Coupled weather research and forecasting-stochastic time-inverted lagrangian transport (WRF-STILT) model
This paper describes the coupling between a mesoscale numerical weather prediction model, the Weather Research and Forecasting (WRF) model, and a Lagrangian Particle Dispersion Model, the Stochastic Time-Inverted Lagrangian Transport (STILT) model. The primary motivation for developing this coupled...
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| Veröffentlicht in: | Meteorology and atmospheric physics 2010-06, Vol.107 (1-2), p.51-64 |
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| creator | Nehrkorn, Thomas Eluszkiewicz, Janusz Wofsy, Steven C Lin, John C Gerbig, Christoph Longo, Marcos Freitas, Saulo |
| description | This paper describes the coupling between a mesoscale numerical weather prediction model, the Weather Research and Forecasting (WRF) model, and a Lagrangian Particle Dispersion Model, the Stochastic Time-Inverted Lagrangian Transport (STILT) model. The primary motivation for developing this coupled model has been to reduce transport errors in continental-scale top-down estimates of terrestrial greenhouse gas fluxes. Examples of the model's application are shown here for backward trajectory computations originating at CO₂ measurement sites in North America. Owing to its unique features, including meteorological realism and large support base, good mass conservation properties, and a realistic treatment of convection within STILT, the WRF-STILT model offers an attractive tool for a wide range of applications, including inverse flux estimates, flight planning, satellite validation, emergency response and source attribution, air quality, and planetary exploration. |
| doi_str_mv | 10.1007/s00703-010-0068-x |
| format | Article |
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The primary motivation for developing this coupled model has been to reduce transport errors in continental-scale top-down estimates of terrestrial greenhouse gas fluxes. Examples of the model's application are shown here for backward trajectory computations originating at CO₂ measurement sites in North America. Owing to its unique features, including meteorological realism and large support base, good mass conservation properties, and a realistic treatment of convection within STILT, the WRF-STILT model offers an attractive tool for a wide range of applications, including inverse flux estimates, flight planning, satellite validation, emergency response and source attribution, air quality, and planetary exploration.</description><identifier>ISSN: 0177-7971</identifier><identifier>EISSN: 1436-5065</identifier><identifier>DOI: 10.1007/s00703-010-0068-x</identifier><identifier>CODEN: MAPHEU</identifier><language>eng</language><publisher>Vienna: Vienna : Springer Vienna</publisher><subject>Air pollution ; Air quality ; Aquatic Pollution ; Atmospheric Sciences ; Climatology ; Conservation ; Earth and Environmental Science ; Earth Sciences ; Earth, ocean, space ; Emergency preparedness ; Estimates ; Exact sciences and technology ; External geophysics ; Fluxes ; Geophysics. Techniques, methods, instrumentation and models ; Greenhouse gases ; Math. Appl. in Environmental Science ; Mathematical models ; Meteorology ; Original Paper ; Other topics in atmospheric geophysics ; Prediction models ; Stochastic models ; Terrestrial Pollution ; Transport ; Waste Water Technology ; Water Management ; Water Pollution Control ; Weather ; Weather forecasting</subject><ispartof>Meteorology and atmospheric physics, 2010-06, Vol.107 (1-2), p.51-64</ispartof><rights>Springer-Verlag 2010</rights><rights>2015 INIST-CNRS</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c434t-86d8c0438de0a53de580e38a8e53338ed584ffdf790cd984fb0cdcd562da513b3</citedby><cites>FETCH-LOGICAL-c434t-86d8c0438de0a53de580e38a8e53338ed584ffdf790cd984fb0cdcd562da513b3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s00703-010-0068-x$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00703-010-0068-x$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,778,782,27907,27908,41471,42540,51302</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=22839164$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Nehrkorn, Thomas</creatorcontrib><creatorcontrib>Eluszkiewicz, Janusz</creatorcontrib><creatorcontrib>Wofsy, Steven C</creatorcontrib><creatorcontrib>Lin, John C</creatorcontrib><creatorcontrib>Gerbig, Christoph</creatorcontrib><creatorcontrib>Longo, Marcos</creatorcontrib><creatorcontrib>Freitas, Saulo</creatorcontrib><title>Coupled weather research and forecasting-stochastic time-inverted lagrangian transport (WRF-STILT) model</title><title>Meteorology and atmospheric physics</title><addtitle>Meteorol Atmos Phys</addtitle><description>This paper describes the coupling between a mesoscale numerical weather prediction model, the Weather Research and Forecasting (WRF) model, and a Lagrangian Particle Dispersion Model, the Stochastic Time-Inverted Lagrangian Transport (STILT) model. The primary motivation for developing this coupled model has been to reduce transport errors in continental-scale top-down estimates of terrestrial greenhouse gas fluxes. Examples of the model's application are shown here for backward trajectory computations originating at CO₂ measurement sites in North America. Owing to its unique features, including meteorological realism and large support base, good mass conservation properties, and a realistic treatment of convection within STILT, the WRF-STILT model offers an attractive tool for a wide range of applications, including inverse flux estimates, flight planning, satellite validation, emergency response and source attribution, air quality, and planetary exploration.</description><subject>Air pollution</subject><subject>Air quality</subject><subject>Aquatic Pollution</subject><subject>Atmospheric Sciences</subject><subject>Climatology</subject><subject>Conservation</subject><subject>Earth and Environmental Science</subject><subject>Earth Sciences</subject><subject>Earth, ocean, space</subject><subject>Emergency preparedness</subject><subject>Estimates</subject><subject>Exact sciences and technology</subject><subject>External geophysics</subject><subject>Fluxes</subject><subject>Geophysics. Techniques, methods, instrumentation and models</subject><subject>Greenhouse gases</subject><subject>Math. 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The primary motivation for developing this coupled model has been to reduce transport errors in continental-scale top-down estimates of terrestrial greenhouse gas fluxes. Examples of the model's application are shown here for backward trajectory computations originating at CO₂ measurement sites in North America. Owing to its unique features, including meteorological realism and large support base, good mass conservation properties, and a realistic treatment of convection within STILT, the WRF-STILT model offers an attractive tool for a wide range of applications, including inverse flux estimates, flight planning, satellite validation, emergency response and source attribution, air quality, and planetary exploration.</abstract><cop>Vienna</cop><pub>Vienna : Springer Vienna</pub><doi>10.1007/s00703-010-0068-x</doi><tpages>14</tpages></addata></record> |
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| subjects | Air pollution Air quality Aquatic Pollution Atmospheric Sciences Climatology Conservation Earth and Environmental Science Earth Sciences Earth, ocean, space Emergency preparedness Estimates Exact sciences and technology External geophysics Fluxes Geophysics. Techniques, methods, instrumentation and models Greenhouse gases Math. Appl. in Environmental Science Mathematical models Meteorology Original Paper Other topics in atmospheric geophysics Prediction models Stochastic models Terrestrial Pollution Transport Waste Water Technology Water Management Water Pollution Control Weather Weather forecasting |
| title | Coupled weather research and forecasting-stochastic time-inverted lagrangian transport (WRF-STILT) model |
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