Characteristics of Latent Heating Rate From GPM and Convective Gravity Wave Momentum Flux Calculated Using the GPM Data
Parameterizations of convective gravity‐wave (CGW) drag (CGWD) require cloud information as input parameters. As cloud information provided from reanalyses includes some uncertainties, observed cloud information is required for better representation of CGWs. For this, characteristics of the latent h...
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| Veröffentlicht in: | Journal of geophysical research. Atmospheres 2022-09, Vol.127 (17), p.n/a |
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| creator | Lee, Hyun‐Kyu Kang, Min‐Jee Chun, Hye‐Yeong Kim, Donghyeck Shin, Dong‐Bin |
| description | Parameterizations of convective gravity‐wave (CGW) drag (CGWD) require cloud information as input parameters. As cloud information provided from reanalyses includes some uncertainties, observed cloud information is required for better representation of CGWs. For this, characteristics of the latent heating rate (LHR) based on the Global Precipitation Measurement (GPM) satellite over 6 yr (June 2014 to May 2020) are investigated, and the CGW momentum flux and CGWD based on an offline CGWD parameterization are calculated using the GPM‐LHR and the Modern‐Era Retrospective Analysis for Research and Applications, Version 2 (MERRA‐2) background variables. Additionally, they are compared with those using LHR afforded by MERRA‐2. The averaged cloud‐bottom height is lower than that from MERRA but the cloud top height is similar for the both data, yielding deeper clouds from GPM that can generate more high phase‐speed components of CGWs. The column‐maximum heating rate, which is an input of the CGW momentum flux, of GPM‐LHR is maximal near the equator and the secondary maximum locates in the winter hemisphere storm tracks. The maximum of the cloud top momentum flux (CTMF) of CGWs locates in the winter hemisphere storm tracks, with the GPM‐CTMF being much larger than MERRA‐CTMF, as extreme convective events occur more frequently in GPM. In the equatorial region above z = 40 km, the GPM‐CGWD is significantly larger because high phase‐speed components of CGWs that survive up to the upper stratosphere are abundant for GPM‐CTMF, and this will contribute to drive more realistic semi‐annual oscillation.
Key Points
Convective gravity wave momentum flux (CGWMF) and drag (CGWD) are calculated using the Global Precipitation Measurement (GPM) and Modern‐Era Retrospective Analysis for Research and Applications, Version 2 (MERRA‐2) heating rate
The CGWMF at the cloud top calculated using GPM is greater at high‐phase speeds than that using MERRA‐2 due to deeper clouds from GPM
Using two different heating rates does not change significantly CGWD below z = 40 km in the tropics |
| doi_str_mv | 10.1029/2022JD037003 |
| format | Article |
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Key Points
Convective gravity wave momentum flux (CGWMF) and drag (CGWD) are calculated using the Global Precipitation Measurement (GPM) and Modern‐Era Retrospective Analysis for Research and Applications, Version 2 (MERRA‐2) heating rate
The CGWMF at the cloud top calculated using GPM is greater at high‐phase speeds than that using MERRA‐2 due to deeper clouds from GPM
Using two different heating rates does not change significantly CGWD below z = 40 km in the tropics</description><identifier>ISSN: 2169-897X</identifier><identifier>EISSN: 2169-8996</identifier><identifier>DOI: 10.1029/2022JD037003</identifier><language>eng</language><publisher>Washington: Blackwell Publishing Ltd</publisher><subject>Annual oscillation ; cloud top momentum flux ; Clouds ; Components ; convective gravity waves ; Equator ; Equatorial regions ; Fluctuations ; Geophysics ; Global precipitation ; gravity wave drag ; Gravity waves ; Heating ; Heating rate ; Height ; latent heating rate ; Mathematical analysis ; Momentum ; Momentum flux ; Momentum transfer ; Parameterization ; Storm tracks ; Storms ; Stratosphere ; Survival ; Upper stratosphere ; Winter</subject><ispartof>Journal of geophysical research. Atmospheres, 2022-09, Vol.127 (17), p.n/a</ispartof><rights>2022. The Authors.</rights><rights>2022. 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><citedby>FETCH-LOGICAL-c2755-26c09f8274a3c3892e4abde692e49bbfde03abcf8fd908eb0f92c0e737d9b3503</citedby><cites>FETCH-LOGICAL-c2755-26c09f8274a3c3892e4abde692e49bbfde03abcf8fd908eb0f92c0e737d9b3503</cites><orcidid>0000-0003-4450-0990 ; 0000-0002-2014-4728 ; 0000-0002-8039-5887 ; 0000-0002-3936-5504</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1029%2F2022JD037003$$EPDF$$P50$$Gwiley$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1029%2F2022JD037003$$EHTML$$P50$$Gwiley$$Hfree_for_read</linktohtml><link.rule.ids>314,780,784,1416,1432,27923,27924,45573,45574,46408,46832</link.rule.ids></links><search><creatorcontrib>Lee, Hyun‐Kyu</creatorcontrib><creatorcontrib>Kang, Min‐Jee</creatorcontrib><creatorcontrib>Chun, Hye‐Yeong</creatorcontrib><creatorcontrib>Kim, Donghyeck</creatorcontrib><creatorcontrib>Shin, Dong‐Bin</creatorcontrib><title>Characteristics of Latent Heating Rate From GPM and Convective Gravity Wave Momentum Flux Calculated Using the GPM Data</title><title>Journal of geophysical research. Atmospheres</title><description>Parameterizations of convective gravity‐wave (CGW) drag (CGWD) require cloud information as input parameters. As cloud information provided from reanalyses includes some uncertainties, observed cloud information is required for better representation of CGWs. For this, characteristics of the latent heating rate (LHR) based on the Global Precipitation Measurement (GPM) satellite over 6 yr (June 2014 to May 2020) are investigated, and the CGW momentum flux and CGWD based on an offline CGWD parameterization are calculated using the GPM‐LHR and the Modern‐Era Retrospective Analysis for Research and Applications, Version 2 (MERRA‐2) background variables. Additionally, they are compared with those using LHR afforded by MERRA‐2. The averaged cloud‐bottom height is lower than that from MERRA but the cloud top height is similar for the both data, yielding deeper clouds from GPM that can generate more high phase‐speed components of CGWs. The column‐maximum heating rate, which is an input of the CGW momentum flux, of GPM‐LHR is maximal near the equator and the secondary maximum locates in the winter hemisphere storm tracks. The maximum of the cloud top momentum flux (CTMF) of CGWs locates in the winter hemisphere storm tracks, with the GPM‐CTMF being much larger than MERRA‐CTMF, as extreme convective events occur more frequently in GPM. In the equatorial region above z = 40 km, the GPM‐CGWD is significantly larger because high phase‐speed components of CGWs that survive up to the upper stratosphere are abundant for GPM‐CTMF, and this will contribute to drive more realistic semi‐annual oscillation.
Key Points
Convective gravity wave momentum flux (CGWMF) and drag (CGWD) are calculated using the Global Precipitation Measurement (GPM) and Modern‐Era Retrospective Analysis for Research and Applications, Version 2 (MERRA‐2) heating rate
The CGWMF at the cloud top calculated using GPM is greater at high‐phase speeds than that using MERRA‐2 due to deeper clouds from GPM
Using two different heating rates does not change significantly CGWD below z = 40 km in the tropics</description><subject>Annual oscillation</subject><subject>cloud top momentum flux</subject><subject>Clouds</subject><subject>Components</subject><subject>convective gravity waves</subject><subject>Equator</subject><subject>Equatorial regions</subject><subject>Fluctuations</subject><subject>Geophysics</subject><subject>Global precipitation</subject><subject>gravity wave drag</subject><subject>Gravity waves</subject><subject>Heating</subject><subject>Heating rate</subject><subject>Height</subject><subject>latent heating rate</subject><subject>Mathematical analysis</subject><subject>Momentum</subject><subject>Momentum flux</subject><subject>Momentum transfer</subject><subject>Parameterization</subject><subject>Storm tracks</subject><subject>Storms</subject><subject>Stratosphere</subject><subject>Survival</subject><subject>Upper stratosphere</subject><subject>Winter</subject><issn>2169-897X</issn><issn>2169-8996</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>24P</sourceid><sourceid>WIN</sourceid><recordid>eNp9kN1LwzAUxYsoOHRv_gEBX63eJv1IHqVznWNDGQ59K2mauI5-zCTd3H9v5kR88r7ce-B3zoXjeVcB3AaA2R0GjKcjIAkAOfEGOIiZTxmLT3_v5O3cGxqzBjcUSBiFA2-XrrjmwkpdGVsJgzqFZtzK1qKJ5LZq39HCSTTWXYOy5znibYnSrt1KYautRJnm28ru0St3Yt41ztg3aFz3nyjltehrZy7R0hyC7Ep-R4y45ZfemeK1kcOffeEtxw8v6cSfPWWP6f3MFziJIh_HApiiOAk5EYQyLENelDI-HKwoVCmB8EIoqkoGVBagGBYgE5KUrCARkAvv-pi70d1HL43N112vW_cyx0mAKUDMQkfdHCmhO2O0VPlGVw3X-zyA_NBu_rddh5Mjvqtquf-XzafZYhTRgEbkCzLSeu4</recordid><startdate>20220916</startdate><enddate>20220916</enddate><creator>Lee, Hyun‐Kyu</creator><creator>Kang, Min‐Jee</creator><creator>Chun, Hye‐Yeong</creator><creator>Kim, Donghyeck</creator><creator>Shin, Dong‐Bin</creator><general>Blackwell Publishing Ltd</general><scope>24P</scope><scope>WIN</scope><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/0000-0003-4450-0990</orcidid><orcidid>https://orcid.org/0000-0002-2014-4728</orcidid><orcidid>https://orcid.org/0000-0002-8039-5887</orcidid><orcidid>https://orcid.org/0000-0002-3936-5504</orcidid></search><sort><creationdate>20220916</creationdate><title>Characteristics of Latent Heating Rate From GPM and Convective Gravity Wave Momentum Flux Calculated Using the GPM Data</title><author>Lee, Hyun‐Kyu ; Kang, Min‐Jee ; Chun, Hye‐Yeong ; Kim, Donghyeck ; Shin, Dong‐Bin</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c2755-26c09f8274a3c3892e4abde692e49bbfde03abcf8fd908eb0f92c0e737d9b3503</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Annual oscillation</topic><topic>cloud top momentum flux</topic><topic>Clouds</topic><topic>Components</topic><topic>convective gravity waves</topic><topic>Equator</topic><topic>Equatorial regions</topic><topic>Fluctuations</topic><topic>Geophysics</topic><topic>Global precipitation</topic><topic>gravity wave drag</topic><topic>Gravity waves</topic><topic>Heating</topic><topic>Heating rate</topic><topic>Height</topic><topic>latent heating rate</topic><topic>Mathematical analysis</topic><topic>Momentum</topic><topic>Momentum flux</topic><topic>Momentum transfer</topic><topic>Parameterization</topic><topic>Storm tracks</topic><topic>Storms</topic><topic>Stratosphere</topic><topic>Survival</topic><topic>Upper stratosphere</topic><topic>Winter</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Lee, Hyun‐Kyu</creatorcontrib><creatorcontrib>Kang, Min‐Jee</creatorcontrib><creatorcontrib>Chun, Hye‐Yeong</creatorcontrib><creatorcontrib>Kim, Donghyeck</creatorcontrib><creatorcontrib>Shin, Dong‐Bin</creatorcontrib><collection>Wiley-Blackwell Open Access Titles</collection><collection>Wiley Free Content</collection><collection>CrossRef</collection><collection>Meteorological & 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 & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>Civil Engineering Abstracts</collection><collection>Aquatic Science & 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>Lee, Hyun‐Kyu</au><au>Kang, Min‐Jee</au><au>Chun, Hye‐Yeong</au><au>Kim, Donghyeck</au><au>Shin, Dong‐Bin</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Characteristics of Latent Heating Rate From GPM and Convective Gravity Wave Momentum Flux Calculated Using the GPM Data</atitle><jtitle>Journal of geophysical research. Atmospheres</jtitle><date>2022-09-16</date><risdate>2022</risdate><volume>127</volume><issue>17</issue><epage>n/a</epage><issn>2169-897X</issn><eissn>2169-8996</eissn><abstract>Parameterizations of convective gravity‐wave (CGW) drag (CGWD) require cloud information as input parameters. As cloud information provided from reanalyses includes some uncertainties, observed cloud information is required for better representation of CGWs. For this, characteristics of the latent heating rate (LHR) based on the Global Precipitation Measurement (GPM) satellite over 6 yr (June 2014 to May 2020) are investigated, and the CGW momentum flux and CGWD based on an offline CGWD parameterization are calculated using the GPM‐LHR and the Modern‐Era Retrospective Analysis for Research and Applications, Version 2 (MERRA‐2) background variables. Additionally, they are compared with those using LHR afforded by MERRA‐2. The averaged cloud‐bottom height is lower than that from MERRA but the cloud top height is similar for the both data, yielding deeper clouds from GPM that can generate more high phase‐speed components of CGWs. The column‐maximum heating rate, which is an input of the CGW momentum flux, of GPM‐LHR is maximal near the equator and the secondary maximum locates in the winter hemisphere storm tracks. The maximum of the cloud top momentum flux (CTMF) of CGWs locates in the winter hemisphere storm tracks, with the GPM‐CTMF being much larger than MERRA‐CTMF, as extreme convective events occur more frequently in GPM. In the equatorial region above z = 40 km, the GPM‐CGWD is significantly larger because high phase‐speed components of CGWs that survive up to the upper stratosphere are abundant for GPM‐CTMF, and this will contribute to drive more realistic semi‐annual oscillation.
Key Points
Convective gravity wave momentum flux (CGWMF) and drag (CGWD) are calculated using the Global Precipitation Measurement (GPM) and Modern‐Era Retrospective Analysis for Research and Applications, Version 2 (MERRA‐2) heating rate
The CGWMF at the cloud top calculated using GPM is greater at high‐phase speeds than that using MERRA‐2 due to deeper clouds from GPM
Using two different heating rates does not change significantly CGWD below z = 40 km in the tropics</abstract><cop>Washington</cop><pub>Blackwell Publishing Ltd</pub><doi>10.1029/2022JD037003</doi><tpages>25</tpages><orcidid>https://orcid.org/0000-0003-4450-0990</orcidid><orcidid>https://orcid.org/0000-0002-2014-4728</orcidid><orcidid>https://orcid.org/0000-0002-8039-5887</orcidid><orcidid>https://orcid.org/0000-0002-3936-5504</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Annual oscillation cloud top momentum flux Clouds Components convective gravity waves Equator Equatorial regions Fluctuations Geophysics Global precipitation gravity wave drag Gravity waves Heating Heating rate Height latent heating rate Mathematical analysis Momentum Momentum flux Momentum transfer Parameterization Storm tracks Storms Stratosphere Survival Upper stratosphere Winter |
| title | Characteristics of Latent Heating Rate From GPM and Convective Gravity Wave Momentum Flux Calculated Using the GPM Data |
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