 1 | _ | |a Banyard, T. P. |0 0000-0001-6376-7397 |b 0 |e Corresponding author |
245 | _ | _ | |a Atmospheric Gravity Waves in Aeolus Wind Lidar Observations |
260 | _ | _ | |a Hoboken, NJ |c 2021 |b Wiley |
336 | 7 | _ | |a article |2 DRIVER |
336 | 7 | _ | |a Output Types/Journal article |2 DataCite |
336 | 7 | _ | |a Journal Article |b journal |m journal |0 PUB:(DE-HGF)16 |s 1622047743_1381 |2 PUB:(DE-HGF) |
336 | 7 | _ | |a ARTICLE |2 BibTeX |
336 | 7 | _ | |a JOURNAL_ARTICLE |2 ORCID |
336 | 7 | _ | |a Journal Article |0 0 |2 EndNote |
520 | _ | _ | |a 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. |
536 | _ | _ | |a 511 - Enabling Computational- & Data-Intensive Science and Engineering (POF4-511) |0 G:(DE-HGF)POF4-511 |c POF4-511 |x 0 |f POF IV |
588 | _ | _ | |a Dataset connected to CrossRef, Journals: juser.fz-juelich.de |
700 | 1 | _ | |a Wright, C. J. |0 0000-0003-2496-953X |b 1 |
700 | 1 | _ | |a Hindley, N. P. |0 0000-0003-4377-2038 |b 2 |
700 | 1 | _ | |a Halloran, G. |0 0000-0001-8127-1918 |b 3 |
700 | 1 | _ | |a Krisch, I. |0 P:(DE-HGF)0 |b 4 |
700 | 1 | _ | |a Kaifler, B. |0 0000-0002-5891-242X |b 5 |
700 | 1 | _ | |a Hoffmann, L. |0 P:(DE-Juel1)129125 |b 6 |
773 | _ | _ | |a 10.1029/2021GL092756 |0 PERI:(DE-600)2021599-X |n 10 |p e2021GL092756 |t Geophysical research letters |v 48 |y 2021 |x 1944-8007 |
856 | 4 | _ | |u https://juser.fz-juelich.de/record/892286/files/2021GL092756.pdf |y OpenAccess |
909 | C | O | |o oai:juser.fz-juelich.de:892286 |p openaire |p open_access |p VDB |p driver |p dnbdelivery |
910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 6 |6 P:(DE-Juel1)129125 |
913 | 0 | _ | |a DE-HGF |b Key Technologies |l Supercomputing & Big Data |1 G:(DE-HGF)POF3-510 |0 G:(DE-HGF)POF3-511 |3 G:(DE-HGF)POF3 |2 G:(DE-HGF)POF3-500 |4 G:(DE-HGF)POF |v Computational Science and Mathematical Methods |x 0 |
913 | 1 | _ | |a DE-HGF |b Key Technologies |l Engineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action |1 G:(DE-HGF)POF4-510 |0 G:(DE-HGF)POF4-511 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-500 |4 G:(DE-HGF)POF |v Enabling Computational- & Data-Intensive Science and Engineering |x 0 |
914 | 1 | _ | |y 2021 |
915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0200 |2 StatID |b SCOPUS |d 2021-01-29 |
915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0160 |2 StatID |b Essential Science Indicators |d 2021-01-29 |
915 | _ | _ | |a Creative Commons Attribution CC BY 4.0 |0 LIC:(DE-HGF)CCBY4 |2 HGFVOC |
915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0600 |2 StatID |b Ebsco Academic Search |d 2021-01-29 |
915 | _ | _ | |a JCR |0 StatID:(DE-HGF)0100 |2 StatID |b GEOPHYS RES LETT : 2019 |d 2021-01-29 |
915 | _ | _ | |a DEAL Wiley |0 StatID:(DE-HGF)3001 |2 StatID |d 2021-01-29 |w ger |
915 | _ | _ | |a WoS |0 StatID:(DE-HGF)0113 |2 StatID |b Science Citation Index Expanded |d 2021-01-29 |
915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0150 |2 StatID |b Web of Science Core Collection |d 2021-01-29 |
915 | _ | _ | |a IF < 5 |0 StatID:(DE-HGF)9900 |2 StatID |d 2021-01-29 |
915 | _ | _ | |a OpenAccess |0 StatID:(DE-HGF)0510 |2 StatID |
915 | _ | _ | |a Peer Review |0 StatID:(DE-HGF)0030 |2 StatID |b ASC |d 2021-01-29 |
915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)1150 |2 StatID |b Current Contents - Physical, Chemical and Earth Sciences |d 2021-01-29 |
915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0300 |2 StatID |b Medline |d 2021-01-29 |
915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0199 |2 StatID |b Clarivate Analytics Master Journal List |d 2021-01-29 |
920 | _ | _ | |l yes |
920 | 1 | _ | |0 I:(DE-Juel1)JSC-20090406 |k JSC |l Jülich Supercomputing Center |x 0 |
980 | _ | _ | |a journal |
980 | _ | _ | |a VDB |
980 | _ | _ | |a UNRESTRICTED |
980 | _ | _ | |a I:(DE-Juel1)JSC-20090406 |
980 | 1 | _ | |a FullTexts |
Library | Collection | CLSMajor | CLSMinor | Language | Author |
---|