Evaluation of Radiative Transfer Models With Clouds
Data from hyperspectral infrared sounders are routinely ingested worldwide by the National Weather Centers. The cloud‐free fraction of this data is used for initializing forecasts which include temperature, water vapor, water cloud, and ice cloud profiles on a global grid. Although the data from the...
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creator | Aumann, Hartmut H. Chen, Xiuhong Fishbein, Evan Geer, Alan Havemann, Stephan Huang, Xianglei Liu, Xu Liuzzi, Giuliano DeSouza‐Machado, Sergio Manning, Evan M. Masiello, Guido Matricardi, Marco Moradi, Isaac Natraj, Vijay Serio, Carmine Strow, Larrabee Vidot, Jerome Chris Wilson, R. Wu, Wan Yang, Qiguang Yung, Yuk L. |
description | Data from hyperspectral infrared sounders are routinely ingested worldwide by the National Weather Centers. The cloud‐free fraction of this data is used for initializing forecasts which include temperature, water vapor, water cloud, and ice cloud profiles on a global grid. Although the data from these sounders are sensitive to the vertical distribution of ice and liquid water in clouds, this information is not fully utilized. In the future, this information could be used for validating clouds in National Weather Center models and for initializing forecasts. We evaluate how well the calculated radiances from hyperspectral Radiative Transfer Models (RTMs) compare to cloudy radiances observed by AIRS and to one another. Vertical profiles of the clouds, temperature, and water vapor from the European Center for Medium‐Range Weather Forecasting were used as input for the RTMs. For nonfrozen ocean day and night data, the histograms derived from the calculations by several RTMs at 900 cm−1 have a better than 0.95 correlation with the histogram derived from the AIRS observations, with a bias relative to AIRS of typically less than 2 K. Differences in the cloud physics and cloud overlap assumptions result in little bias between the RTMs, but the standard deviation of the differences ranges from 6 to 12 K. Results at 2,616 cm−1 at night are reasonably consistent with results at 900 cm−1. Except for RTMs which use full scattering calculations, the bias and histogram correlations at 2,616 cm−1 are inferior to those at 900 cm−1 for daytime calculations.
Plain Language Summary
Getting the right clouds of the right type, at the right time and location in Global Circulation Models, is key to getting the local energy balance right. This is key to an accurate forecast. If the clouds are of the wrong type or at the wrong location or time, the accuracy of the forecast is degraded. We evaluate the accuracy of the best currently available cloud description (produced by the European Center for Medium‐Range Weather Forecasting) by comparing the radiances calculated using Radiative Transfer Models (RTMs) from six major development teams to cloudy radiances observed by the Atmospheric Infrared Sounder at the same location and time. The better RTMs fit statistically reasonably well in the 11‐μm atmospheric window area, with little latitude (zonal) and day/night cloud‐type related bias. None of the RTMs fit well in the 4‐μm atmospheric window area during daytime, unless the calculatio |
doi_str_mv | 10.1029/2017JD028063 |
format | Article |
fullrecord | <record><control><sourceid>proquest_hal_p</sourceid><recordid>TN_cdi_hal_primary_oai_HAL_hal_03496946v1</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2058556526</sourcerecordid><originalsourceid>FETCH-LOGICAL-c3797-bcd41539906becf74c9ea52edc40b9f488e75ef90ac6ea76626d0cc0810bae203</originalsourceid><addsrcrecordid>eNp9kEtLAzEUhYMoWGp3_oABV4KjN89JlqWtraUilIruQiaToVPGpiadSv-9U0bElXdzH3wczj0IXWO4x0DUAwGczcdAJAh6hnoEC5VKpcT575y9X6JBjBtoSwJlnPUQnRxM3Zh95beJL5OlKap2ObhkFcw2li4kz75wdUzeqv06GdW-KeIVuihNHd3gp_fR6-NkNZqli5fp02i4SC3NVJbmtmCYU6VA5M6WGbPKGU5cYRnkqmRSuoy7UoGxwplMCCIKsBYkhtw4ArSPbjvdtan1LlQfJhy1N5WeDRf6dGt_UEIxccAte9Oxu-A_Gxf3euObsG3taQJcci44ES1111E2-BiDK39lMehTivpvii1OO_yrqt3xX1bPp8sxZ1xm9BsDc3Dc</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2058556526</pqid></control><display><type>article</type><title>Evaluation of Radiative Transfer Models With Clouds</title><source>Wiley Online Library Journals Frontfile Complete</source><source>Wiley Online Library Free Content</source><source>Alma/SFX Local Collection</source><creator>Aumann, Hartmut H. ; Chen, Xiuhong ; Fishbein, Evan ; Geer, Alan ; Havemann, Stephan ; Huang, Xianglei ; Liu, Xu ; Liuzzi, Giuliano ; DeSouza‐Machado, Sergio ; Manning, Evan M. ; Masiello, Guido ; Matricardi, Marco ; Moradi, Isaac ; Natraj, Vijay ; Serio, Carmine ; Strow, Larrabee ; Vidot, Jerome ; Chris Wilson, R. ; Wu, Wan ; Yang, Qiguang ; Yung, Yuk L.</creator><creatorcontrib>Aumann, Hartmut H. ; Chen, Xiuhong ; Fishbein, Evan ; Geer, Alan ; Havemann, Stephan ; Huang, Xianglei ; Liu, Xu ; Liuzzi, Giuliano ; DeSouza‐Machado, Sergio ; Manning, Evan M. ; Masiello, Guido ; Matricardi, Marco ; Moradi, Isaac ; Natraj, Vijay ; Serio, Carmine ; Strow, Larrabee ; Vidot, Jerome ; Chris Wilson, R. ; Wu, Wan ; Yang, Qiguang ; Yung, Yuk L.</creatorcontrib><description>Data from hyperspectral infrared sounders are routinely ingested worldwide by the National Weather Centers. The cloud‐free fraction of this data is used for initializing forecasts which include temperature, water vapor, water cloud, and ice cloud profiles on a global grid. Although the data from these sounders are sensitive to the vertical distribution of ice and liquid water in clouds, this information is not fully utilized. In the future, this information could be used for validating clouds in National Weather Center models and for initializing forecasts. We evaluate how well the calculated radiances from hyperspectral Radiative Transfer Models (RTMs) compare to cloudy radiances observed by AIRS and to one another. Vertical profiles of the clouds, temperature, and water vapor from the European Center for Medium‐Range Weather Forecasting were used as input for the RTMs. For nonfrozen ocean day and night data, the histograms derived from the calculations by several RTMs at 900 cm−1 have a better than 0.95 correlation with the histogram derived from the AIRS observations, with a bias relative to AIRS of typically less than 2 K. Differences in the cloud physics and cloud overlap assumptions result in little bias between the RTMs, but the standard deviation of the differences ranges from 6 to 12 K. Results at 2,616 cm−1 at night are reasonably consistent with results at 900 cm−1. Except for RTMs which use full scattering calculations, the bias and histogram correlations at 2,616 cm−1 are inferior to those at 900 cm−1 for daytime calculations.
Plain Language Summary
Getting the right clouds of the right type, at the right time and location in Global Circulation Models, is key to getting the local energy balance right. This is key to an accurate forecast. If the clouds are of the wrong type or at the wrong location or time, the accuracy of the forecast is degraded. We evaluate the accuracy of the best currently available cloud description (produced by the European Center for Medium‐Range Weather Forecasting) by comparing the radiances calculated using Radiative Transfer Models (RTMs) from six major development teams to cloudy radiances observed by the Atmospheric Infrared Sounder at the same location and time. The better RTMs fit statistically reasonably well in the 11‐μm atmospheric window area, with little latitude (zonal) and day/night cloud‐type related bias. None of the RTMs fit well in the 4‐μm atmospheric window area during daytime, unless the calculations use full scattering. With the current state of art, all major RTMs would be suitable to start the validation of cloud effects in the National Weather Center models using just one 11‐μm atmospheric window channel.
Key Points
In the 900‐cm−1 atmospheric window channel several Radiative Transfer Models have a better than 0.95 correlation between the histogram derived from the observations and those derived from the calculations
Differences in the bias between observations and calculations for the 2,616‐cm−1 atmospheric window channel are not inconsistent with results at 900 cm−1 if the daytime calculations use full scattering
Differences in the cloud physics and cloud overlap assumptions between Radiative Transfer Models result in a standard deviation of the pairwise difference of between 6 and 12 K; differences due to the cloud overlap assumption alone result in a 3‐K standard deviation</description><identifier>ISSN: 2169-897X</identifier><identifier>EISSN: 2169-8996</identifier><identifier>DOI: 10.1029/2017JD028063</identifier><language>eng</language><publisher>Washington: Blackwell Publishing Ltd</publisher><subject>Accuracy ; Atmospheric Infrared Sounder ; Atmospheric models ; Bias ; climate ; cloud ; Cloud effects ; Cloud physics ; Cloud types ; Clouds ; Correlation ; Data ; Daytime ; Energy balance ; Environmental Sciences ; Evaluation ; Geophysics ; Histograms ; hyperspectral ; Ice clouds ; Information processing ; infrared ; Meteorological satellites ; Night ; Physics ; Profiles ; Radiative transfer ; Radiative transfer models ; Scattering ; Sciences of the Universe ; Temperature ; Vertical distribution ; Vertical profiles ; Water ; Water vapor ; Water vapour ; Weather effects ; Weather forecasting</subject><ispartof>Journal of geophysical research. Atmospheres, 2018-06, Vol.123 (11), p.6142-6157</ispartof><rights>2018. American Geophysical Union. All Rights Reserved.</rights><rights>Copyright</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3797-bcd41539906becf74c9ea52edc40b9f488e75ef90ac6ea76626d0cc0810bae203</citedby><cites>FETCH-LOGICAL-c3797-bcd41539906becf74c9ea52edc40b9f488e75ef90ac6ea76626d0cc0810bae203</cites><orcidid>0000-0001-7514-9473 ; 0000-0002-3259-091X ; 0000-0003-2194-1427 ; 0000-0003-3154-9429 ; 0000-0002-7986-8296 ; 0000-0001-5999-3519 ; 0000-0002-5931-7681 ; 0000-0003-3638-5750 ; 0000-0002-7129-614X ; 0000-0002-4311-7546 ; 0000-0002-0473-3143 ; 0000-0002-1069-5809</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%2F2017JD028063$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1029%2F2017JD028063$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>230,314,776,780,881,1411,1427,27901,27902,45550,45551,46384,46808</link.rule.ids><backlink>$$Uhttps://hal.science/hal-03496946$$DView record in HAL$$Hfree_for_read</backlink></links><search><creatorcontrib>Aumann, Hartmut H.</creatorcontrib><creatorcontrib>Chen, Xiuhong</creatorcontrib><creatorcontrib>Fishbein, Evan</creatorcontrib><creatorcontrib>Geer, Alan</creatorcontrib><creatorcontrib>Havemann, Stephan</creatorcontrib><creatorcontrib>Huang, Xianglei</creatorcontrib><creatorcontrib>Liu, Xu</creatorcontrib><creatorcontrib>Liuzzi, Giuliano</creatorcontrib><creatorcontrib>DeSouza‐Machado, Sergio</creatorcontrib><creatorcontrib>Manning, Evan M.</creatorcontrib><creatorcontrib>Masiello, Guido</creatorcontrib><creatorcontrib>Matricardi, Marco</creatorcontrib><creatorcontrib>Moradi, Isaac</creatorcontrib><creatorcontrib>Natraj, Vijay</creatorcontrib><creatorcontrib>Serio, Carmine</creatorcontrib><creatorcontrib>Strow, Larrabee</creatorcontrib><creatorcontrib>Vidot, Jerome</creatorcontrib><creatorcontrib>Chris Wilson, R.</creatorcontrib><creatorcontrib>Wu, Wan</creatorcontrib><creatorcontrib>Yang, Qiguang</creatorcontrib><creatorcontrib>Yung, Yuk L.</creatorcontrib><title>Evaluation of Radiative Transfer Models With Clouds</title><title>Journal of geophysical research. Atmospheres</title><description>Data from hyperspectral infrared sounders are routinely ingested worldwide by the National Weather Centers. The cloud‐free fraction of this data is used for initializing forecasts which include temperature, water vapor, water cloud, and ice cloud profiles on a global grid. Although the data from these sounders are sensitive to the vertical distribution of ice and liquid water in clouds, this information is not fully utilized. In the future, this information could be used for validating clouds in National Weather Center models and for initializing forecasts. We evaluate how well the calculated radiances from hyperspectral Radiative Transfer Models (RTMs) compare to cloudy radiances observed by AIRS and to one another. Vertical profiles of the clouds, temperature, and water vapor from the European Center for Medium‐Range Weather Forecasting were used as input for the RTMs. For nonfrozen ocean day and night data, the histograms derived from the calculations by several RTMs at 900 cm−1 have a better than 0.95 correlation with the histogram derived from the AIRS observations, with a bias relative to AIRS of typically less than 2 K. Differences in the cloud physics and cloud overlap assumptions result in little bias between the RTMs, but the standard deviation of the differences ranges from 6 to 12 K. Results at 2,616 cm−1 at night are reasonably consistent with results at 900 cm−1. Except for RTMs which use full scattering calculations, the bias and histogram correlations at 2,616 cm−1 are inferior to those at 900 cm−1 for daytime calculations.
Plain Language Summary
Getting the right clouds of the right type, at the right time and location in Global Circulation Models, is key to getting the local energy balance right. This is key to an accurate forecast. If the clouds are of the wrong type or at the wrong location or time, the accuracy of the forecast is degraded. We evaluate the accuracy of the best currently available cloud description (produced by the European Center for Medium‐Range Weather Forecasting) by comparing the radiances calculated using Radiative Transfer Models (RTMs) from six major development teams to cloudy radiances observed by the Atmospheric Infrared Sounder at the same location and time. The better RTMs fit statistically reasonably well in the 11‐μm atmospheric window area, with little latitude (zonal) and day/night cloud‐type related bias. None of the RTMs fit well in the 4‐μm atmospheric window area during daytime, unless the calculations use full scattering. With the current state of art, all major RTMs would be suitable to start the validation of cloud effects in the National Weather Center models using just one 11‐μm atmospheric window channel.
Key Points
In the 900‐cm−1 atmospheric window channel several Radiative Transfer Models have a better than 0.95 correlation between the histogram derived from the observations and those derived from the calculations
Differences in the bias between observations and calculations for the 2,616‐cm−1 atmospheric window channel are not inconsistent with results at 900 cm−1 if the daytime calculations use full scattering
Differences in the cloud physics and cloud overlap assumptions between Radiative Transfer Models result in a standard deviation of the pairwise difference of between 6 and 12 K; differences due to the cloud overlap assumption alone result in a 3‐K standard deviation</description><subject>Accuracy</subject><subject>Atmospheric Infrared Sounder</subject><subject>Atmospheric models</subject><subject>Bias</subject><subject>climate</subject><subject>cloud</subject><subject>Cloud effects</subject><subject>Cloud physics</subject><subject>Cloud types</subject><subject>Clouds</subject><subject>Correlation</subject><subject>Data</subject><subject>Daytime</subject><subject>Energy balance</subject><subject>Environmental Sciences</subject><subject>Evaluation</subject><subject>Geophysics</subject><subject>Histograms</subject><subject>hyperspectral</subject><subject>Ice clouds</subject><subject>Information processing</subject><subject>infrared</subject><subject>Meteorological satellites</subject><subject>Night</subject><subject>Physics</subject><subject>Profiles</subject><subject>Radiative transfer</subject><subject>Radiative transfer models</subject><subject>Scattering</subject><subject>Sciences of the Universe</subject><subject>Temperature</subject><subject>Vertical distribution</subject><subject>Vertical profiles</subject><subject>Water</subject><subject>Water vapor</subject><subject>Water vapour</subject><subject>Weather effects</subject><subject>Weather forecasting</subject><issn>2169-897X</issn><issn>2169-8996</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><recordid>eNp9kEtLAzEUhYMoWGp3_oABV4KjN89JlqWtraUilIruQiaToVPGpiadSv-9U0bElXdzH3wczj0IXWO4x0DUAwGczcdAJAh6hnoEC5VKpcT575y9X6JBjBtoSwJlnPUQnRxM3Zh95beJL5OlKap2ObhkFcw2li4kz75wdUzeqv06GdW-KeIVuihNHd3gp_fR6-NkNZqli5fp02i4SC3NVJbmtmCYU6VA5M6WGbPKGU5cYRnkqmRSuoy7UoGxwplMCCIKsBYkhtw4ArSPbjvdtan1LlQfJhy1N5WeDRf6dGt_UEIxccAte9Oxu-A_Gxf3euObsG3taQJcci44ES1111E2-BiDK39lMehTivpvii1OO_yrqt3xX1bPp8sxZ1xm9BsDc3Dc</recordid><startdate>20180616</startdate><enddate>20180616</enddate><creator>Aumann, Hartmut H.</creator><creator>Chen, Xiuhong</creator><creator>Fishbein, Evan</creator><creator>Geer, Alan</creator><creator>Havemann, Stephan</creator><creator>Huang, Xianglei</creator><creator>Liu, Xu</creator><creator>Liuzzi, Giuliano</creator><creator>DeSouza‐Machado, Sergio</creator><creator>Manning, Evan M.</creator><creator>Masiello, Guido</creator><creator>Matricardi, Marco</creator><creator>Moradi, Isaac</creator><creator>Natraj, Vijay</creator><creator>Serio, Carmine</creator><creator>Strow, Larrabee</creator><creator>Vidot, Jerome</creator><creator>Chris Wilson, R.</creator><creator>Wu, Wan</creator><creator>Yang, Qiguang</creator><creator>Yung, Yuk L.</creator><general>Blackwell Publishing Ltd</general><general>American Geophysical Union</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><scope>1XC</scope><scope>VOOES</scope><orcidid>https://orcid.org/0000-0001-7514-9473</orcidid><orcidid>https://orcid.org/0000-0002-3259-091X</orcidid><orcidid>https://orcid.org/0000-0003-2194-1427</orcidid><orcidid>https://orcid.org/0000-0003-3154-9429</orcidid><orcidid>https://orcid.org/0000-0002-7986-8296</orcidid><orcidid>https://orcid.org/0000-0001-5999-3519</orcidid><orcidid>https://orcid.org/0000-0002-5931-7681</orcidid><orcidid>https://orcid.org/0000-0003-3638-5750</orcidid><orcidid>https://orcid.org/0000-0002-7129-614X</orcidid><orcidid>https://orcid.org/0000-0002-4311-7546</orcidid><orcidid>https://orcid.org/0000-0002-0473-3143</orcidid><orcidid>https://orcid.org/0000-0002-1069-5809</orcidid></search><sort><creationdate>20180616</creationdate><title>Evaluation of Radiative Transfer Models With Clouds</title><author>Aumann, Hartmut H. ; Chen, Xiuhong ; Fishbein, Evan ; Geer, Alan ; Havemann, Stephan ; Huang, Xianglei ; Liu, Xu ; Liuzzi, Giuliano ; DeSouza‐Machado, Sergio ; Manning, Evan M. ; Masiello, Guido ; Matricardi, Marco ; Moradi, Isaac ; Natraj, Vijay ; Serio, Carmine ; Strow, Larrabee ; Vidot, Jerome ; Chris Wilson, R. ; Wu, Wan ; Yang, Qiguang ; Yung, Yuk L.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3797-bcd41539906becf74c9ea52edc40b9f488e75ef90ac6ea76626d0cc0810bae203</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Accuracy</topic><topic>Atmospheric Infrared Sounder</topic><topic>Atmospheric models</topic><topic>Bias</topic><topic>climate</topic><topic>cloud</topic><topic>Cloud effects</topic><topic>Cloud physics</topic><topic>Cloud types</topic><topic>Clouds</topic><topic>Correlation</topic><topic>Data</topic><topic>Daytime</topic><topic>Energy balance</topic><topic>Environmental Sciences</topic><topic>Evaluation</topic><topic>Geophysics</topic><topic>Histograms</topic><topic>hyperspectral</topic><topic>Ice clouds</topic><topic>Information processing</topic><topic>infrared</topic><topic>Meteorological satellites</topic><topic>Night</topic><topic>Physics</topic><topic>Profiles</topic><topic>Radiative transfer</topic><topic>Radiative transfer models</topic><topic>Scattering</topic><topic>Sciences of the Universe</topic><topic>Temperature</topic><topic>Vertical distribution</topic><topic>Vertical profiles</topic><topic>Water</topic><topic>Water vapor</topic><topic>Water vapour</topic><topic>Weather effects</topic><topic>Weather forecasting</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Aumann, Hartmut H.</creatorcontrib><creatorcontrib>Chen, Xiuhong</creatorcontrib><creatorcontrib>Fishbein, Evan</creatorcontrib><creatorcontrib>Geer, Alan</creatorcontrib><creatorcontrib>Havemann, Stephan</creatorcontrib><creatorcontrib>Huang, Xianglei</creatorcontrib><creatorcontrib>Liu, Xu</creatorcontrib><creatorcontrib>Liuzzi, Giuliano</creatorcontrib><creatorcontrib>DeSouza‐Machado, Sergio</creatorcontrib><creatorcontrib>Manning, Evan M.</creatorcontrib><creatorcontrib>Masiello, Guido</creatorcontrib><creatorcontrib>Matricardi, Marco</creatorcontrib><creatorcontrib>Moradi, Isaac</creatorcontrib><creatorcontrib>Natraj, Vijay</creatorcontrib><creatorcontrib>Serio, Carmine</creatorcontrib><creatorcontrib>Strow, Larrabee</creatorcontrib><creatorcontrib>Vidot, Jerome</creatorcontrib><creatorcontrib>Chris Wilson, R.</creatorcontrib><creatorcontrib>Wu, Wan</creatorcontrib><creatorcontrib>Yang, Qiguang</creatorcontrib><creatorcontrib>Yung, Yuk L.</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><collection>Hyper Article en Ligne (HAL)</collection><collection>Hyper Article en Ligne (HAL) (Open Access)</collection><jtitle>Journal of geophysical research. Atmospheres</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Aumann, Hartmut H.</au><au>Chen, Xiuhong</au><au>Fishbein, Evan</au><au>Geer, Alan</au><au>Havemann, Stephan</au><au>Huang, Xianglei</au><au>Liu, Xu</au><au>Liuzzi, Giuliano</au><au>DeSouza‐Machado, Sergio</au><au>Manning, Evan M.</au><au>Masiello, Guido</au><au>Matricardi, Marco</au><au>Moradi, Isaac</au><au>Natraj, Vijay</au><au>Serio, Carmine</au><au>Strow, Larrabee</au><au>Vidot, Jerome</au><au>Chris Wilson, R.</au><au>Wu, Wan</au><au>Yang, Qiguang</au><au>Yung, Yuk L.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Evaluation of Radiative Transfer Models With Clouds</atitle><jtitle>Journal of geophysical research. Atmospheres</jtitle><date>2018-06-16</date><risdate>2018</risdate><volume>123</volume><issue>11</issue><spage>6142</spage><epage>6157</epage><pages>6142-6157</pages><issn>2169-897X</issn><eissn>2169-8996</eissn><abstract>Data from hyperspectral infrared sounders are routinely ingested worldwide by the National Weather Centers. The cloud‐free fraction of this data is used for initializing forecasts which include temperature, water vapor, water cloud, and ice cloud profiles on a global grid. Although the data from these sounders are sensitive to the vertical distribution of ice and liquid water in clouds, this information is not fully utilized. In the future, this information could be used for validating clouds in National Weather Center models and for initializing forecasts. We evaluate how well the calculated radiances from hyperspectral Radiative Transfer Models (RTMs) compare to cloudy radiances observed by AIRS and to one another. Vertical profiles of the clouds, temperature, and water vapor from the European Center for Medium‐Range Weather Forecasting were used as input for the RTMs. For nonfrozen ocean day and night data, the histograms derived from the calculations by several RTMs at 900 cm−1 have a better than 0.95 correlation with the histogram derived from the AIRS observations, with a bias relative to AIRS of typically less than 2 K. Differences in the cloud physics and cloud overlap assumptions result in little bias between the RTMs, but the standard deviation of the differences ranges from 6 to 12 K. Results at 2,616 cm−1 at night are reasonably consistent with results at 900 cm−1. Except for RTMs which use full scattering calculations, the bias and histogram correlations at 2,616 cm−1 are inferior to those at 900 cm−1 for daytime calculations.
Plain Language Summary
Getting the right clouds of the right type, at the right time and location in Global Circulation Models, is key to getting the local energy balance right. This is key to an accurate forecast. If the clouds are of the wrong type or at the wrong location or time, the accuracy of the forecast is degraded. We evaluate the accuracy of the best currently available cloud description (produced by the European Center for Medium‐Range Weather Forecasting) by comparing the radiances calculated using Radiative Transfer Models (RTMs) from six major development teams to cloudy radiances observed by the Atmospheric Infrared Sounder at the same location and time. The better RTMs fit statistically reasonably well in the 11‐μm atmospheric window area, with little latitude (zonal) and day/night cloud‐type related bias. None of the RTMs fit well in the 4‐μm atmospheric window area during daytime, unless the calculations use full scattering. With the current state of art, all major RTMs would be suitable to start the validation of cloud effects in the National Weather Center models using just one 11‐μm atmospheric window channel.
Key Points
In the 900‐cm−1 atmospheric window channel several Radiative Transfer Models have a better than 0.95 correlation between the histogram derived from the observations and those derived from the calculations
Differences in the bias between observations and calculations for the 2,616‐cm−1 atmospheric window channel are not inconsistent with results at 900 cm−1 if the daytime calculations use full scattering
Differences in the cloud physics and cloud overlap assumptions between Radiative Transfer Models result in a standard deviation of the pairwise difference of between 6 and 12 K; differences due to the cloud overlap assumption alone result in a 3‐K standard deviation</abstract><cop>Washington</cop><pub>Blackwell Publishing Ltd</pub><doi>10.1029/2017JD028063</doi><tpages>16</tpages><orcidid>https://orcid.org/0000-0001-7514-9473</orcidid><orcidid>https://orcid.org/0000-0002-3259-091X</orcidid><orcidid>https://orcid.org/0000-0003-2194-1427</orcidid><orcidid>https://orcid.org/0000-0003-3154-9429</orcidid><orcidid>https://orcid.org/0000-0002-7986-8296</orcidid><orcidid>https://orcid.org/0000-0001-5999-3519</orcidid><orcidid>https://orcid.org/0000-0002-5931-7681</orcidid><orcidid>https://orcid.org/0000-0003-3638-5750</orcidid><orcidid>https://orcid.org/0000-0002-7129-614X</orcidid><orcidid>https://orcid.org/0000-0002-4311-7546</orcidid><orcidid>https://orcid.org/0000-0002-0473-3143</orcidid><orcidid>https://orcid.org/0000-0002-1069-5809</orcidid><oa>free_for_read</oa></addata></record> |
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identifier | ISSN: 2169-897X |
ispartof | Journal of geophysical research. Atmospheres, 2018-06, Vol.123 (11), p.6142-6157 |
issn | 2169-897X 2169-8996 |
language | eng |
recordid | cdi_hal_primary_oai_HAL_hal_03496946v1 |
source | Wiley Online Library Journals Frontfile Complete; Wiley Online Library Free Content; Alma/SFX Local Collection |
subjects | Accuracy Atmospheric Infrared Sounder Atmospheric models Bias climate cloud Cloud effects Cloud physics Cloud types Clouds Correlation Data Daytime Energy balance Environmental Sciences Evaluation Geophysics Histograms hyperspectral Ice clouds Information processing infrared Meteorological satellites Night Physics Profiles Radiative transfer Radiative transfer models Scattering Sciences of the Universe Temperature Vertical distribution Vertical profiles Water Water vapor Water vapour Weather effects Weather forecasting |
title | Evaluation of Radiative Transfer Models With Clouds |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-21T18%3A06%3A28IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_hal_p&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Evaluation%20of%20Radiative%20Transfer%20Models%20With%20Clouds&rft.jtitle=Journal%20of%20geophysical%20research.%20Atmospheres&rft.au=Aumann,%20Hartmut%20H.&rft.date=2018-06-16&rft.volume=123&rft.issue=11&rft.spage=6142&rft.epage=6157&rft.pages=6142-6157&rft.issn=2169-897X&rft.eissn=2169-8996&rft_id=info:doi/10.1029/2017JD028063&rft_dat=%3Cproquest_hal_p%3E2058556526%3C/proquest_hal_p%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2058556526&rft_id=info:pmid/&rfr_iscdi=true |