Comparison of Eddy Dissipation Rate Estimated From Operational Radiosonde and Commercial Aircraft Observations in the United States

The one‐third power of the energy dissipation rate (EDR), a primary aviation turbulence metric, is calculated using high vertical‐resolution radiosonde data (HVRRD) and compared with flight‐EDR observed from commercial airlines. Comparisons are made along the main flight routes over the United State...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Journal of geophysical research. Atmospheres 2023-10, Vol.128 (20)
Hauptverfasser: Ko, Han‐Chang, Chun, Hye‐Yeong, Sharman, Robert D., Kim, Jung‐Hoon
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page
container_issue 20
container_start_page
container_title Journal of geophysical research. Atmospheres
container_volume 128
creator Ko, Han‐Chang
Chun, Hye‐Yeong
Sharman, Robert D.
Kim, Jung‐Hoon
description The one‐third power of the energy dissipation rate (EDR), a primary aviation turbulence metric, is calculated using high vertical‐resolution radiosonde data (HVRRD) and compared with flight‐EDR observed from commercial airlines. Comparisons are made along the main flight routes over the United States and at z  = 20–45 kft for 6 years (2012–2017). The horizontal distributions of moderate‐or‐greater (MOG) ratio of HVRRD‐EDR show large values over the Rocky Mountains, consistent with those of flight‐EDR. Vertically, the MOG ratios of HVRRD‐EDR show local peaks at z  = 20–23 kft and 41–44 kft, while those of flight‐EDR at z  = 23–26 kft and 35–41 kft. Temporally, HVRRD‐EDR has maximum MOG values in JJA and minimum values in DJF at z  = 20–30 kft, which is opposite to the flight‐EDR. At z  = 30–40 kft, HVRRD‐EDR shows nearly no seasonal variation but flight‐EDR has large values in MAM and small values in JJA. At z  = 40–45 kft, HVRRD‐EDR (flight‐EDR) shows large values in MAM and small values in SON (DJF). Discrepancies in spatiotemporal distributions between the two data sets likely stem from: (a) turbulence observed from the two data sets cannot be the same event, (b) the limitation of HVRRD‐EDR in capturing shear‐instability under statically stable condition (i.e., Kelvin‐Helmholtz instability) which probably accounts for most flight‐EDR events at upper levels, and (c) limitation in aircraft measurements response to fluctuations at smaller scales than the aircraft size. We calculated a primary aviation turbulence metric, eddy dissipation rate (EDR), using operational high‐resolution radiosonde data and compared it with flight‐EDR observed from commercial airlines. EDR is an index for representing the intensity of turbulence. Analyzing 6 years of data (2012–2017) over the United States, we find that the horizontal distributions of both EDRs from radiosonde data and flight data show large values over the Rocky Mountains. However, they show large differences in vertical and temporal distributions in terms of their peak location and timing. We attribute these discrepancies to three factors. First, turbulence observed from the two data sets cannot be the same event, because the radiosonde and aircraft cannot coincide at the same location and time. Second, the sources of turbulence derived from radiosonde and flight observation may be different: static‐instability and dynamic‐instability for radiosonde‐EDR and flight‐EDR, respectively. Third, aircraft have limitat
doi_str_mv 10.1029/2023JD039352
format Article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2881747174</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2881747174</sourcerecordid><originalsourceid>FETCH-LOGICAL-c258t-958c47ef28cd9be7d767986df6f063974afec668477380e4be33ba6c48b811833</originalsourceid><addsrcrecordid>eNpNUFlLAzEQDqJgqX3zBwR8tZpNdnM8lh4eFApqwbclmwNTups1SYU--8dNWxEHhhnmO2A-AK4LdFcgLO4xwuR5hoggFT4DA1xQMeZC0PO_nb1fglGMG5SLI1JW5QB8T33by-Ci76C3cK71Hs5cjK6XyeXbi0wGzmNybV40XATfwlVvwhGV24xr57NYGyg7DbNba4JyGZm4oIK0Ca6aaMLXURCh62D6MHDduYPda8qu8QpcWLmNZvQ7h2C9mL9NH8fL1cPTdLIcK1zxNBYVVyUzFnOlRWOYZpQJTrWlFlEiWCmtUZTykjHCkSkbQ0gjqSp5w4uCEzIENyffPvjPnYmp3vhdyF_EGnNesJLlzqzbE0sFH2Mwtu5D_j7s6wLVh6Tr_0mTHwpYcX8</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2881747174</pqid></control><display><type>article</type><title>Comparison of Eddy Dissipation Rate Estimated From Operational Radiosonde and Commercial Aircraft Observations in the United States</title><source>Wiley Online Library Journals Frontfile Complete</source><source>Alma/SFX Local Collection</source><creator>Ko, Han‐Chang ; Chun, Hye‐Yeong ; Sharman, Robert D. ; Kim, Jung‐Hoon</creator><creatorcontrib>Ko, Han‐Chang ; Chun, Hye‐Yeong ; Sharman, Robert D. ; Kim, Jung‐Hoon</creatorcontrib><description>The one‐third power of the energy dissipation rate (EDR), a primary aviation turbulence metric, is calculated using high vertical‐resolution radiosonde data (HVRRD) and compared with flight‐EDR observed from commercial airlines. Comparisons are made along the main flight routes over the United States and at z  = 20–45 kft for 6 years (2012–2017). The horizontal distributions of moderate‐or‐greater (MOG) ratio of HVRRD‐EDR show large values over the Rocky Mountains, consistent with those of flight‐EDR. Vertically, the MOG ratios of HVRRD‐EDR show local peaks at z  = 20–23 kft and 41–44 kft, while those of flight‐EDR at z  = 23–26 kft and 35–41 kft. Temporally, HVRRD‐EDR has maximum MOG values in JJA and minimum values in DJF at z  = 20–30 kft, which is opposite to the flight‐EDR. At z  = 30–40 kft, HVRRD‐EDR shows nearly no seasonal variation but flight‐EDR has large values in MAM and small values in JJA. At z  = 40–45 kft, HVRRD‐EDR (flight‐EDR) shows large values in MAM and small values in SON (DJF). Discrepancies in spatiotemporal distributions between the two data sets likely stem from: (a) turbulence observed from the two data sets cannot be the same event, (b) the limitation of HVRRD‐EDR in capturing shear‐instability under statically stable condition (i.e., Kelvin‐Helmholtz instability) which probably accounts for most flight‐EDR events at upper levels, and (c) limitation in aircraft measurements response to fluctuations at smaller scales than the aircraft size. We calculated a primary aviation turbulence metric, eddy dissipation rate (EDR), using operational high‐resolution radiosonde data and compared it with flight‐EDR observed from commercial airlines. EDR is an index for representing the intensity of turbulence. Analyzing 6 years of data (2012–2017) over the United States, we find that the horizontal distributions of both EDRs from radiosonde data and flight data show large values over the Rocky Mountains. However, they show large differences in vertical and temporal distributions in terms of their peak location and timing. We attribute these discrepancies to three factors. First, turbulence observed from the two data sets cannot be the same event, because the radiosonde and aircraft cannot coincide at the same location and time. Second, the sources of turbulence derived from radiosonde and flight observation may be different: static‐instability and dynamic‐instability for radiosonde‐EDR and flight‐EDR, respectively. Third, aircraft have limitations detecting fluctuation at scales smaller than the aircraft size. Given the limited global data on atmospheric turbulence, EDR estimated from operational radiosonde data can be a valuable resource for research and development for the aviation industry and numerical weather forecasting models. Radiosonde‐derived turbulence is applied to aviation turbulence metric Spatiotemporal distributions of radiosonde‐ and flight‐turbulence are compared Causes of discrepancies between radiosonde‐ and flight‐turbulence are discussed</description><identifier>ISSN: 2169-897X</identifier><identifier>EISSN: 2169-8996</identifier><identifier>DOI: 10.1029/2023JD039352</identifier><language>eng</language><publisher>Washington: Blackwell Publishing Ltd</publisher><subject>Aerospace industry ; Aircraft ; Aircraft detection ; Aircraft observations ; Airlines ; Atmospheric data ; Atmospheric models ; Atmospheric turbulence ; Aviation ; Commercial aircraft ; Datasets ; Dynamic stability ; Energy dissipation ; Energy exchange ; Flight ; Forecasting models ; Geophysics ; Horizontal distribution ; Instability ; Kelvin-helmholtz instability ; Mathematical analysis ; Mountains ; Numerical forecasting ; Numerical forecasting models ; Numerical weather forecasting ; Oligodendrocyte-myelin glycoprotein ; R&amp;D ; Radiosonde data ; Radiosondes ; Research &amp; development ; Seasonal variation ; Seasonal variations ; Vortices ; Weather forecasting</subject><ispartof>Journal of geophysical research. Atmospheres, 2023-10, Vol.128 (20)</ispartof><rights>2023. This article is published under http://creativecommons.org/licenses/by-nc-nd/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c258t-958c47ef28cd9be7d767986df6f063974afec668477380e4be33ba6c48b811833</cites><orcidid>0000-0002-5336-9536 ; 0000-0002-2384-7517 ; 0000-0002-2014-4728 ; 0000-0002-9243-9998</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,27901,27902</link.rule.ids></links><search><creatorcontrib>Ko, Han‐Chang</creatorcontrib><creatorcontrib>Chun, Hye‐Yeong</creatorcontrib><creatorcontrib>Sharman, Robert D.</creatorcontrib><creatorcontrib>Kim, Jung‐Hoon</creatorcontrib><title>Comparison of Eddy Dissipation Rate Estimated From Operational Radiosonde and Commercial Aircraft Observations in the United States</title><title>Journal of geophysical research. Atmospheres</title><description>The one‐third power of the energy dissipation rate (EDR), a primary aviation turbulence metric, is calculated using high vertical‐resolution radiosonde data (HVRRD) and compared with flight‐EDR observed from commercial airlines. Comparisons are made along the main flight routes over the United States and at z  = 20–45 kft for 6 years (2012–2017). The horizontal distributions of moderate‐or‐greater (MOG) ratio of HVRRD‐EDR show large values over the Rocky Mountains, consistent with those of flight‐EDR. Vertically, the MOG ratios of HVRRD‐EDR show local peaks at z  = 20–23 kft and 41–44 kft, while those of flight‐EDR at z  = 23–26 kft and 35–41 kft. Temporally, HVRRD‐EDR has maximum MOG values in JJA and minimum values in DJF at z  = 20–30 kft, which is opposite to the flight‐EDR. At z  = 30–40 kft, HVRRD‐EDR shows nearly no seasonal variation but flight‐EDR has large values in MAM and small values in JJA. At z  = 40–45 kft, HVRRD‐EDR (flight‐EDR) shows large values in MAM and small values in SON (DJF). Discrepancies in spatiotemporal distributions between the two data sets likely stem from: (a) turbulence observed from the two data sets cannot be the same event, (b) the limitation of HVRRD‐EDR in capturing shear‐instability under statically stable condition (i.e., Kelvin‐Helmholtz instability) which probably accounts for most flight‐EDR events at upper levels, and (c) limitation in aircraft measurements response to fluctuations at smaller scales than the aircraft size. We calculated a primary aviation turbulence metric, eddy dissipation rate (EDR), using operational high‐resolution radiosonde data and compared it with flight‐EDR observed from commercial airlines. EDR is an index for representing the intensity of turbulence. Analyzing 6 years of data (2012–2017) over the United States, we find that the horizontal distributions of both EDRs from radiosonde data and flight data show large values over the Rocky Mountains. However, they show large differences in vertical and temporal distributions in terms of their peak location and timing. We attribute these discrepancies to three factors. First, turbulence observed from the two data sets cannot be the same event, because the radiosonde and aircraft cannot coincide at the same location and time. Second, the sources of turbulence derived from radiosonde and flight observation may be different: static‐instability and dynamic‐instability for radiosonde‐EDR and flight‐EDR, respectively. Third, aircraft have limitations detecting fluctuation at scales smaller than the aircraft size. Given the limited global data on atmospheric turbulence, EDR estimated from operational radiosonde data can be a valuable resource for research and development for the aviation industry and numerical weather forecasting models. Radiosonde‐derived turbulence is applied to aviation turbulence metric Spatiotemporal distributions of radiosonde‐ and flight‐turbulence are compared Causes of discrepancies between radiosonde‐ and flight‐turbulence are discussed</description><subject>Aerospace industry</subject><subject>Aircraft</subject><subject>Aircraft detection</subject><subject>Aircraft observations</subject><subject>Airlines</subject><subject>Atmospheric data</subject><subject>Atmospheric models</subject><subject>Atmospheric turbulence</subject><subject>Aviation</subject><subject>Commercial aircraft</subject><subject>Datasets</subject><subject>Dynamic stability</subject><subject>Energy dissipation</subject><subject>Energy exchange</subject><subject>Flight</subject><subject>Forecasting models</subject><subject>Geophysics</subject><subject>Horizontal distribution</subject><subject>Instability</subject><subject>Kelvin-helmholtz instability</subject><subject>Mathematical analysis</subject><subject>Mountains</subject><subject>Numerical forecasting</subject><subject>Numerical forecasting models</subject><subject>Numerical weather forecasting</subject><subject>Oligodendrocyte-myelin glycoprotein</subject><subject>R&amp;D</subject><subject>Radiosonde data</subject><subject>Radiosondes</subject><subject>Research &amp; development</subject><subject>Seasonal variation</subject><subject>Seasonal variations</subject><subject>Vortices</subject><subject>Weather forecasting</subject><issn>2169-897X</issn><issn>2169-8996</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><recordid>eNpNUFlLAzEQDqJgqX3zBwR8tZpNdnM8lh4eFApqwbclmwNTups1SYU--8dNWxEHhhnmO2A-AK4LdFcgLO4xwuR5hoggFT4DA1xQMeZC0PO_nb1fglGMG5SLI1JW5QB8T33by-Ci76C3cK71Hs5cjK6XyeXbi0wGzmNybV40XATfwlVvwhGV24xr57NYGyg7DbNba4JyGZm4oIK0Ca6aaMLXURCh62D6MHDduYPda8qu8QpcWLmNZvQ7h2C9mL9NH8fL1cPTdLIcK1zxNBYVVyUzFnOlRWOYZpQJTrWlFlEiWCmtUZTykjHCkSkbQ0gjqSp5w4uCEzIENyffPvjPnYmp3vhdyF_EGnNesJLlzqzbE0sFH2Mwtu5D_j7s6wLVh6Tr_0mTHwpYcX8</recordid><startdate>20231027</startdate><enddate>20231027</enddate><creator>Ko, Han‐Chang</creator><creator>Chun, Hye‐Yeong</creator><creator>Sharman, Robert D.</creator><creator>Kim, Jung‐Hoon</creator><general>Blackwell Publishing Ltd</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7TG</scope><scope>7UA</scope><scope>8FD</scope><scope>C1K</scope><scope>F1W</scope><scope>FR3</scope><scope>H8D</scope><scope>H96</scope><scope>KL.</scope><scope>KR7</scope><scope>L.G</scope><scope>L7M</scope><orcidid>https://orcid.org/0000-0002-5336-9536</orcidid><orcidid>https://orcid.org/0000-0002-2384-7517</orcidid><orcidid>https://orcid.org/0000-0002-2014-4728</orcidid><orcidid>https://orcid.org/0000-0002-9243-9998</orcidid></search><sort><creationdate>20231027</creationdate><title>Comparison of Eddy Dissipation Rate Estimated From Operational Radiosonde and Commercial Aircraft Observations in the United States</title><author>Ko, Han‐Chang ; Chun, Hye‐Yeong ; Sharman, Robert D. ; Kim, Jung‐Hoon</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c258t-958c47ef28cd9be7d767986df6f063974afec668477380e4be33ba6c48b811833</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Aerospace industry</topic><topic>Aircraft</topic><topic>Aircraft detection</topic><topic>Aircraft observations</topic><topic>Airlines</topic><topic>Atmospheric data</topic><topic>Atmospheric models</topic><topic>Atmospheric turbulence</topic><topic>Aviation</topic><topic>Commercial aircraft</topic><topic>Datasets</topic><topic>Dynamic stability</topic><topic>Energy dissipation</topic><topic>Energy exchange</topic><topic>Flight</topic><topic>Forecasting models</topic><topic>Geophysics</topic><topic>Horizontal distribution</topic><topic>Instability</topic><topic>Kelvin-helmholtz instability</topic><topic>Mathematical analysis</topic><topic>Mountains</topic><topic>Numerical forecasting</topic><topic>Numerical forecasting models</topic><topic>Numerical weather forecasting</topic><topic>Oligodendrocyte-myelin glycoprotein</topic><topic>R&amp;D</topic><topic>Radiosonde data</topic><topic>Radiosondes</topic><topic>Research &amp; development</topic><topic>Seasonal variation</topic><topic>Seasonal variations</topic><topic>Vortices</topic><topic>Weather forecasting</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ko, Han‐Chang</creatorcontrib><creatorcontrib>Chun, Hye‐Yeong</creatorcontrib><creatorcontrib>Sharman, Robert D.</creatorcontrib><creatorcontrib>Kim, Jung‐Hoon</creatorcontrib><collection>CrossRef</collection><collection>Meteorological &amp; Geoastrophysical Abstracts</collection><collection>Water Resources Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Aquatic Science &amp; Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy &amp; Non-Living Resources</collection><collection>Meteorological &amp; Geoastrophysical Abstracts - Academic</collection><collection>Civil Engineering Abstracts</collection><collection>Aquatic Science &amp; Fisheries Abstracts (ASFA) Professional</collection><collection>Advanced Technologies Database with Aerospace</collection><jtitle>Journal of geophysical research. Atmospheres</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Ko, Han‐Chang</au><au>Chun, Hye‐Yeong</au><au>Sharman, Robert D.</au><au>Kim, Jung‐Hoon</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Comparison of Eddy Dissipation Rate Estimated From Operational Radiosonde and Commercial Aircraft Observations in the United States</atitle><jtitle>Journal of geophysical research. Atmospheres</jtitle><date>2023-10-27</date><risdate>2023</risdate><volume>128</volume><issue>20</issue><issn>2169-897X</issn><eissn>2169-8996</eissn><abstract>The one‐third power of the energy dissipation rate (EDR), a primary aviation turbulence metric, is calculated using high vertical‐resolution radiosonde data (HVRRD) and compared with flight‐EDR observed from commercial airlines. Comparisons are made along the main flight routes over the United States and at z  = 20–45 kft for 6 years (2012–2017). The horizontal distributions of moderate‐or‐greater (MOG) ratio of HVRRD‐EDR show large values over the Rocky Mountains, consistent with those of flight‐EDR. Vertically, the MOG ratios of HVRRD‐EDR show local peaks at z  = 20–23 kft and 41–44 kft, while those of flight‐EDR at z  = 23–26 kft and 35–41 kft. Temporally, HVRRD‐EDR has maximum MOG values in JJA and minimum values in DJF at z  = 20–30 kft, which is opposite to the flight‐EDR. At z  = 30–40 kft, HVRRD‐EDR shows nearly no seasonal variation but flight‐EDR has large values in MAM and small values in JJA. At z  = 40–45 kft, HVRRD‐EDR (flight‐EDR) shows large values in MAM and small values in SON (DJF). Discrepancies in spatiotemporal distributions between the two data sets likely stem from: (a) turbulence observed from the two data sets cannot be the same event, (b) the limitation of HVRRD‐EDR in capturing shear‐instability under statically stable condition (i.e., Kelvin‐Helmholtz instability) which probably accounts for most flight‐EDR events at upper levels, and (c) limitation in aircraft measurements response to fluctuations at smaller scales than the aircraft size. We calculated a primary aviation turbulence metric, eddy dissipation rate (EDR), using operational high‐resolution radiosonde data and compared it with flight‐EDR observed from commercial airlines. EDR is an index for representing the intensity of turbulence. Analyzing 6 years of data (2012–2017) over the United States, we find that the horizontal distributions of both EDRs from radiosonde data and flight data show large values over the Rocky Mountains. However, they show large differences in vertical and temporal distributions in terms of their peak location and timing. We attribute these discrepancies to three factors. First, turbulence observed from the two data sets cannot be the same event, because the radiosonde and aircraft cannot coincide at the same location and time. Second, the sources of turbulence derived from radiosonde and flight observation may be different: static‐instability and dynamic‐instability for radiosonde‐EDR and flight‐EDR, respectively. Third, aircraft have limitations detecting fluctuation at scales smaller than the aircraft size. Given the limited global data on atmospheric turbulence, EDR estimated from operational radiosonde data can be a valuable resource for research and development for the aviation industry and numerical weather forecasting models. Radiosonde‐derived turbulence is applied to aviation turbulence metric Spatiotemporal distributions of radiosonde‐ and flight‐turbulence are compared Causes of discrepancies between radiosonde‐ and flight‐turbulence are discussed</abstract><cop>Washington</cop><pub>Blackwell Publishing Ltd</pub><doi>10.1029/2023JD039352</doi><orcidid>https://orcid.org/0000-0002-5336-9536</orcidid><orcidid>https://orcid.org/0000-0002-2384-7517</orcidid><orcidid>https://orcid.org/0000-0002-2014-4728</orcidid><orcidid>https://orcid.org/0000-0002-9243-9998</orcidid><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 2169-897X
ispartof Journal of geophysical research. Atmospheres, 2023-10, Vol.128 (20)
issn 2169-897X
2169-8996
language eng
recordid cdi_proquest_journals_2881747174
source Wiley Online Library Journals Frontfile Complete; Alma/SFX Local Collection
subjects Aerospace industry
Aircraft
Aircraft detection
Aircraft observations
Airlines
Atmospheric data
Atmospheric models
Atmospheric turbulence
Aviation
Commercial aircraft
Datasets
Dynamic stability
Energy dissipation
Energy exchange
Flight
Forecasting models
Geophysics
Horizontal distribution
Instability
Kelvin-helmholtz instability
Mathematical analysis
Mountains
Numerical forecasting
Numerical forecasting models
Numerical weather forecasting
Oligodendrocyte-myelin glycoprotein
R&D
Radiosonde data
Radiosondes
Research & development
Seasonal variation
Seasonal variations
Vortices
Weather forecasting
title Comparison of Eddy Dissipation Rate Estimated From Operational Radiosonde and Commercial Aircraft Observations in the United States
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-13T08%3A41%3A26IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Comparison%20of%20Eddy%20Dissipation%20Rate%20Estimated%20From%20Operational%20Radiosonde%20and%20Commercial%20Aircraft%20Observations%20in%20the%20United%20States&rft.jtitle=Journal%20of%20geophysical%20research.%20Atmospheres&rft.au=Ko,%20Han%E2%80%90Chang&rft.date=2023-10-27&rft.volume=128&rft.issue=20&rft.issn=2169-897X&rft.eissn=2169-8996&rft_id=info:doi/10.1029/2023JD039352&rft_dat=%3Cproquest_cross%3E2881747174%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2881747174&rft_id=info:pmid/&rfr_iscdi=true