Evaluation of Dispersion Forecasts Driven by Atmospheric Model Output at Coarse and Fine Resolution
Lagrangian parcel models are often used to predict the fate of airborne hazardous material releases. The atmospheric input for these integrations is typically supplied by surrounding surface and upper-air observations. However, situations may arise in which observations are unavailable and numerical...
Gespeichert in:
| Veröffentlicht in: | Journal of applied meteorology (1988) 2007-11, Vol.46 (11), p.1967-1980 |
|---|---|
| Hauptverfasser: | , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 1980 |
|---|---|
| container_issue | 11 |
| container_start_page | 1967 |
| container_title | Journal of applied meteorology (1988) |
| container_volume | 46 |
| creator | Nachamkin, Jason E. Cook, John Frost, Mike Martinez, Daniel Sprung, Gary |
| description | Lagrangian parcel models are often used to predict the fate of airborne hazardous material releases. The atmospheric input for these integrations is typically supplied by surrounding surface and upper-air observations. However, situations may arise in which observations are unavailable and numerical model forecasts may be the only source of atmospheric data. In this study, the quality of the atmospheric forecasts for use in dispersion applications is investigated as a function of the horizontal grid spacing of the atmospheric model. The Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS) was used to generate atmospheric forecasts for 14 separate Dipole Pride 26 trials. The simulations consisted of four telescoping one-way nested grids with horizontal spacings of 27, 9, 3, and 1 km, respectively. The 27- and 1-km forecasts were then used as input for dispersion forecasts using the Hazard Prediction Assessment Capability (HPAC) modeling system. The resulting atmospheric and dispersion forecasts were then compared with meteorological and gas-dosage observations collected during Dipole Pride 26. Although the 1-km COAMPS forecasts displayed considerably more detail than those on the 27-km grid, the RMS and bias statistics associated with the atmospheric observations were similar. However, statistics from the HPAC forecasts showed the 1-km atmospheric forcing produced more accurate trajectories than the 27-km output when compared with the dosage measurements. |
| doi_str_mv | 10.1175/2007jamc1570.1 |
| format | Article |
| fullrecord | <record><control><sourceid>jstor_proqu</sourceid><recordid>TN_cdi_proquest_miscellaneous_20448447</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><jstor_id>26172109</jstor_id><sourcerecordid>26172109</sourcerecordid><originalsourceid>FETCH-LOGICAL-c517t-8899a1c83e814133087fea443e6f830893bad5d4150259b8203a147f3cffe2ff3</originalsourceid><addsrcrecordid>eNqNkU1r3DAQhk1JoWnaa28FUWhvm-pjZEnHZTfbJCQESns2s1qJevFarkYO5N_HZkMKvSSn-XrmhZm3qj4Jfi6E0d8l52aPBy-0mTpvqlOhtV1YUPLkOZfwrnpPtOccwBh9WvmLe-xGLG3qWYps3dIQMs3VJuXgkQqxdW7vQ8-2D2xZDomGPyG3nt2mXejY3ViGsTAsbJUwU2DY79im7QP7GSh14yz8oXobsaPw8SmeVb83F79Wl4ubux9Xq-XNwmthysJa51B4q4IVIJTi1sSAACrU0U6VU1vc6R0IzaV2Wyu5QgEmKh9jkDGqs-rbUXfI6e8YqDSHlnzoOuxDGqlRoEFZJV8EJa81QK1eAQLY6ZMT-OU_cJ_G3E_XNlKCMqCcnqDzI-RzIsohNkNuD5gfGsGb2cJmtvB6ebuaLWzEtPD1SRXJYxcz9r6lf1vOaXCinrjPR25PJeXnuayFkYI79QiEzKQV</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>224374395</pqid></control><display><type>article</type><title>Evaluation of Dispersion Forecasts Driven by Atmospheric Model Output at Coarse and Fine Resolution</title><source>American Meteorological Society</source><source>JSTOR Archive Collection A-Z Listing</source><source>EZB-FREE-00999 freely available EZB journals</source><source>Alma/SFX Local Collection</source><creator>Nachamkin, Jason E. ; Cook, John ; Frost, Mike ; Martinez, Daniel ; Sprung, Gary</creator><creatorcontrib>Nachamkin, Jason E. ; Cook, John ; Frost, Mike ; Martinez, Daniel ; Sprung, Gary</creatorcontrib><description>Lagrangian parcel models are often used to predict the fate of airborne hazardous material releases. The atmospheric input for these integrations is typically supplied by surrounding surface and upper-air observations. However, situations may arise in which observations are unavailable and numerical model forecasts may be the only source of atmospheric data. In this study, the quality of the atmospheric forecasts for use in dispersion applications is investigated as a function of the horizontal grid spacing of the atmospheric model. The Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS) was used to generate atmospheric forecasts for 14 separate Dipole Pride 26 trials. The simulations consisted of four telescoping one-way nested grids with horizontal spacings of 27, 9, 3, and 1 km, respectively. The 27- and 1-km forecasts were then used as input for dispersion forecasts using the Hazard Prediction Assessment Capability (HPAC) modeling system. The resulting atmospheric and dispersion forecasts were then compared with meteorological and gas-dosage observations collected during Dipole Pride 26. Although the 1-km COAMPS forecasts displayed considerably more detail than those on the 27-km grid, the RMS and bias statistics associated with the atmospheric observations were similar. However, statistics from the HPAC forecasts showed the 1-km atmospheric forcing produced more accurate trajectories than the 27-km output when compared with the dosage measurements.</description><identifier>ISSN: 1558-8424</identifier><identifier>ISSN: 0894-8763</identifier><identifier>EISSN: 1558-8432</identifier><identifier>EISSN: 1520-0450</identifier><identifier>DOI: 10.1175/2007jamc1570.1</identifier><identifier>CODEN: JOAMEZ</identifier><language>eng</language><publisher>Boston, MA: American Meteorological Society</publisher><subject>Analysis methods ; Applied sciences ; Atmospheric forcing ; Atmospheric models ; Atmospheric pollution ; Atmospherics ; Boundary layer ; Correlations ; Data analysis ; Data assimilation ; Exact sciences and technology ; Experiments ; Forecasting models ; Hazardous materials ; Marine ; Mathematical models ; Meteorology ; Plumes ; Pollutants physicochemistry study: properties, effects, reactions, transport and distribution ; Pollution ; Statistical forecasts ; Statistical weather forecasting ; Studies ; Variables ; Weather ; Weather forecasting ; Wind velocity</subject><ispartof>Journal of applied meteorology (1988), 2007-11, Vol.46 (11), p.1967-1980</ispartof><rights>2007 American Meteorological Society</rights><rights>2008 INIST-CNRS</rights><rights>Copyright American Meteorological Society Nov 2007</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c517t-8899a1c83e814133087fea443e6f830893bad5d4150259b8203a147f3cffe2ff3</citedby><cites>FETCH-LOGICAL-c517t-8899a1c83e814133087fea443e6f830893bad5d4150259b8203a147f3cffe2ff3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.jstor.org/stable/pdf/26172109$$EPDF$$P50$$Gjstor$$H</linktopdf><linktohtml>$$Uhttps://www.jstor.org/stable/26172109$$EHTML$$P50$$Gjstor$$H</linktohtml><link.rule.ids>314,780,784,803,3681,27924,27925,58017,58250</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=19954916$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Nachamkin, Jason E.</creatorcontrib><creatorcontrib>Cook, John</creatorcontrib><creatorcontrib>Frost, Mike</creatorcontrib><creatorcontrib>Martinez, Daniel</creatorcontrib><creatorcontrib>Sprung, Gary</creatorcontrib><title>Evaluation of Dispersion Forecasts Driven by Atmospheric Model Output at Coarse and Fine Resolution</title><title>Journal of applied meteorology (1988)</title><description>Lagrangian parcel models are often used to predict the fate of airborne hazardous material releases. The atmospheric input for these integrations is typically supplied by surrounding surface and upper-air observations. However, situations may arise in which observations are unavailable and numerical model forecasts may be the only source of atmospheric data. In this study, the quality of the atmospheric forecasts for use in dispersion applications is investigated as a function of the horizontal grid spacing of the atmospheric model. The Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS) was used to generate atmospheric forecasts for 14 separate Dipole Pride 26 trials. The simulations consisted of four telescoping one-way nested grids with horizontal spacings of 27, 9, 3, and 1 km, respectively. The 27- and 1-km forecasts were then used as input for dispersion forecasts using the Hazard Prediction Assessment Capability (HPAC) modeling system. The resulting atmospheric and dispersion forecasts were then compared with meteorological and gas-dosage observations collected during Dipole Pride 26. Although the 1-km COAMPS forecasts displayed considerably more detail than those on the 27-km grid, the RMS and bias statistics associated with the atmospheric observations were similar. However, statistics from the HPAC forecasts showed the 1-km atmospheric forcing produced more accurate trajectories than the 27-km output when compared with the dosage measurements.</description><subject>Analysis methods</subject><subject>Applied sciences</subject><subject>Atmospheric forcing</subject><subject>Atmospheric models</subject><subject>Atmospheric pollution</subject><subject>Atmospherics</subject><subject>Boundary layer</subject><subject>Correlations</subject><subject>Data analysis</subject><subject>Data assimilation</subject><subject>Exact sciences and technology</subject><subject>Experiments</subject><subject>Forecasting models</subject><subject>Hazardous materials</subject><subject>Marine</subject><subject>Mathematical models</subject><subject>Meteorology</subject><subject>Plumes</subject><subject>Pollutants physicochemistry study: properties, effects, reactions, transport and distribution</subject><subject>Pollution</subject><subject>Statistical forecasts</subject><subject>Statistical weather forecasting</subject><subject>Studies</subject><subject>Variables</subject><subject>Weather</subject><subject>Weather forecasting</subject><subject>Wind velocity</subject><issn>1558-8424</issn><issn>0894-8763</issn><issn>1558-8432</issn><issn>1520-0450</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2007</creationdate><recordtype>article</recordtype><sourceid>8G5</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>GUQSH</sourceid><sourceid>M2O</sourceid><recordid>eNqNkU1r3DAQhk1JoWnaa28FUWhvm-pjZEnHZTfbJCQESns2s1qJevFarkYO5N_HZkMKvSSn-XrmhZm3qj4Jfi6E0d8l52aPBy-0mTpvqlOhtV1YUPLkOZfwrnpPtOccwBh9WvmLe-xGLG3qWYps3dIQMs3VJuXgkQqxdW7vQ8-2D2xZDomGPyG3nt2mXejY3ViGsTAsbJUwU2DY79im7QP7GSh14yz8oXobsaPw8SmeVb83F79Wl4ubux9Xq-XNwmthysJa51B4q4IVIJTi1sSAACrU0U6VU1vc6R0IzaV2Wyu5QgEmKh9jkDGqs-rbUXfI6e8YqDSHlnzoOuxDGqlRoEFZJV8EJa81QK1eAQLY6ZMT-OU_cJ_G3E_XNlKCMqCcnqDzI-RzIsohNkNuD5gfGsGb2cJmtvB6ebuaLWzEtPD1SRXJYxcz9r6lf1vOaXCinrjPR25PJeXnuayFkYI79QiEzKQV</recordid><startdate>20071101</startdate><enddate>20071101</enddate><creator>Nachamkin, Jason E.</creator><creator>Cook, John</creator><creator>Frost, Mike</creator><creator>Martinez, Daniel</creator><creator>Sprung, Gary</creator><general>American Meteorological Society</general><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7TG</scope><scope>7UA</scope><scope>7XB</scope><scope>88F</scope><scope>88I</scope><scope>8AF</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FK</scope><scope>8G5</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>BKSAR</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>F1W</scope><scope>FR3</scope><scope>GNUQQ</scope><scope>GUQSH</scope><scope>H8D</scope><scope>H96</scope><scope>HCIFZ</scope><scope>KL.</scope><scope>KR7</scope><scope>L.G</scope><scope>L7M</scope><scope>M1Q</scope><scope>M2O</scope><scope>M2P</scope><scope>MBDVC</scope><scope>P5Z</scope><scope>P62</scope><scope>PATMY</scope><scope>PCBAR</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PYCSY</scope><scope>Q9U</scope><scope>R05</scope><scope>S0X</scope><scope>7TN</scope></search><sort><creationdate>20071101</creationdate><title>Evaluation of Dispersion Forecasts Driven by Atmospheric Model Output at Coarse and Fine Resolution</title><author>Nachamkin, Jason E. ; Cook, John ; Frost, Mike ; Martinez, Daniel ; Sprung, Gary</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c517t-8899a1c83e814133087fea443e6f830893bad5d4150259b8203a147f3cffe2ff3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2007</creationdate><topic>Analysis methods</topic><topic>Applied sciences</topic><topic>Atmospheric forcing</topic><topic>Atmospheric models</topic><topic>Atmospheric pollution</topic><topic>Atmospherics</topic><topic>Boundary layer</topic><topic>Correlations</topic><topic>Data analysis</topic><topic>Data assimilation</topic><topic>Exact sciences and technology</topic><topic>Experiments</topic><topic>Forecasting models</topic><topic>Hazardous materials</topic><topic>Marine</topic><topic>Mathematical models</topic><topic>Meteorology</topic><topic>Plumes</topic><topic>Pollutants physicochemistry study: properties, effects, reactions, transport and distribution</topic><topic>Pollution</topic><topic>Statistical forecasts</topic><topic>Statistical weather forecasting</topic><topic>Studies</topic><topic>Variables</topic><topic>Weather</topic><topic>Weather forecasting</topic><topic>Wind velocity</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Nachamkin, Jason E.</creatorcontrib><creatorcontrib>Cook, John</creatorcontrib><creatorcontrib>Frost, Mike</creatorcontrib><creatorcontrib>Martinez, Daniel</creatorcontrib><creatorcontrib>Sprung, Gary</creatorcontrib><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Water Resources Abstracts</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Military Database (Alumni Edition)</collection><collection>Science Database (Alumni Edition)</collection><collection>STEM Database</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Research Library (Alumni Edition)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>eLibrary</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>Earth, Atmospheric & Aquatic Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Engineering Research Database</collection><collection>ProQuest Central Student</collection><collection>Research Library Prep</collection><collection>Aerospace Database</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>SciTech Premium Collection</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>Military Database</collection><collection>Research Library</collection><collection>Science Database</collection><collection>Research Library (Corporate)</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Environmental Science Database</collection><collection>Earth, Atmospheric & Aquatic Science Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Environmental Science Collection</collection><collection>ProQuest Central Basic</collection><collection>University of Michigan</collection><collection>SIRS Editorial</collection><collection>Oceanic Abstracts</collection><jtitle>Journal of applied meteorology (1988)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Nachamkin, Jason E.</au><au>Cook, John</au><au>Frost, Mike</au><au>Martinez, Daniel</au><au>Sprung, Gary</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Evaluation of Dispersion Forecasts Driven by Atmospheric Model Output at Coarse and Fine Resolution</atitle><jtitle>Journal of applied meteorology (1988)</jtitle><date>2007-11-01</date><risdate>2007</risdate><volume>46</volume><issue>11</issue><spage>1967</spage><epage>1980</epage><pages>1967-1980</pages><issn>1558-8424</issn><issn>0894-8763</issn><eissn>1558-8432</eissn><eissn>1520-0450</eissn><coden>JOAMEZ</coden><abstract>Lagrangian parcel models are often used to predict the fate of airborne hazardous material releases. The atmospheric input for these integrations is typically supplied by surrounding surface and upper-air observations. However, situations may arise in which observations are unavailable and numerical model forecasts may be the only source of atmospheric data. In this study, the quality of the atmospheric forecasts for use in dispersion applications is investigated as a function of the horizontal grid spacing of the atmospheric model. The Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS) was used to generate atmospheric forecasts for 14 separate Dipole Pride 26 trials. The simulations consisted of four telescoping one-way nested grids with horizontal spacings of 27, 9, 3, and 1 km, respectively. The 27- and 1-km forecasts were then used as input for dispersion forecasts using the Hazard Prediction Assessment Capability (HPAC) modeling system. The resulting atmospheric and dispersion forecasts were then compared with meteorological and gas-dosage observations collected during Dipole Pride 26. Although the 1-km COAMPS forecasts displayed considerably more detail than those on the 27-km grid, the RMS and bias statistics associated with the atmospheric observations were similar. However, statistics from the HPAC forecasts showed the 1-km atmospheric forcing produced more accurate trajectories than the 27-km output when compared with the dosage measurements.</abstract><cop>Boston, MA</cop><pub>American Meteorological Society</pub><doi>10.1175/2007jamc1570.1</doi><tpages>14</tpages><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1558-8424 |
| ispartof | Journal of applied meteorology (1988), 2007-11, Vol.46 (11), p.1967-1980 |
| issn | 1558-8424 0894-8763 1558-8432 1520-0450 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_20448447 |
| source | American Meteorological Society; JSTOR Archive Collection A-Z Listing; EZB-FREE-00999 freely available EZB journals; Alma/SFX Local Collection |
| subjects | Analysis methods Applied sciences Atmospheric forcing Atmospheric models Atmospheric pollution Atmospherics Boundary layer Correlations Data analysis Data assimilation Exact sciences and technology Experiments Forecasting models Hazardous materials Marine Mathematical models Meteorology Plumes Pollutants physicochemistry study: properties, effects, reactions, transport and distribution Pollution Statistical forecasts Statistical weather forecasting Studies Variables Weather Weather forecasting Wind velocity |
| title | Evaluation of Dispersion Forecasts Driven by Atmospheric Model Output at Coarse and Fine Resolution |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-03T23%3A36%3A10IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-jstor_proqu&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Evaluation%20of%20Dispersion%20Forecasts%20Driven%20by%20Atmospheric%20Model%20Output%20at%20Coarse%20and%20Fine%20Resolution&rft.jtitle=Journal%20of%20applied%20meteorology%20(1988)&rft.au=Nachamkin,%20Jason%20E.&rft.date=2007-11-01&rft.volume=46&rft.issue=11&rft.spage=1967&rft.epage=1980&rft.pages=1967-1980&rft.issn=1558-8424&rft.eissn=1558-8432&rft.coden=JOAMEZ&rft_id=info:doi/10.1175/2007jamc1570.1&rft_dat=%3Cjstor_proqu%3E26172109%3C/jstor_proqu%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=224374395&rft_id=info:pmid/&rft_jstor_id=26172109&rfr_iscdi=true |