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An intercomparison of long‐term planetary boundary layer heights retrieved from CALIPSO, ground‐based lidar, and radiosonde measurements over Hong Kong
The planetary boundary layer height (PBLH) is a very important parameter in the atmosphere, because it determines the range where the most effective dispersion processes take place, and serves as a constraint on the vertical transport of heat, moisture, and pollutants. As the only space‐borne lidar,...
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Published in: | Journal of geophysical research. Atmospheres 2017-04, Vol.122 (7), p.3929-3943 |
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description | The planetary boundary layer height (PBLH) is a very important parameter in the atmosphere, because it determines the range where the most effective dispersion processes take place, and serves as a constraint on the vertical transport of heat, moisture, and pollutants. As the only space‐borne lidar, Cloud‐Aerosol Lidar with Orthogonal Polarization onboard Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) measures the vertical distribution of aerosol signals and thus offers the potential to retrieve large‐scale PBLH climatology. In this study, we explore different techniques for retrieving PBLH from CALIPSO measurements and validate the results against those obtained from ground‐based micropulse lidar (MPL) and radiosonde (RS) data over Hong Kong, where long‐term MPL and RS measurements are available. Two methods, namely maximum standard deviation (MSD) and wavelet covariance transform (WCT), are used to retrieve PBLH from CALIPSO. Results show that the RS‐ and MPL‐derived PBLHs share similar interannual variation and seasonality and can complement each other. Both MSD and WCT perform reasonably well compared with MPL/RS products, especially under sufficient aerosol loading. Uncertainties increase when aerosol loading is low and the CALIPSO signal consequently becomes noisier. Overall, CALIPSO captures the general PBLH seasonal variability over Hong Kong, despite a high bias in spring and a low bias in summer. The spring high bias is likely associated with elevated aerosol layers due to transport, while the summer low bias can be attributed to higher noise level associated with weaker aerosol signal.
Key Points
CALIPSO data demonstrated good performance in estimating PBLH, and WCT appears to be a suitable technique
CALIPSO results best agree with MPL and RS for winter and fall when aerosol loading is relatively high
Elevated aerosol layers in spring and low aerosol loadings in summer are likely causes for the bias in CALIPSO PBLH |
doi_str_mv | 10.1002/2016JD025937 |
format | article |
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Key Points
CALIPSO data demonstrated good performance in estimating PBLH, and WCT appears to be a suitable technique
CALIPSO results best agree with MPL and RS for winter and fall when aerosol loading is relatively high
Elevated aerosol layers in spring and low aerosol loadings in summer are likely causes for the bias in CALIPSO PBLH</description><identifier>ISSN: 2169-897X</identifier><identifier>EISSN: 2169-8996</identifier><identifier>DOI: 10.1002/2016JD025937</identifier><language>eng</language><publisher>Washington: Blackwell Publishing Ltd</publisher><subject>Aerosol layers ; Aerosols ; Airborne particulates ; Atmosphere ; Bias ; Boundary layer height ; Boundary layers ; CALIPSO (Pathfinder satellite) ; CALIPSO space lidar ; Climatology ; Covariance ; Dispersion ; Dispersion processes ; Geophysics ; Heat ; Height ; Intercomparison ; Lidar ; Lidar measurements ; maximum standard deviation method ; Meteorological satellites ; Methods ; micropulse lidar ; Moisture ; Noise ; Noise levels ; Onboard ; Planetary boundary layer ; planetary boundary layer height ; Polarization ; Pollutants ; Pollution dispersion ; Products ; radiosonde ; Radiosondes ; Satellite observation ; Satellites ; Seasonal variability ; Seasonal variation ; Seasonal variations ; Seasonality ; Spring ; Spring (season) ; Summer ; Transport ; Variability ; Vertical advection ; Vertical distribution ; Wavelet analysis ; wavelet covariance transform method ; Winter</subject><ispartof>Journal of geophysical research. Atmospheres, 2017-04, Vol.122 (7), p.3929-3943</ispartof><rights>2017. American Geophysical Union. All Rights Reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c4676-b7e334fe8744ba2c0290d69b1eadc2c059704e09b884cb35d6e4be1ec4fa55cf3</citedby><cites>FETCH-LOGICAL-c4676-b7e334fe8744ba2c0290d69b1eadc2c059704e09b884cb35d6e4be1ec4fa55cf3</cites><orcidid>0000-0001-8530-8976 ; 0000-0002-0540-0412 ; 0000-0001-8860-1916 ; 0000-0003-3056-2592</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27923,27924</link.rule.ids></links><search><creatorcontrib>Su, Tianning</creatorcontrib><creatorcontrib>Li, Jing</creatorcontrib><creatorcontrib>Li, Chengcai</creatorcontrib><creatorcontrib>Xiang, Pengzhan</creatorcontrib><creatorcontrib>Lau, Alexis Kai‐Hon</creatorcontrib><creatorcontrib>Guo, Jianping</creatorcontrib><creatorcontrib>Yang, Dongwei</creatorcontrib><creatorcontrib>Miao, Yucong</creatorcontrib><title>An intercomparison of long‐term planetary boundary layer heights retrieved from CALIPSO, ground‐based lidar, and radiosonde measurements over Hong Kong</title><title>Journal of geophysical research. Atmospheres</title><description>The planetary boundary layer height (PBLH) is a very important parameter in the atmosphere, because it determines the range where the most effective dispersion processes take place, and serves as a constraint on the vertical transport of heat, moisture, and pollutants. As the only space‐borne lidar, Cloud‐Aerosol Lidar with Orthogonal Polarization onboard Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) measures the vertical distribution of aerosol signals and thus offers the potential to retrieve large‐scale PBLH climatology. In this study, we explore different techniques for retrieving PBLH from CALIPSO measurements and validate the results against those obtained from ground‐based micropulse lidar (MPL) and radiosonde (RS) data over Hong Kong, where long‐term MPL and RS measurements are available. Two methods, namely maximum standard deviation (MSD) and wavelet covariance transform (WCT), are used to retrieve PBLH from CALIPSO. Results show that the RS‐ and MPL‐derived PBLHs share similar interannual variation and seasonality and can complement each other. Both MSD and WCT perform reasonably well compared with MPL/RS products, especially under sufficient aerosol loading. Uncertainties increase when aerosol loading is low and the CALIPSO signal consequently becomes noisier. Overall, CALIPSO captures the general PBLH seasonal variability over Hong Kong, despite a high bias in spring and a low bias in summer. The spring high bias is likely associated with elevated aerosol layers due to transport, while the summer low bias can be attributed to higher noise level associated with weaker aerosol signal.
Key Points
CALIPSO data demonstrated good performance in estimating PBLH, and WCT appears to be a suitable technique
CALIPSO results best agree with MPL and RS for winter and fall when aerosol loading is relatively high
Elevated aerosol layers in spring and low aerosol loadings in summer are likely causes for the bias in CALIPSO PBLH</description><subject>Aerosol layers</subject><subject>Aerosols</subject><subject>Airborne particulates</subject><subject>Atmosphere</subject><subject>Bias</subject><subject>Boundary layer height</subject><subject>Boundary layers</subject><subject>CALIPSO (Pathfinder satellite)</subject><subject>CALIPSO space lidar</subject><subject>Climatology</subject><subject>Covariance</subject><subject>Dispersion</subject><subject>Dispersion processes</subject><subject>Geophysics</subject><subject>Heat</subject><subject>Height</subject><subject>Intercomparison</subject><subject>Lidar</subject><subject>Lidar measurements</subject><subject>maximum standard deviation method</subject><subject>Meteorological satellites</subject><subject>Methods</subject><subject>micropulse lidar</subject><subject>Moisture</subject><subject>Noise</subject><subject>Noise levels</subject><subject>Onboard</subject><subject>Planetary boundary layer</subject><subject>planetary boundary layer height</subject><subject>Polarization</subject><subject>Pollutants</subject><subject>Pollution dispersion</subject><subject>Products</subject><subject>radiosonde</subject><subject>Radiosondes</subject><subject>Satellite observation</subject><subject>Satellites</subject><subject>Seasonal variability</subject><subject>Seasonal variation</subject><subject>Seasonal variations</subject><subject>Seasonality</subject><subject>Spring</subject><subject>Spring (season)</subject><subject>Summer</subject><subject>Transport</subject><subject>Variability</subject><subject>Vertical advection</subject><subject>Vertical distribution</subject><subject>Wavelet analysis</subject><subject>wavelet covariance transform method</subject><subject>Winter</subject><issn>2169-897X</issn><issn>2169-8996</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><recordid>eNqFkc1u1DAUhSMEElXprg9giQ2LGfB_7GXV0j8qFRUqsYsc52bqKrEHOymaHY_AnrfjSbijQQixaL2wr-3P5-j6VNUho28Zpfwdp0xfnlCurKifVXucabs01urnf-v6y8vqoJR7isNQIZXcq34eRRLiBNmnce1yKCmS1JMhxdWv7z_wfCTrwUWYXN6QNs2x2xaD20AmdxBWd1MhGaYc4AE60uc0kuOjq4uPn64XZJW3PMq0ruDlEPDtgrjYkey6kNCqAzKCK3OGESIqpQeUPUdv8gGnV9WL3g0FDv6s-9Xt6fvPx-fLq-uzC3RZeqlrvWxrEEL2YGopW8c95ZZ22rYMXOdxq2xNJVDbGiN9K1SnQbbAwMveKeV7sV-92emuc_o6Q5maMRQPw7bvNJeGWSq5ooLqp1H8ZWG4sRzR1_-h92nOERtBQaaEpprRRyljmTE1BoXUYkf5nErJ0DfrHEZMomG02abf_Js-4mKHfwsDbB5lm8uzmxMlaq7Fb641sys</recordid><startdate>20170416</startdate><enddate>20170416</enddate><creator>Su, Tianning</creator><creator>Li, Jing</creator><creator>Li, Chengcai</creator><creator>Xiang, Pengzhan</creator><creator>Lau, Alexis Kai‐Hon</creator><creator>Guo, Jianping</creator><creator>Yang, Dongwei</creator><creator>Miao, Yucong</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/0000-0001-8530-8976</orcidid><orcidid>https://orcid.org/0000-0002-0540-0412</orcidid><orcidid>https://orcid.org/0000-0001-8860-1916</orcidid><orcidid>https://orcid.org/0000-0003-3056-2592</orcidid></search><sort><creationdate>20170416</creationdate><title>An intercomparison of long‐term planetary boundary layer heights retrieved from CALIPSO, ground‐based lidar, and radiosonde measurements over Hong Kong</title><author>Su, Tianning ; Li, Jing ; Li, Chengcai ; Xiang, Pengzhan ; Lau, Alexis Kai‐Hon ; Guo, Jianping ; Yang, Dongwei ; Miao, Yucong</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c4676-b7e334fe8744ba2c0290d69b1eadc2c059704e09b884cb35d6e4be1ec4fa55cf3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Aerosol layers</topic><topic>Aerosols</topic><topic>Airborne particulates</topic><topic>Atmosphere</topic><topic>Bias</topic><topic>Boundary layer height</topic><topic>Boundary layers</topic><topic>CALIPSO (Pathfinder satellite)</topic><topic>CALIPSO space lidar</topic><topic>Climatology</topic><topic>Covariance</topic><topic>Dispersion</topic><topic>Dispersion processes</topic><topic>Geophysics</topic><topic>Heat</topic><topic>Height</topic><topic>Intercomparison</topic><topic>Lidar</topic><topic>Lidar measurements</topic><topic>maximum standard deviation method</topic><topic>Meteorological satellites</topic><topic>Methods</topic><topic>micropulse lidar</topic><topic>Moisture</topic><topic>Noise</topic><topic>Noise levels</topic><topic>Onboard</topic><topic>Planetary boundary layer</topic><topic>planetary boundary layer height</topic><topic>Polarization</topic><topic>Pollutants</topic><topic>Pollution dispersion</topic><topic>Products</topic><topic>radiosonde</topic><topic>Radiosondes</topic><topic>Satellite observation</topic><topic>Satellites</topic><topic>Seasonal variability</topic><topic>Seasonal variation</topic><topic>Seasonal variations</topic><topic>Seasonality</topic><topic>Spring</topic><topic>Spring (season)</topic><topic>Summer</topic><topic>Transport</topic><topic>Variability</topic><topic>Vertical advection</topic><topic>Vertical distribution</topic><topic>Wavelet analysis</topic><topic>wavelet covariance transform method</topic><topic>Winter</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Su, Tianning</creatorcontrib><creatorcontrib>Li, Jing</creatorcontrib><creatorcontrib>Li, Chengcai</creatorcontrib><creatorcontrib>Xiang, Pengzhan</creatorcontrib><creatorcontrib>Lau, Alexis Kai‐Hon</creatorcontrib><creatorcontrib>Guo, Jianping</creatorcontrib><creatorcontrib>Yang, Dongwei</creatorcontrib><creatorcontrib>Miao, Yucong</creatorcontrib><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>Su, Tianning</au><au>Li, Jing</au><au>Li, Chengcai</au><au>Xiang, Pengzhan</au><au>Lau, Alexis Kai‐Hon</au><au>Guo, Jianping</au><au>Yang, Dongwei</au><au>Miao, Yucong</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>An intercomparison of long‐term planetary boundary layer heights retrieved from CALIPSO, ground‐based lidar, and radiosonde measurements over Hong Kong</atitle><jtitle>Journal of geophysical research. Atmospheres</jtitle><date>2017-04-16</date><risdate>2017</risdate><volume>122</volume><issue>7</issue><spage>3929</spage><epage>3943</epage><pages>3929-3943</pages><issn>2169-897X</issn><eissn>2169-8996</eissn><abstract>The planetary boundary layer height (PBLH) is a very important parameter in the atmosphere, because it determines the range where the most effective dispersion processes take place, and serves as a constraint on the vertical transport of heat, moisture, and pollutants. As the only space‐borne lidar, Cloud‐Aerosol Lidar with Orthogonal Polarization onboard Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) measures the vertical distribution of aerosol signals and thus offers the potential to retrieve large‐scale PBLH climatology. In this study, we explore different techniques for retrieving PBLH from CALIPSO measurements and validate the results against those obtained from ground‐based micropulse lidar (MPL) and radiosonde (RS) data over Hong Kong, where long‐term MPL and RS measurements are available. Two methods, namely maximum standard deviation (MSD) and wavelet covariance transform (WCT), are used to retrieve PBLH from CALIPSO. Results show that the RS‐ and MPL‐derived PBLHs share similar interannual variation and seasonality and can complement each other. Both MSD and WCT perform reasonably well compared with MPL/RS products, especially under sufficient aerosol loading. Uncertainties increase when aerosol loading is low and the CALIPSO signal consequently becomes noisier. Overall, CALIPSO captures the general PBLH seasonal variability over Hong Kong, despite a high bias in spring and a low bias in summer. The spring high bias is likely associated with elevated aerosol layers due to transport, while the summer low bias can be attributed to higher noise level associated with weaker aerosol signal.
Key Points
CALIPSO data demonstrated good performance in estimating PBLH, and WCT appears to be a suitable technique
CALIPSO results best agree with MPL and RS for winter and fall when aerosol loading is relatively high
Elevated aerosol layers in spring and low aerosol loadings in summer are likely causes for the bias in CALIPSO PBLH</abstract><cop>Washington</cop><pub>Blackwell Publishing Ltd</pub><doi>10.1002/2016JD025937</doi><tpages>15</tpages><orcidid>https://orcid.org/0000-0001-8530-8976</orcidid><orcidid>https://orcid.org/0000-0002-0540-0412</orcidid><orcidid>https://orcid.org/0000-0001-8860-1916</orcidid><orcidid>https://orcid.org/0000-0003-3056-2592</orcidid></addata></record> |
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subjects | Aerosol layers Aerosols Airborne particulates Atmosphere Bias Boundary layer height Boundary layers CALIPSO (Pathfinder satellite) CALIPSO space lidar Climatology Covariance Dispersion Dispersion processes Geophysics Heat Height Intercomparison Lidar Lidar measurements maximum standard deviation method Meteorological satellites Methods micropulse lidar Moisture Noise Noise levels Onboard Planetary boundary layer planetary boundary layer height Polarization Pollutants Pollution dispersion Products radiosonde Radiosondes Satellite observation Satellites Seasonal variability Seasonal variation Seasonal variations Seasonality Spring Spring (season) Summer Transport Variability Vertical advection Vertical distribution Wavelet analysis wavelet covariance transform method Winter |
title | An intercomparison of long‐term planetary boundary layer heights retrieved from CALIPSO, ground‐based lidar, and radiosonde measurements over Hong Kong |
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