A Comparison of Statistical and Dynamical Downscaling of Winter Precipitation over Complex Terrain
Statistical downscaling is widely used to improve spatial and/or temporal distributions of meteorological variables from regional and global climate models. This downscaling is important because climate models are spatially coarse (50–200 km) and often misrepresent extremes in important meteorologic...
Gespeichert in:
| Veröffentlicht in: | Journal of climate 2012-01, Vol.25 (1), p.262-281 |
|---|---|
| Hauptverfasser: | , , , , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 281 |
|---|---|
| container_issue | 1 |
| container_start_page | 262 |
| container_title | Journal of climate |
| container_volume | 25 |
| creator | Gutmann, Ethan D. Rasmussen, Roy M. Liu, Changhai Ikeda, Kyoko Gochis, David J. Clark, Martyn P. Dudhia, Jimy Thompson, Gregory |
| description | Statistical downscaling is widely used to improve spatial and/or temporal distributions of meteorological variables from regional and global climate models. This downscaling is important because climate models are spatially coarse (50–200 km) and often misrepresent extremes in important meteorological variables, such as temperature and precipitation. However, these downscaling methods rely on current estimates of the spatial distributions of these variables and largely assume that the small-scale spatial distribution will not change significantly in a modified climate. In this study the authors compare data typically used to derive spatial distributions of precipitation [Parameter-Elevation Regressions on Independent Slopes Model (PRISM)] to a high-resolution (2 km) weather model [Weather Research and Forecasting model (WRF)] under the current climate in the mountains of Colorado. It is shown that there are regions of significant difference in November–May precipitation totals (>300 mm) between the two, and possible causes for these differences are discussed. A simple statistical downscaling is then presented that is based on the 2-km WRF data applied to a series of regional climate models [North American Regional Climate Change Assessment Program (NARCCAP)], and the downscaled precipitation data are validated with observations at 65 snow telemetry (SNOTEL) sites throughout Colorado for the winter seasons from 1988 to 2000. The authors also compare statistically downscaled precipitation froma 36-km model under an imposed warming scenario with dynamically downscaled data from a 2-km model using the same forcing data. Although the statistical downscaling improved the domain-average precipitation relative to the original 36-km model, the changes in the spatial pattern of precipitation did not match the changes in the dynamically downscaled 2-km model. This study illustrates some of the uncertainties in applying statistical downscaling to future climate. |
| doi_str_mv | 10.1175/2011jcli4109.1 |
| format | Article |
| fullrecord | <record><control><sourceid>jstor_proqu</sourceid><recordid>TN_cdi_proquest_miscellaneous_923210688</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><jstor_id>26191507</jstor_id><sourcerecordid>26191507</sourcerecordid><originalsourceid>FETCH-LOGICAL-c529t-d2265860c33ee0a42c5bd1194f51e7dc8a3de14902bdc922dcc500d2dc7c8f783</originalsourceid><addsrcrecordid>eNp1kd1LHDEUxYNUcKu-9k0YWopPs96bj5nkUdb6xYKFKn0cspmMZJlNtsmsH_-9E1csCD6d5OZ3DpccQr4hTBFrcUIBcWl6xxHUFHfIBAWFEjinX8gEpOKlrIXYI19TWgIgrQAmZHFazMJqraNLwRehK_4MenBpcEb3hfZtcfbs9er1dhYefRoPzt9n8K_zg43F72iNW7vsygEP4ygH9vapuLUxaucPyG6n-2QP33Sf3J3_up1dlvObi6vZ6bw0gqqhbCmthKzAMGYtaE6NWLSIincCbd0aqVlrkSugi9YoSltjBEA7am1kV0u2T463uesY_m1sGpqVS8b2vfY2bFKjKKMIlczk9w_kMmyiH5cbIWAKeAUj9OMziErkTFJkOFLTLWViSCnarllHt9LxuUFoci1NruV6Nr_KtTTZ8PMtVufP7KL2xqV3FxUVA8HFyB1tuWUaQvz_XqFCATV7AT-vldU</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2814382131</pqid></control><display><type>article</type><title>A Comparison of Statistical and Dynamical Downscaling of Winter Precipitation over Complex Terrain</title><source>Jstor Complete Legacy</source><source>American Meteorological Society</source><source>EZB-FREE-00999 freely available EZB journals</source><creator>Gutmann, Ethan D. ; Rasmussen, Roy M. ; Liu, Changhai ; Ikeda, Kyoko ; Gochis, David J. ; Clark, Martyn P. ; Dudhia, Jimy ; Thompson, Gregory</creator><creatorcontrib>Gutmann, Ethan D. ; Rasmussen, Roy M. ; Liu, Changhai ; Ikeda, Kyoko ; Gochis, David J. ; Clark, Martyn P. ; Dudhia, Jimy ; Thompson, Gregory</creatorcontrib><description>Statistical downscaling is widely used to improve spatial and/or temporal distributions of meteorological variables from regional and global climate models. This downscaling is important because climate models are spatially coarse (50–200 km) and often misrepresent extremes in important meteorological variables, such as temperature and precipitation. However, these downscaling methods rely on current estimates of the spatial distributions of these variables and largely assume that the small-scale spatial distribution will not change significantly in a modified climate. In this study the authors compare data typically used to derive spatial distributions of precipitation [Parameter-Elevation Regressions on Independent Slopes Model (PRISM)] to a high-resolution (2 km) weather model [Weather Research and Forecasting model (WRF)] under the current climate in the mountains of Colorado. It is shown that there are regions of significant difference in November–May precipitation totals (>300 mm) between the two, and possible causes for these differences are discussed. A simple statistical downscaling is then presented that is based on the 2-km WRF data applied to a series of regional climate models [North American Regional Climate Change Assessment Program (NARCCAP)], and the downscaled precipitation data are validated with observations at 65 snow telemetry (SNOTEL) sites throughout Colorado for the winter seasons from 1988 to 2000. The authors also compare statistically downscaled precipitation froma 36-km model under an imposed warming scenario with dynamically downscaled data from a 2-km model using the same forcing data. Although the statistical downscaling improved the domain-average precipitation relative to the original 36-km model, the changes in the spatial pattern of precipitation did not match the changes in the dynamically downscaled 2-km model. This study illustrates some of the uncertainties in applying statistical downscaling to future climate.</description><identifier>ISSN: 0894-8755</identifier><identifier>EISSN: 1520-0442</identifier><identifier>DOI: 10.1175/2011jcli4109.1</identifier><language>eng</language><publisher>Boston, MA: American Meteorological Society</publisher><subject>Atmospheric models ; Boundary conditions ; Climate and weather ; Climate change ; Climate models ; Climatic zones ; Climatology ; Datasets ; Earth, ocean, space ; Environmental assessment ; Exact sciences and technology ; External geophysics ; Future climates ; Global climate ; Global climate models ; Hydrologic data ; Methods ; Modeling ; Modelling ; Mountains ; Precipitation ; Precipitation data ; R&D ; Radiation ; Regional analysis ; Regional climate models ; Regional climates ; Regions ; Research & development ; Simulation ; Spatial distribution ; Spatial models ; Statistics ; Studies ; Telemetry ; Time series ; Topography ; Weather forecasting ; Winter ; Winter precipitation</subject><ispartof>Journal of climate, 2012-01, Vol.25 (1), p.262-281</ispartof><rights>2012 American Meteorological Society</rights><rights>2015 INIST-CNRS</rights><rights>Copyright American Meteorological Society 2012</rights><rights>Copyright American Meteorological Society Jan 1, 2012</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c529t-d2265860c33ee0a42c5bd1194f51e7dc8a3de14902bdc922dcc500d2dc7c8f783</citedby><cites>FETCH-LOGICAL-c529t-d2265860c33ee0a42c5bd1194f51e7dc8a3de14902bdc922dcc500d2dc7c8f783</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.jstor.org/stable/pdf/26191507$$EPDF$$P50$$Gjstor$$H</linktopdf><linktohtml>$$Uhttps://www.jstor.org/stable/26191507$$EHTML$$P50$$Gjstor$$H</linktohtml><link.rule.ids>314,776,780,799,3668,4010,27900,27901,27902,57992,58225</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=25630545$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Gutmann, Ethan D.</creatorcontrib><creatorcontrib>Rasmussen, Roy M.</creatorcontrib><creatorcontrib>Liu, Changhai</creatorcontrib><creatorcontrib>Ikeda, Kyoko</creatorcontrib><creatorcontrib>Gochis, David J.</creatorcontrib><creatorcontrib>Clark, Martyn P.</creatorcontrib><creatorcontrib>Dudhia, Jimy</creatorcontrib><creatorcontrib>Thompson, Gregory</creatorcontrib><title>A Comparison of Statistical and Dynamical Downscaling of Winter Precipitation over Complex Terrain</title><title>Journal of climate</title><description>Statistical downscaling is widely used to improve spatial and/or temporal distributions of meteorological variables from regional and global climate models. This downscaling is important because climate models are spatially coarse (50–200 km) and often misrepresent extremes in important meteorological variables, such as temperature and precipitation. However, these downscaling methods rely on current estimates of the spatial distributions of these variables and largely assume that the small-scale spatial distribution will not change significantly in a modified climate. In this study the authors compare data typically used to derive spatial distributions of precipitation [Parameter-Elevation Regressions on Independent Slopes Model (PRISM)] to a high-resolution (2 km) weather model [Weather Research and Forecasting model (WRF)] under the current climate in the mountains of Colorado. It is shown that there are regions of significant difference in November–May precipitation totals (>300 mm) between the two, and possible causes for these differences are discussed. A simple statistical downscaling is then presented that is based on the 2-km WRF data applied to a series of regional climate models [North American Regional Climate Change Assessment Program (NARCCAP)], and the downscaled precipitation data are validated with observations at 65 snow telemetry (SNOTEL) sites throughout Colorado for the winter seasons from 1988 to 2000. The authors also compare statistically downscaled precipitation froma 36-km model under an imposed warming scenario with dynamically downscaled data from a 2-km model using the same forcing data. Although the statistical downscaling improved the domain-average precipitation relative to the original 36-km model, the changes in the spatial pattern of precipitation did not match the changes in the dynamically downscaled 2-km model. This study illustrates some of the uncertainties in applying statistical downscaling to future climate.</description><subject>Atmospheric models</subject><subject>Boundary conditions</subject><subject>Climate and weather</subject><subject>Climate change</subject><subject>Climate models</subject><subject>Climatic zones</subject><subject>Climatology</subject><subject>Datasets</subject><subject>Earth, ocean, space</subject><subject>Environmental assessment</subject><subject>Exact sciences and technology</subject><subject>External geophysics</subject><subject>Future climates</subject><subject>Global climate</subject><subject>Global climate models</subject><subject>Hydrologic data</subject><subject>Methods</subject><subject>Modeling</subject><subject>Modelling</subject><subject>Mountains</subject><subject>Precipitation</subject><subject>Precipitation data</subject><subject>R&D</subject><subject>Radiation</subject><subject>Regional analysis</subject><subject>Regional climate models</subject><subject>Regional climates</subject><subject>Regions</subject><subject>Research & development</subject><subject>Simulation</subject><subject>Spatial distribution</subject><subject>Spatial models</subject><subject>Statistics</subject><subject>Studies</subject><subject>Telemetry</subject><subject>Time series</subject><subject>Topography</subject><subject>Weather forecasting</subject><subject>Winter</subject><subject>Winter precipitation</subject><issn>0894-8755</issn><issn>1520-0442</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2012</creationdate><recordtype>article</recordtype><sourceid>8G5</sourceid><sourceid>BEC</sourceid><sourceid>BENPR</sourceid><sourceid>GUQSH</sourceid><sourceid>M2O</sourceid><recordid>eNp1kd1LHDEUxYNUcKu-9k0YWopPs96bj5nkUdb6xYKFKn0cspmMZJlNtsmsH_-9E1csCD6d5OZ3DpccQr4hTBFrcUIBcWl6xxHUFHfIBAWFEjinX8gEpOKlrIXYI19TWgIgrQAmZHFazMJqraNLwRehK_4MenBpcEb3hfZtcfbs9er1dhYefRoPzt9n8K_zg43F72iNW7vsygEP4ygH9vapuLUxaucPyG6n-2QP33Sf3J3_up1dlvObi6vZ6bw0gqqhbCmthKzAMGYtaE6NWLSIincCbd0aqVlrkSugi9YoSltjBEA7am1kV0u2T463uesY_m1sGpqVS8b2vfY2bFKjKKMIlczk9w_kMmyiH5cbIWAKeAUj9OMziErkTFJkOFLTLWViSCnarllHt9LxuUFoci1NruV6Nr_KtTTZ8PMtVufP7KL2xqV3FxUVA8HFyB1tuWUaQvz_XqFCATV7AT-vldU</recordid><startdate>20120101</startdate><enddate>20120101</enddate><creator>Gutmann, Ethan D.</creator><creator>Rasmussen, Roy M.</creator><creator>Liu, Changhai</creator><creator>Ikeda, Kyoko</creator><creator>Gochis, David J.</creator><creator>Clark, Martyn P.</creator><creator>Dudhia, Jimy</creator><creator>Thompson, Gregory</creator><general>American Meteorological Society</general><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7QH</scope><scope>7TG</scope><scope>7UA</scope><scope>7X2</scope><scope>7XB</scope><scope>88F</scope><scope>88I</scope><scope>8AF</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FK</scope><scope>8G5</scope><scope>ABUWG</scope><scope>AEUYN</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>GNUQQ</scope><scope>GUQSH</scope><scope>H96</scope><scope>HCIFZ</scope><scope>KL.</scope><scope>L.G</scope><scope>M0K</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>PHGZM</scope><scope>PHGZT</scope><scope>PKEHL</scope><scope>PQEST</scope><scope>PQGLB</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PYCSY</scope><scope>Q9U</scope><scope>S0X</scope></search><sort><creationdate>20120101</creationdate><title>A Comparison of Statistical and Dynamical Downscaling of Winter Precipitation over Complex Terrain</title><author>Gutmann, Ethan D. ; Rasmussen, Roy M. ; Liu, Changhai ; Ikeda, Kyoko ; Gochis, David J. ; Clark, Martyn P. ; Dudhia, Jimy ; Thompson, Gregory</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c529t-d2265860c33ee0a42c5bd1194f51e7dc8a3de14902bdc922dcc500d2dc7c8f783</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2012</creationdate><topic>Atmospheric models</topic><topic>Boundary conditions</topic><topic>Climate and weather</topic><topic>Climate change</topic><topic>Climate models</topic><topic>Climatic zones</topic><topic>Climatology</topic><topic>Datasets</topic><topic>Earth, ocean, space</topic><topic>Environmental assessment</topic><topic>Exact sciences and technology</topic><topic>External geophysics</topic><topic>Future climates</topic><topic>Global climate</topic><topic>Global climate models</topic><topic>Hydrologic data</topic><topic>Methods</topic><topic>Modeling</topic><topic>Modelling</topic><topic>Mountains</topic><topic>Precipitation</topic><topic>Precipitation data</topic><topic>R&D</topic><topic>Radiation</topic><topic>Regional analysis</topic><topic>Regional climate models</topic><topic>Regional climates</topic><topic>Regions</topic><topic>Research & development</topic><topic>Simulation</topic><topic>Spatial distribution</topic><topic>Spatial models</topic><topic>Statistics</topic><topic>Studies</topic><topic>Telemetry</topic><topic>Time series</topic><topic>Topography</topic><topic>Weather forecasting</topic><topic>Winter</topic><topic>Winter precipitation</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Gutmann, Ethan D.</creatorcontrib><creatorcontrib>Rasmussen, Roy M.</creatorcontrib><creatorcontrib>Liu, Changhai</creatorcontrib><creatorcontrib>Ikeda, Kyoko</creatorcontrib><creatorcontrib>Gochis, David J.</creatorcontrib><creatorcontrib>Clark, Martyn P.</creatorcontrib><creatorcontrib>Dudhia, Jimy</creatorcontrib><creatorcontrib>Thompson, Gregory</creatorcontrib><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Aqualine</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Water Resources Abstracts</collection><collection>Agricultural Science Collection</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>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science 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 One Sustainability</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>ProQuest Central Student</collection><collection>Research Library Prep</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>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>Agricultural Science Database</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 Central (New)</collection><collection>ProQuest One Academic (New)</collection><collection>ProQuest One Academic Middle East (New)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Applied & Life Sciences</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Environmental Science Collection</collection><collection>ProQuest Central Basic</collection><collection>SIRS Editorial</collection><jtitle>Journal of climate</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Gutmann, Ethan D.</au><au>Rasmussen, Roy M.</au><au>Liu, Changhai</au><au>Ikeda, Kyoko</au><au>Gochis, David J.</au><au>Clark, Martyn P.</au><au>Dudhia, Jimy</au><au>Thompson, Gregory</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A Comparison of Statistical and Dynamical Downscaling of Winter Precipitation over Complex Terrain</atitle><jtitle>Journal of climate</jtitle><date>2012-01-01</date><risdate>2012</risdate><volume>25</volume><issue>1</issue><spage>262</spage><epage>281</epage><pages>262-281</pages><issn>0894-8755</issn><eissn>1520-0442</eissn><abstract>Statistical downscaling is widely used to improve spatial and/or temporal distributions of meteorological variables from regional and global climate models. This downscaling is important because climate models are spatially coarse (50–200 km) and often misrepresent extremes in important meteorological variables, such as temperature and precipitation. However, these downscaling methods rely on current estimates of the spatial distributions of these variables and largely assume that the small-scale spatial distribution will not change significantly in a modified climate. In this study the authors compare data typically used to derive spatial distributions of precipitation [Parameter-Elevation Regressions on Independent Slopes Model (PRISM)] to a high-resolution (2 km) weather model [Weather Research and Forecasting model (WRF)] under the current climate in the mountains of Colorado. It is shown that there are regions of significant difference in November–May precipitation totals (>300 mm) between the two, and possible causes for these differences are discussed. A simple statistical downscaling is then presented that is based on the 2-km WRF data applied to a series of regional climate models [North American Regional Climate Change Assessment Program (NARCCAP)], and the downscaled precipitation data are validated with observations at 65 snow telemetry (SNOTEL) sites throughout Colorado for the winter seasons from 1988 to 2000. The authors also compare statistically downscaled precipitation froma 36-km model under an imposed warming scenario with dynamically downscaled data from a 2-km model using the same forcing data. Although the statistical downscaling improved the domain-average precipitation relative to the original 36-km model, the changes in the spatial pattern of precipitation did not match the changes in the dynamically downscaled 2-km model. This study illustrates some of the uncertainties in applying statistical downscaling to future climate.</abstract><cop>Boston, MA</cop><pub>American Meteorological Society</pub><doi>10.1175/2011jcli4109.1</doi><tpages>20</tpages><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0894-8755 |
| ispartof | Journal of climate, 2012-01, Vol.25 (1), p.262-281 |
| issn | 0894-8755 1520-0442 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_923210688 |
| source | Jstor Complete Legacy; American Meteorological Society; EZB-FREE-00999 freely available EZB journals |
| subjects | Atmospheric models Boundary conditions Climate and weather Climate change Climate models Climatic zones Climatology Datasets Earth, ocean, space Environmental assessment Exact sciences and technology External geophysics Future climates Global climate Global climate models Hydrologic data Methods Modeling Modelling Mountains Precipitation Precipitation data R&D Radiation Regional analysis Regional climate models Regional climates Regions Research & development Simulation Spatial distribution Spatial models Statistics Studies Telemetry Time series Topography Weather forecasting Winter Winter precipitation |
| title | A Comparison of Statistical and Dynamical Downscaling of Winter Precipitation over Complex Terrain |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-21T19%3A44%3A26IST&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=A%20Comparison%20of%20Statistical%20and%20Dynamical%20Downscaling%20of%20Winter%20Precipitation%20over%20Complex%20Terrain&rft.jtitle=Journal%20of%20climate&rft.au=Gutmann,%20Ethan%20D.&rft.date=2012-01-01&rft.volume=25&rft.issue=1&rft.spage=262&rft.epage=281&rft.pages=262-281&rft.issn=0894-8755&rft.eissn=1520-0442&rft_id=info:doi/10.1175/2011jcli4109.1&rft_dat=%3Cjstor_proqu%3E26191507%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=2814382131&rft_id=info:pmid/&rft_jstor_id=26191507&rfr_iscdi=true |