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Dependence of the Ice Water Content and Snowfall Rate on Temperature, Globally: Comparison of in Situ Observations, Satellite Active Remote Sensing Retrievals, and Global Climate Model Simulations
Cloud ice microphysical properties measured or estimated from in situ aircraft observations are compared with global climate models and satellite active remote sensor retrievals. Two large datasets, with direct measurements of the ice water content (IWC) and encompassing data from polar to tropical...
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Published in: | Journal of applied meteorology and climatology 2017-01, Vol.56 (1), p.189-215 |
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description | Cloud ice microphysical properties measured or estimated from in situ aircraft observations are compared with global climate models and satellite active remote sensor retrievals. Two large datasets, with direct measurements of the ice water content (IWC) and encompassing data from polar to tropical regions, are combined to yield a large database of in situ measurements. The intention of this study is to identify strengths and weaknesses of the various methods used to derive ice cloud microphysical properties. The in situ data are measured with total water hygrometers, condensed water probes, and particle spectrometers. Data from polar, midlatitude, and tropical locations are included. The satellite data are retrieved from CloudSat/CALIPSO [the CloudSat Ice Cloud Property Product (2C-ICE) and 2C-SNOW-PROFILE] and Global Precipitation Measurement (GPM) Level2A. Although the 2C-ICE retrieval is for IWC, a method to use the IWC to get snowfall rates S is developed. The GPM retrievals are for snowfall rate only. Model results are derived using the Community Atmosphere Model (CAM5) and the Met Office Unified Model [Global Atmosphere 7 (GA7)]. The retrievals and model results are related to the in situ observations using temperature and are partitioned by geographical region. Specific variables compared between the in situ observations, models, and retrievals are the IWC and S. Satellite-retrieved IWCs are reasonably close in value to the in situ observations, whereas the models’ values are relatively low by comparison. Differences between the in situ IWCs and those from the other methods are compounded when S is considered, leading tomodel snowfall rates that are considerably lower than those derived from the in situ data. Anomalous trends with temperature are noted in some instances. |
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Two large datasets, with direct measurements of the ice water content (IWC) and encompassing data from polar to tropical regions, are combined to yield a large database of in situ measurements. The intention of this study is to identify strengths and weaknesses of the various methods used to derive ice cloud microphysical properties. The in situ data are measured with total water hygrometers, condensed water probes, and particle spectrometers. Data from polar, midlatitude, and tropical locations are included. The satellite data are retrieved from CloudSat/CALIPSO [the CloudSat Ice Cloud Property Product (2C-ICE) and 2C-SNOW-PROFILE] and Global Precipitation Measurement (GPM) Level2A. Although the 2C-ICE retrieval is for IWC, a method to use the IWC to get snowfall rates S is developed. The GPM retrievals are for snowfall rate only. Model results are derived using the Community Atmosphere Model (CAM5) and the Met Office Unified Model [Global Atmosphere 7 (GA7)]. The retrievals and model results are related to the in situ observations using temperature and are partitioned by geographical region. Specific variables compared between the in situ observations, models, and retrievals are the IWC and S. Satellite-retrieved IWCs are reasonably close in value to the in situ observations, whereas the models’ values are relatively low by comparison. Differences between the in situ IWCs and those from the other methods are compounded when S is considered, leading tomodel snowfall rates that are considerably lower than those derived from the in situ data. Anomalous trends with temperature are noted in some instances.</description><identifier>ISSN: 1558-8424</identifier><identifier>EISSN: 1558-8432</identifier><identifier>DOI: 10.1175/jamc-d-16-0230.1</identifier><language>eng</language><publisher>Boston: American Meteorological Society</publisher><subject>Airborne observation ; Aircraft ; Aircraft observations ; Atmosphere ; Atmospheric models ; Atmospheric precipitations ; Climate ; Climate models ; Cloud microphysics ; Computer simulation ; Global climate ; Global climate models ; Global precipitation ; Hygrometers ; Hygrometry ; Ice ; Ice clouds ; Identification methods ; In situ measurement ; Measurement ; Meteorological satellites ; Moisture content ; Motivation ; Precipitation ; Probes ; Properties ; Remote sensing ; Remote sensors ; Satellite data ; Satellite observation ; Satellites ; Sensors ; Snow ; Snowfall ; Spectrometers ; Temperature ; Temperature effects ; Tropical climate ; Tropical environments ; Water content ; Yields</subject><ispartof>Journal of applied meteorology and climatology, 2017-01, Vol.56 (1), p.189-215</ispartof><rights>2017 American Meteorological Society</rights><rights>Copyright American Meteorological Society Jan 2017</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c331t-382882ac5e1506d20d451cc79601ceeb358e4c8407d905d99106c4d3a29fb8fc3</citedby><cites>FETCH-LOGICAL-c331t-382882ac5e1506d20d451cc79601ceeb358e4c8407d905d99106c4d3a29fb8fc3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.jstor.org/stable/pdf/26179867$$EPDF$$P50$$Gjstor$$H</linktopdf><linktohtml>$$Uhttps://www.jstor.org/stable/26179867$$EHTML$$P50$$Gjstor$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,58238,58471</link.rule.ids></links><search><creatorcontrib>Heymsfield, Andrew</creatorcontrib><creatorcontrib>Krämer, Martina</creatorcontrib><creatorcontrib>Wood, Norman B.</creatorcontrib><creatorcontrib>Gettelman, Andrew</creatorcontrib><creatorcontrib>Field, Paul R.</creatorcontrib><creatorcontrib>Liu, Guosheng</creatorcontrib><title>Dependence of the Ice Water Content and Snowfall Rate on Temperature, Globally: Comparison of in Situ Observations, Satellite Active Remote Sensing Retrievals, and Global Climate Model Simulations</title><title>Journal of applied meteorology and climatology</title><description>Cloud ice microphysical properties measured or estimated from in situ aircraft observations are compared with global climate models and satellite active remote sensor retrievals. Two large datasets, with direct measurements of the ice water content (IWC) and encompassing data from polar to tropical regions, are combined to yield a large database of in situ measurements. The intention of this study is to identify strengths and weaknesses of the various methods used to derive ice cloud microphysical properties. The in situ data are measured with total water hygrometers, condensed water probes, and particle spectrometers. Data from polar, midlatitude, and tropical locations are included. The satellite data are retrieved from CloudSat/CALIPSO [the CloudSat Ice Cloud Property Product (2C-ICE) and 2C-SNOW-PROFILE] and Global Precipitation Measurement (GPM) Level2A. Although the 2C-ICE retrieval is for IWC, a method to use the IWC to get snowfall rates S is developed. The GPM retrievals are for snowfall rate only. Model results are derived using the Community Atmosphere Model (CAM5) and the Met Office Unified Model [Global Atmosphere 7 (GA7)]. The retrievals and model results are related to the in situ observations using temperature and are partitioned by geographical region. Specific variables compared between the in situ observations, models, and retrievals are the IWC and S. Satellite-retrieved IWCs are reasonably close in value to the in situ observations, whereas the models’ values are relatively low by comparison. Differences between the in situ IWCs and those from the other methods are compounded when S is considered, leading tomodel snowfall rates that are considerably lower than those derived from the in situ data. Anomalous trends with temperature are noted in some instances.</description><subject>Airborne observation</subject><subject>Aircraft</subject><subject>Aircraft observations</subject><subject>Atmosphere</subject><subject>Atmospheric models</subject><subject>Atmospheric precipitations</subject><subject>Climate</subject><subject>Climate models</subject><subject>Cloud microphysics</subject><subject>Computer simulation</subject><subject>Global climate</subject><subject>Global climate models</subject><subject>Global precipitation</subject><subject>Hygrometers</subject><subject>Hygrometry</subject><subject>Ice</subject><subject>Ice clouds</subject><subject>Identification methods</subject><subject>In situ measurement</subject><subject>Measurement</subject><subject>Meteorological satellites</subject><subject>Moisture content</subject><subject>Motivation</subject><subject>Precipitation</subject><subject>Probes</subject><subject>Properties</subject><subject>Remote sensing</subject><subject>Remote sensors</subject><subject>Satellite data</subject><subject>Satellite observation</subject><subject>Satellites</subject><subject>Sensors</subject><subject>Snow</subject><subject>Snowfall</subject><subject>Spectrometers</subject><subject>Temperature</subject><subject>Temperature effects</subject><subject>Tropical climate</subject><subject>Tropical environments</subject><subject>Water 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of the Ice Water Content and Snowfall Rate on Temperature, Globally</title><author>Heymsfield, Andrew ; Krämer, Martina ; Wood, Norman B. ; Gettelman, Andrew ; Field, Paul R. ; Liu, Guosheng</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c331t-382882ac5e1506d20d451cc79601ceeb358e4c8407d905d99106c4d3a29fb8fc3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Airborne observation</topic><topic>Aircraft</topic><topic>Aircraft observations</topic><topic>Atmosphere</topic><topic>Atmospheric models</topic><topic>Atmospheric precipitations</topic><topic>Climate</topic><topic>Climate models</topic><topic>Cloud microphysics</topic><topic>Computer simulation</topic><topic>Global climate</topic><topic>Global climate models</topic><topic>Global precipitation</topic><topic>Hygrometers</topic><topic>Hygrometry</topic><topic>Ice</topic><topic>Ice clouds</topic><topic>Identification methods</topic><topic>In situ measurement</topic><topic>Measurement</topic><topic>Meteorological satellites</topic><topic>Moisture content</topic><topic>Motivation</topic><topic>Precipitation</topic><topic>Probes</topic><topic>Properties</topic><topic>Remote sensing</topic><topic>Remote sensors</topic><topic>Satellite data</topic><topic>Satellite observation</topic><topic>Satellites</topic><topic>Sensors</topic><topic>Snow</topic><topic>Snowfall</topic><topic>Spectrometers</topic><topic>Temperature</topic><topic>Temperature effects</topic><topic>Tropical climate</topic><topic>Tropical environments</topic><topic>Water content</topic><topic>Yields</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Heymsfield, Andrew</creatorcontrib><creatorcontrib>Krämer, Martina</creatorcontrib><creatorcontrib>Wood, Norman B.</creatorcontrib><creatorcontrib>Gettelman, Andrew</creatorcontrib><creatorcontrib>Field, Paul R.</creatorcontrib><creatorcontrib>Liu, 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Basic</collection><collection>University of Michigan</collection><collection>SIRS Editorial</collection><jtitle>Journal of applied meteorology and climatology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Heymsfield, Andrew</au><au>Krämer, Martina</au><au>Wood, Norman B.</au><au>Gettelman, Andrew</au><au>Field, Paul R.</au><au>Liu, Guosheng</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Dependence of the Ice Water Content and Snowfall Rate on Temperature, Globally: Comparison of in Situ Observations, Satellite Active Remote Sensing Retrievals, and Global Climate Model Simulations</atitle><jtitle>Journal of applied meteorology and climatology</jtitle><date>2017-01-01</date><risdate>2017</risdate><volume>56</volume><issue>1</issue><spage>189</spage><epage>215</epage><pages>189-215</pages><issn>1558-8424</issn><eissn>1558-8432</eissn><abstract>Cloud ice microphysical properties measured or estimated from in situ aircraft observations are compared with global climate models and satellite active remote sensor retrievals. Two large datasets, with direct measurements of the ice water content (IWC) and encompassing data from polar to tropical regions, are combined to yield a large database of in situ measurements. The intention of this study is to identify strengths and weaknesses of the various methods used to derive ice cloud microphysical properties. The in situ data are measured with total water hygrometers, condensed water probes, and particle spectrometers. Data from polar, midlatitude, and tropical locations are included. The satellite data are retrieved from CloudSat/CALIPSO [the CloudSat Ice Cloud Property Product (2C-ICE) and 2C-SNOW-PROFILE] and Global Precipitation Measurement (GPM) Level2A. Although the 2C-ICE retrieval is for IWC, a method to use the IWC to get snowfall rates S is developed. The GPM retrievals are for snowfall rate only. Model results are derived using the Community Atmosphere Model (CAM5) and the Met Office Unified Model [Global Atmosphere 7 (GA7)]. The retrievals and model results are related to the in situ observations using temperature and are partitioned by geographical region. Specific variables compared between the in situ observations, models, and retrievals are the IWC and S. Satellite-retrieved IWCs are reasonably close in value to the in situ observations, whereas the models’ values are relatively low by comparison. Differences between the in situ IWCs and those from the other methods are compounded when S is considered, leading tomodel snowfall rates that are considerably lower than those derived from the in situ data. Anomalous trends with temperature are noted in some instances.</abstract><cop>Boston</cop><pub>American Meteorological Society</pub><doi>10.1175/jamc-d-16-0230.1</doi><tpages>27</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Airborne observation Aircraft Aircraft observations Atmosphere Atmospheric models Atmospheric precipitations Climate Climate models Cloud microphysics Computer simulation Global climate Global climate models Global precipitation Hygrometers Hygrometry Ice Ice clouds Identification methods In situ measurement Measurement Meteorological satellites Moisture content Motivation Precipitation Probes Properties Remote sensing Remote sensors Satellite data Satellite observation Satellites Sensors Snow Snowfall Spectrometers Temperature Temperature effects Tropical climate Tropical environments Water content Yields |
title | Dependence of the Ice Water Content and Snowfall Rate on Temperature, Globally: Comparison of in Situ Observations, Satellite Active Remote Sensing Retrievals, and Global Climate Model Simulations |
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