assessment of surface soil temperature products from numerical weather prediction models using ground-based measurements
Surface soil temperature estimates at approximately 0.05 m depth are needed to retrieve soil moisture from the planned Soil Moisture Active Passive (SMAP) L-band (1.4 GHz) satellite. Numerical weather prediction (NWP) systems as operated by various weather centers produce global estimates of soil te...
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| Veröffentlicht in: | Water resources research 2012-02, Vol.48 (2), p.1-n/a |
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| description | Surface soil temperature estimates at approximately 0.05 m depth are needed to retrieve soil moisture from the planned Soil Moisture Active Passive (SMAP) L-band (1.4 GHz) satellite. Numerical weather prediction (NWP) systems as operated by various weather centers produce global estimates of soil temperature. In this study in situ data collected over the state of Oklahoma are used to assess surface (soil) temperature from three NWP systems: (1) the integrated forecast system from the European Center for Medium range Weather Forecasts (ECMWF), (2) the modern-era retrospective analysis for research and applications (MERRA) from the NASA Global Modeling and Assimilation Office, and (3) the global data assimilation system used by the National Center for Environmental Prediction (NCEP). The results are presented by hour of day with specific attention directed to the SMAP early morning overpass time at around 6 A.M. local time, and the period of 1 April to 1 October 2009. It was found that the NWP systems estimate the 0.05 m soil temperature at this time of day with an overall root mean square error of 1.9 to 2.0 K. It is shown that this error can be reduced to 1.6 to 1.8 K when differences between the modeling and measurement depth are accounted for by synchronizing each NWP set to match the mean phase of the in situ data and adjusting the amplitude in accordance with heat flow principles. These results indicate that with little calibration all products meet the SMAP error budget criteria over Oklahoma. |
| doi_str_mv | 10.1029/2011WR010538 |
| format | Article |
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H ; Jackson, Thomas J ; Reichle, Rolf H ; Basara, Jeffrey B</creator><creatorcontrib>Holmes, Thomas R. H ; Jackson, Thomas J ; Reichle, Rolf H ; Basara, Jeffrey B</creatorcontrib><description>Surface soil temperature estimates at approximately 0.05 m depth are needed to retrieve soil moisture from the planned Soil Moisture Active Passive (SMAP) L-band (1.4 GHz) satellite. Numerical weather prediction (NWP) systems as operated by various weather centers produce global estimates of soil temperature. In this study in situ data collected over the state of Oklahoma are used to assess surface (soil) temperature from three NWP systems: (1) the integrated forecast system from the European Center for Medium range Weather Forecasts (ECMWF), (2) the modern-era retrospective analysis for research and applications (MERRA) from the NASA Global Modeling and Assimilation Office, and (3) the global data assimilation system used by the National Center for Environmental Prediction (NCEP). The results are presented by hour of day with specific attention directed to the SMAP early morning overpass time at around 6 A.M. local time, and the period of 1 April to 1 October 2009. It was found that the NWP systems estimate the 0.05 m soil temperature at this time of day with an overall root mean square error of 1.9 to 2.0 K. It is shown that this error can be reduced to 1.6 to 1.8 K when differences between the modeling and measurement depth are accounted for by synchronizing each NWP set to match the mean phase of the in situ data and adjusting the amplitude in accordance with heat flow principles. These results indicate that with little calibration all products meet the SMAP error budget criteria over Oklahoma.</description><identifier>ISSN: 0043-1397</identifier><identifier>EISSN: 1944-7973</identifier><identifier>DOI: 10.1029/2011WR010538</identifier><language>eng</language><publisher>Washington: Blackwell Publishing Ltd</publisher><subject>Accuracy ; Atmospheric boundary layer ; Data collection ; Datasets ; Dielectric properties ; environmental models ; field experimentation ; heat ; Heat flow ; heatflow ; Hydrology ; mathematical models ; microwave radiometers ; model validation ; Moisture content ; Oklahoma ; prediction ; Prediction models ; Radiation ; Remote sensing ; Satellites ; Sensors ; soil depth ; Soil moisture ; soil surface layers ; Soil surfaces ; Soil temperature ; soil water ; soil-atmosphere interactions ; Soils ; spatial data ; Surface temperature ; Temperature ; time series analysis ; weather ; Weather forecasting</subject><ispartof>Water resources research, 2012-02, Vol.48 (2), p.1-n/a</ispartof><rights>Copyright 2012 by the American Geophysical Union</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-a4634-d387fa810a0a3fc1464d5aaeba02798bf5f242936cfca55bbd6e2927aec2fac23</citedby><cites>FETCH-LOGICAL-a4634-d387fa810a0a3fc1464d5aaeba02798bf5f242936cfca55bbd6e2927aec2fac23</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1029%2F2011WR010538$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1029%2F2011WR010538$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,776,780,1411,11493,27901,27902,45550,45551,46443,46867</link.rule.ids></links><search><creatorcontrib>Holmes, Thomas R. H</creatorcontrib><creatorcontrib>Jackson, Thomas J</creatorcontrib><creatorcontrib>Reichle, Rolf H</creatorcontrib><creatorcontrib>Basara, Jeffrey B</creatorcontrib><title>assessment of surface soil temperature products from numerical weather prediction models using ground-based measurements</title><title>Water resources research</title><addtitle>Water Resour. Res</addtitle><description>Surface soil temperature estimates at approximately 0.05 m depth are needed to retrieve soil moisture from the planned Soil Moisture Active Passive (SMAP) L-band (1.4 GHz) satellite. Numerical weather prediction (NWP) systems as operated by various weather centers produce global estimates of soil temperature. In this study in situ data collected over the state of Oklahoma are used to assess surface (soil) temperature from three NWP systems: (1) the integrated forecast system from the European Center for Medium range Weather Forecasts (ECMWF), (2) the modern-era retrospective analysis for research and applications (MERRA) from the NASA Global Modeling and Assimilation Office, and (3) the global data assimilation system used by the National Center for Environmental Prediction (NCEP). The results are presented by hour of day with specific attention directed to the SMAP early morning overpass time at around 6 A.M. local time, and the period of 1 April to 1 October 2009. It was found that the NWP systems estimate the 0.05 m soil temperature at this time of day with an overall root mean square error of 1.9 to 2.0 K. It is shown that this error can be reduced to 1.6 to 1.8 K when differences between the modeling and measurement depth are accounted for by synchronizing each NWP set to match the mean phase of the in situ data and adjusting the amplitude in accordance with heat flow principles. These results indicate that with little calibration all products meet the SMAP error budget criteria over Oklahoma.</description><subject>Accuracy</subject><subject>Atmospheric boundary layer</subject><subject>Data collection</subject><subject>Datasets</subject><subject>Dielectric properties</subject><subject>environmental models</subject><subject>field experimentation</subject><subject>heat</subject><subject>Heat flow</subject><subject>heatflow</subject><subject>Hydrology</subject><subject>mathematical models</subject><subject>microwave radiometers</subject><subject>model validation</subject><subject>Moisture content</subject><subject>Oklahoma</subject><subject>prediction</subject><subject>Prediction models</subject><subject>Radiation</subject><subject>Remote sensing</subject><subject>Satellites</subject><subject>Sensors</subject><subject>soil depth</subject><subject>Soil moisture</subject><subject>soil surface layers</subject><subject>Soil surfaces</subject><subject>Soil temperature</subject><subject>soil water</subject><subject>soil-atmosphere interactions</subject><subject>Soils</subject><subject>spatial data</subject><subject>Surface temperature</subject><subject>Temperature</subject><subject>time series analysis</subject><subject>weather</subject><subject>Weather forecasting</subject><issn>0043-1397</issn><issn>1944-7973</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2012</creationdate><recordtype>article</recordtype><sourceid>8G5</sourceid><sourceid>BENPR</sourceid><sourceid>GUQSH</sourceid><sourceid>M2O</sourceid><recordid>eNp90U1v1DAQBuAIgcSycOOOJS4cSPFHYsdHdkULUvnQQrVHa5KMl5Qk3noStf33eJUKIQ6cfPAzMx6_WfZS8DPBpX0nuRD7HRe8VNWjbCVsUeTGGvU4W3FeqFwoa55mz4iuORdFqc0quwMiJBpwnFjwjObooUFGoevZhMMRI0xzRHaMoZ2biZiPYWDjPGDsGujZLcL0E2O6x7Zrpi6MbAgt9sRm6sYDO8Qwj21eA2HLBoQ0AE_D6Hn2xENP-OLhXGdX5x9-bD_ml18vPm3fX-ZQaFXkraqMh0pw4KB8IwpdtCUA1sClsVXtSy8LaZVufANlWdetRmmlAWxk2kSqdfZm6Zs2uJmRJjd01GDfw4hhJie0VlqYqtKJvv6HXoc5jul1Ln0vV9JawZN6u6gmBqKI3h1jN0C8T-jkrPs7hsTVwm-7Hu__a91-t90JJVJU6yxfqjqa8O5PFcRfThtlSrf_cuHON5-_bfZ85zbJv1q8h-DgEDtyV99T75KfkrayVL8BQsOkog</recordid><startdate>201202</startdate><enddate>201202</enddate><creator>Holmes, Thomas R. 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H</au><au>Jackson, Thomas J</au><au>Reichle, Rolf H</au><au>Basara, Jeffrey B</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>assessment of surface soil temperature products from numerical weather prediction models using ground-based measurements</atitle><jtitle>Water resources research</jtitle><addtitle>Water Resour. Res</addtitle><date>2012-02</date><risdate>2012</risdate><volume>48</volume><issue>2</issue><spage>1</spage><epage>n/a</epage><pages>1-n/a</pages><issn>0043-1397</issn><eissn>1944-7973</eissn><abstract>Surface soil temperature estimates at approximately 0.05 m depth are needed to retrieve soil moisture from the planned Soil Moisture Active Passive (SMAP) L-band (1.4 GHz) satellite. Numerical weather prediction (NWP) systems as operated by various weather centers produce global estimates of soil temperature. In this study in situ data collected over the state of Oklahoma are used to assess surface (soil) temperature from three NWP systems: (1) the integrated forecast system from the European Center for Medium range Weather Forecasts (ECMWF), (2) the modern-era retrospective analysis for research and applications (MERRA) from the NASA Global Modeling and Assimilation Office, and (3) the global data assimilation system used by the National Center for Environmental Prediction (NCEP). The results are presented by hour of day with specific attention directed to the SMAP early morning overpass time at around 6 A.M. local time, and the period of 1 April to 1 October 2009. It was found that the NWP systems estimate the 0.05 m soil temperature at this time of day with an overall root mean square error of 1.9 to 2.0 K. It is shown that this error can be reduced to 1.6 to 1.8 K when differences between the modeling and measurement depth are accounted for by synchronizing each NWP set to match the mean phase of the in situ data and adjusting the amplitude in accordance with heat flow principles. These results indicate that with little calibration all products meet the SMAP error budget criteria over Oklahoma.</abstract><cop>Washington</cop><pub>Blackwell Publishing Ltd</pub><doi>10.1029/2011WR010538</doi><tpages>14</tpages><oa>free_for_read</oa></addata></record> |
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| source | Wiley-Blackwell AGU Digital Library; Wiley Online Library Journals Frontfile Complete; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals |
| subjects | Accuracy Atmospheric boundary layer Data collection Datasets Dielectric properties environmental models field experimentation heat Heat flow heatflow Hydrology mathematical models microwave radiometers model validation Moisture content Oklahoma prediction Prediction models Radiation Remote sensing Satellites Sensors soil depth Soil moisture soil surface layers Soil surfaces Soil temperature soil water soil-atmosphere interactions Soils spatial data Surface temperature Temperature time series analysis weather Weather forecasting |
| title | assessment of surface soil temperature products from numerical weather prediction models using ground-based measurements |
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