Regime‐Specific Cloud Vertical Overlap Characteristics From Radar and Lidar Observations at the ARM Sites
Climate and numerical weather prediction models require assumptions to represent the vertical distribution of subgrid‐scale clouds, which have radiative transfer implications. In this study, nearly 25 years of ground‐based radar and lidar observations of vertical cloud profiles at the Atmospheric Ra...
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description | Climate and numerical weather prediction models require assumptions to represent the vertical distribution of subgrid‐scale clouds, which have radiative transfer implications. In this study, nearly 25 years of ground‐based radar and lidar observations of vertical cloud profiles at the Atmospheric Radiation Measurement Program (ARM) Southern Great Plains (SGP) site are utilized to derive cloud vertical overlap characteristics from the Cloud Type (CLDTYPE) data product. The cloud vertical overlap characteristics are further separated by cloud regime by considering seven cloud types (i.e., low cloud, congestus, deep convection, altocumulus, altostratus, cirrostratus, and cirrus) as well as periods of shallow cumulus. The decorrelation length scale (i.e., exponential transition from maximum to random overlap with layer separation) is found to vary by cloud regime, ranging between 0.04 km for cirrostratus paired with cirrus to 4.58 km for low cloud paired with cirrus at SGP. Cloud vertical overlap characteristics are also considered for other ARM sites including the Tropical Western Pacific (TWP), North Slope of Alaska (NSA), and Eastern North Atlantic (ENA) sites among other shorter term ARM deployments globally. The decorrelation length scale ranged globally from 1.03 km in the Arctic Ocean to 3.06 km in Manacapuru, Brazil. Globally, the decorrelation length scale by cloud regime exhibited similarities (e.g., for cirrus paired with cirrus) and differences (e.g., congestus paired with cirrus). The results could help inform development of cloud vertical overlap assumptions within operational numerical weather prediction models and potentially improve prediction of radiative fluxes for weather, climate, and renewable energy forecasting.
Plain Language Summary
The complexity of cloud vertical structure requires approximations in weather and climate models, which has implication for the forecast of how much sunlight reaches the surface. Cloud observations from radars and lidars help to inform how cloud layers overlap vertically. The cloud observations ultimately inform what model assumptions best capture the observed cloud vertical structure. In this study, cloud profiles from ground‐based radars and lidars are considered to calculate cloud vertical overlap characteristics at 18 long‐term and short‐term sites across the globe. The cloud vertical overlap characteristics are also considered by cloud types to understand if cloud type provides additional information o |
doi_str_mv | 10.1029/2022JD037772 |
format | Article |
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Plain Language Summary
The complexity of cloud vertical structure requires approximations in weather and climate models, which has implication for the forecast of how much sunlight reaches the surface. Cloud observations from radars and lidars help to inform how cloud layers overlap vertically. The cloud observations ultimately inform what model assumptions best capture the observed cloud vertical structure. In this study, cloud profiles from ground‐based radars and lidars are considered to calculate cloud vertical overlap characteristics at 18 long‐term and short‐term sites across the globe. The cloud vertical overlap characteristics are also considered by cloud types to understand if cloud type provides additional information on the model assumptions to use. The cloud vertical overlap characteristics are found to vary by site, season, and cloud type. The results could help inform development of cloud vertical overlap assumptions within weather and climate models and potentially improve prediction of sunlight reaching the surface, which has implications for weather, climate, and renewable energy forecasting.
Key Points
Decorrelation length scales varied by cloud regime at the Atmospheric Radiation Measurement Program Southern Great Plains site between 0.04 and 4.58 km
Decorrelation length scales varied by site with values ranging from near 1 km in the Arctic to near 3 km in Brazil
Decorrelation length scales for the same cloud regime across the sites were similar, although some cloud regimes exhibited differences</description><identifier>ISSN: 2169-897X</identifier><identifier>EISSN: 2169-8996</identifier><identifier>DOI: 10.1029/2022JD037772</identifier><language>eng</language><publisher>Washington: Blackwell Publishing Ltd</publisher><subject>Altocumulus clouds ; Altostratus clouds ; Atmospheric models ; Atmospheric radiation ; Atmospheric radiation measurements ; Climate ; Climate and weather ; Climate models ; Climate prediction ; Cloud observations ; Cloud types ; Clouds ; Convection ; Downward long wave radiation ; ENVIRONMENTAL SCIENCES ; Geophysics ; Lidar ; Lidar observations ; Low clouds ; Meteorological satellites ; Modelling ; Numerical prediction ; Numerical weather forecasting ; Prediction models ; Radar ; Radiation measurement ; Radiative transfer ; Renewable energy ; Renewable resources ; Sunlight ; Vertical distribution ; Vertical profiles ; Weather ; Weather forecasting</subject><ispartof>Journal of geophysical research. Atmospheres, 2023-03, Vol.128 (6), p.n/a</ispartof><rights>2023 The Authors. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.</rights><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-c3297-4c92e9a7535f7dfd41aa6b06c20bc5615bd5b8b4b5d05e73b4b60cf6db5c237c3</cites><orcidid>0000-0003-1097-897X ; 0000-0003-3612-0808 ; 0000-0003-3101-9401 ; 0000-0001-9515-160X ; 0000-0003-0898-5177 ; 0000-0002-1794-3860 ; 0000000217943860 ; 0000000336120808 ; 000000019515160X ; 0000000331019401 ; 0000000308985177 ; 000000031097897X</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%2F2022JD037772$$EPDF$$P50$$Gwiley$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1029%2F2022JD037772$$EHTML$$P50$$Gwiley$$Hfree_for_read</linktohtml><link.rule.ids>230,314,776,780,881,1411,27903,27904,45553,45554</link.rule.ids><backlink>$$Uhttps://www.osti.gov/servlets/purl/1960768$$D View this record in Osti.gov$$Hfree_for_read</backlink></links><search><creatorcontrib>Balmes, Kelly A.</creatorcontrib><creatorcontrib>Sedlar, Joseph</creatorcontrib><creatorcontrib>Riihimaki, Laura D.</creatorcontrib><creatorcontrib>Olson, Joseph B.</creatorcontrib><creatorcontrib>Turner, David D.</creatorcontrib><creatorcontrib>Lantz, Kathleen</creatorcontrib><creatorcontrib>Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Atmospheric Radiation Measurement (ARM) Data Center</creatorcontrib><title>Regime‐Specific Cloud Vertical Overlap Characteristics From Radar and Lidar Observations at the ARM Sites</title><title>Journal of geophysical research. Atmospheres</title><description>Climate and numerical weather prediction models require assumptions to represent the vertical distribution of subgrid‐scale clouds, which have radiative transfer implications. In this study, nearly 25 years of ground‐based radar and lidar observations of vertical cloud profiles at the Atmospheric Radiation Measurement Program (ARM) Southern Great Plains (SGP) site are utilized to derive cloud vertical overlap characteristics from the Cloud Type (CLDTYPE) data product. The cloud vertical overlap characteristics are further separated by cloud regime by considering seven cloud types (i.e., low cloud, congestus, deep convection, altocumulus, altostratus, cirrostratus, and cirrus) as well as periods of shallow cumulus. The decorrelation length scale (i.e., exponential transition from maximum to random overlap with layer separation) is found to vary by cloud regime, ranging between 0.04 km for cirrostratus paired with cirrus to 4.58 km for low cloud paired with cirrus at SGP. Cloud vertical overlap characteristics are also considered for other ARM sites including the Tropical Western Pacific (TWP), North Slope of Alaska (NSA), and Eastern North Atlantic (ENA) sites among other shorter term ARM deployments globally. The decorrelation length scale ranged globally from 1.03 km in the Arctic Ocean to 3.06 km in Manacapuru, Brazil. Globally, the decorrelation length scale by cloud regime exhibited similarities (e.g., for cirrus paired with cirrus) and differences (e.g., congestus paired with cirrus). The results could help inform development of cloud vertical overlap assumptions within operational numerical weather prediction models and potentially improve prediction of radiative fluxes for weather, climate, and renewable energy forecasting.
Plain Language Summary
The complexity of cloud vertical structure requires approximations in weather and climate models, which has implication for the forecast of how much sunlight reaches the surface. Cloud observations from radars and lidars help to inform how cloud layers overlap vertically. The cloud observations ultimately inform what model assumptions best capture the observed cloud vertical structure. In this study, cloud profiles from ground‐based radars and lidars are considered to calculate cloud vertical overlap characteristics at 18 long‐term and short‐term sites across the globe. The cloud vertical overlap characteristics are also considered by cloud types to understand if cloud type provides additional information on the model assumptions to use. The cloud vertical overlap characteristics are found to vary by site, season, and cloud type. The results could help inform development of cloud vertical overlap assumptions within weather and climate models and potentially improve prediction of sunlight reaching the surface, which has implications for weather, climate, and renewable energy forecasting.
Key Points
Decorrelation length scales varied by cloud regime at the Atmospheric Radiation Measurement Program Southern Great Plains site between 0.04 and 4.58 km
Decorrelation length scales varied by site with values ranging from near 1 km in the Arctic to near 3 km in Brazil
Decorrelation length scales for the same cloud regime across the sites were similar, although some cloud regimes exhibited differences</description><subject>Altocumulus clouds</subject><subject>Altostratus clouds</subject><subject>Atmospheric models</subject><subject>Atmospheric radiation</subject><subject>Atmospheric radiation measurements</subject><subject>Climate</subject><subject>Climate and weather</subject><subject>Climate models</subject><subject>Climate prediction</subject><subject>Cloud observations</subject><subject>Cloud types</subject><subject>Clouds</subject><subject>Convection</subject><subject>Downward long wave radiation</subject><subject>ENVIRONMENTAL SCIENCES</subject><subject>Geophysics</subject><subject>Lidar</subject><subject>Lidar observations</subject><subject>Low clouds</subject><subject>Meteorological satellites</subject><subject>Modelling</subject><subject>Numerical prediction</subject><subject>Numerical weather forecasting</subject><subject>Prediction models</subject><subject>Radar</subject><subject>Radiation measurement</subject><subject>Radiative transfer</subject><subject>Renewable energy</subject><subject>Renewable resources</subject><subject>Sunlight</subject><subject>Vertical distribution</subject><subject>Vertical profiles</subject><subject>Weather</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><sourceid>24P</sourceid><recordid>eNp9kE1OwzAQhSMEEhV0xwEs2FLwT2zHyyqlhaqoUgqIneU4DnVJk2CnRd1xBM7ISUgVhFgxm3nS-zTz9ILgDMErBLG4xhDj6QgSzjk-CHoYMTGIhGCHv5o_Hwd971ewnQiSkIa94DUxL3Ztvj4-F7XRNrcaxEW1ycCTcY3VqgDzrXGFqkG8VE7pxjjrW8ODsavWIFGZckCVGZjZvZqn3ritamxVeqAa0CwNGCb3YGEb40-Do1wV3vR_9knwOL55iG8Hs_nkLh7OBppgwQehFtgIxSmhOc_yLERKsRQyjWGqKUM0zWgapWFKM0gNJ61iUOcsS6nGhGtyEpx3d6s2qfS6_a2XuipLoxuJBIOcRS100UG1q942xjdyVW1c2eaSmAtEGSVwT112lHaV987ksnZ2rdxOIij3tcu_tbc46fB3W5jdv6ycTpIRjSjl5BtgVIP4</recordid><startdate>20230327</startdate><enddate>20230327</enddate><creator>Balmes, Kelly A.</creator><creator>Sedlar, Joseph</creator><creator>Riihimaki, Laura D.</creator><creator>Olson, Joseph B.</creator><creator>Turner, David D.</creator><creator>Lantz, Kathleen</creator><general>Blackwell Publishing Ltd</general><general>American Geophysical Union</general><scope>24P</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><scope>OIOZB</scope><scope>OTOTI</scope><orcidid>https://orcid.org/0000-0003-1097-897X</orcidid><orcidid>https://orcid.org/0000-0003-3612-0808</orcidid><orcidid>https://orcid.org/0000-0003-3101-9401</orcidid><orcidid>https://orcid.org/0000-0001-9515-160X</orcidid><orcidid>https://orcid.org/0000-0003-0898-5177</orcidid><orcidid>https://orcid.org/0000-0002-1794-3860</orcidid><orcidid>https://orcid.org/0000000217943860</orcidid><orcidid>https://orcid.org/0000000336120808</orcidid><orcidid>https://orcid.org/000000019515160X</orcidid><orcidid>https://orcid.org/0000000331019401</orcidid><orcidid>https://orcid.org/0000000308985177</orcidid><orcidid>https://orcid.org/000000031097897X</orcidid></search><sort><creationdate>20230327</creationdate><title>Regime‐Specific Cloud Vertical Overlap Characteristics From Radar and Lidar Observations at the ARM Sites</title><author>Balmes, Kelly A. ; Sedlar, Joseph ; Riihimaki, Laura D. ; Olson, Joseph B. ; Turner, David D. ; Lantz, Kathleen</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3297-4c92e9a7535f7dfd41aa6b06c20bc5615bd5b8b4b5d05e73b4b60cf6db5c237c3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Altocumulus clouds</topic><topic>Altostratus clouds</topic><topic>Atmospheric models</topic><topic>Atmospheric radiation</topic><topic>Atmospheric radiation measurements</topic><topic>Climate</topic><topic>Climate and weather</topic><topic>Climate models</topic><topic>Climate prediction</topic><topic>Cloud observations</topic><topic>Cloud types</topic><topic>Clouds</topic><topic>Convection</topic><topic>Downward long wave radiation</topic><topic>ENVIRONMENTAL SCIENCES</topic><topic>Geophysics</topic><topic>Lidar</topic><topic>Lidar observations</topic><topic>Low clouds</topic><topic>Meteorological satellites</topic><topic>Modelling</topic><topic>Numerical prediction</topic><topic>Numerical weather forecasting</topic><topic>Prediction models</topic><topic>Radar</topic><topic>Radiation measurement</topic><topic>Radiative transfer</topic><topic>Renewable energy</topic><topic>Renewable resources</topic><topic>Sunlight</topic><topic>Vertical distribution</topic><topic>Vertical profiles</topic><topic>Weather</topic><topic>Weather forecasting</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Balmes, Kelly A.</creatorcontrib><creatorcontrib>Sedlar, Joseph</creatorcontrib><creatorcontrib>Riihimaki, Laura D.</creatorcontrib><creatorcontrib>Olson, Joseph B.</creatorcontrib><creatorcontrib>Turner, David D.</creatorcontrib><creatorcontrib>Lantz, Kathleen</creatorcontrib><creatorcontrib>Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Atmospheric Radiation Measurement (ARM) Data Center</creatorcontrib><collection>Wiley Open Access Journals</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><collection>OSTI.GOV - Hybrid</collection><collection>OSTI.GOV</collection><jtitle>Journal of geophysical research. Atmospheres</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Balmes, Kelly A.</au><au>Sedlar, Joseph</au><au>Riihimaki, Laura D.</au><au>Olson, Joseph B.</au><au>Turner, David D.</au><au>Lantz, Kathleen</au><aucorp>Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Atmospheric Radiation Measurement (ARM) Data Center</aucorp><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Regime‐Specific Cloud Vertical Overlap Characteristics From Radar and Lidar Observations at the ARM Sites</atitle><jtitle>Journal of geophysical research. Atmospheres</jtitle><date>2023-03-27</date><risdate>2023</risdate><volume>128</volume><issue>6</issue><epage>n/a</epage><issn>2169-897X</issn><eissn>2169-8996</eissn><abstract>Climate and numerical weather prediction models require assumptions to represent the vertical distribution of subgrid‐scale clouds, which have radiative transfer implications. In this study, nearly 25 years of ground‐based radar and lidar observations of vertical cloud profiles at the Atmospheric Radiation Measurement Program (ARM) Southern Great Plains (SGP) site are utilized to derive cloud vertical overlap characteristics from the Cloud Type (CLDTYPE) data product. The cloud vertical overlap characteristics are further separated by cloud regime by considering seven cloud types (i.e., low cloud, congestus, deep convection, altocumulus, altostratus, cirrostratus, and cirrus) as well as periods of shallow cumulus. The decorrelation length scale (i.e., exponential transition from maximum to random overlap with layer separation) is found to vary by cloud regime, ranging between 0.04 km for cirrostratus paired with cirrus to 4.58 km for low cloud paired with cirrus at SGP. Cloud vertical overlap characteristics are also considered for other ARM sites including the Tropical Western Pacific (TWP), North Slope of Alaska (NSA), and Eastern North Atlantic (ENA) sites among other shorter term ARM deployments globally. The decorrelation length scale ranged globally from 1.03 km in the Arctic Ocean to 3.06 km in Manacapuru, Brazil. Globally, the decorrelation length scale by cloud regime exhibited similarities (e.g., for cirrus paired with cirrus) and differences (e.g., congestus paired with cirrus). The results could help inform development of cloud vertical overlap assumptions within operational numerical weather prediction models and potentially improve prediction of radiative fluxes for weather, climate, and renewable energy forecasting.
Plain Language Summary
The complexity of cloud vertical structure requires approximations in weather and climate models, which has implication for the forecast of how much sunlight reaches the surface. Cloud observations from radars and lidars help to inform how cloud layers overlap vertically. The cloud observations ultimately inform what model assumptions best capture the observed cloud vertical structure. In this study, cloud profiles from ground‐based radars and lidars are considered to calculate cloud vertical overlap characteristics at 18 long‐term and short‐term sites across the globe. The cloud vertical overlap characteristics are also considered by cloud types to understand if cloud type provides additional information on the model assumptions to use. The cloud vertical overlap characteristics are found to vary by site, season, and cloud type. The results could help inform development of cloud vertical overlap assumptions within weather and climate models and potentially improve prediction of sunlight reaching the surface, which has implications for weather, climate, and renewable energy forecasting.
Key Points
Decorrelation length scales varied by cloud regime at the Atmospheric Radiation Measurement Program Southern Great Plains site between 0.04 and 4.58 km
Decorrelation length scales varied by site with values ranging from near 1 km in the Arctic to near 3 km in Brazil
Decorrelation length scales for the same cloud regime across the sites were similar, although some cloud regimes exhibited differences</abstract><cop>Washington</cop><pub>Blackwell Publishing Ltd</pub><doi>10.1029/2022JD037772</doi><tpages>16</tpages><orcidid>https://orcid.org/0000-0003-1097-897X</orcidid><orcidid>https://orcid.org/0000-0003-3612-0808</orcidid><orcidid>https://orcid.org/0000-0003-3101-9401</orcidid><orcidid>https://orcid.org/0000-0001-9515-160X</orcidid><orcidid>https://orcid.org/0000-0003-0898-5177</orcidid><orcidid>https://orcid.org/0000-0002-1794-3860</orcidid><orcidid>https://orcid.org/0000000217943860</orcidid><orcidid>https://orcid.org/0000000336120808</orcidid><orcidid>https://orcid.org/000000019515160X</orcidid><orcidid>https://orcid.org/0000000331019401</orcidid><orcidid>https://orcid.org/0000000308985177</orcidid><orcidid>https://orcid.org/000000031097897X</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Altocumulus clouds Altostratus clouds Atmospheric models Atmospheric radiation Atmospheric radiation measurements Climate Climate and weather Climate models Climate prediction Cloud observations Cloud types Clouds Convection Downward long wave radiation ENVIRONMENTAL SCIENCES Geophysics Lidar Lidar observations Low clouds Meteorological satellites Modelling Numerical prediction Numerical weather forecasting Prediction models Radar Radiation measurement Radiative transfer Renewable energy Renewable resources Sunlight Vertical distribution Vertical profiles Weather Weather forecasting |
title | Regime‐Specific Cloud Vertical Overlap Characteristics From Radar and Lidar Observations at the ARM Sites |
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