A Space-Time Multiscale Analysis System: A Sequential Variational Analysis Approach
As new observation systems are developed and deployed, new and presumably more precise information is becoming available for weather forecasting and climate monitoring. To take advantage of these new observations, it is desirable to have schemes to accurately retrieve the information before statisti...
| Үндсэн зохиолчид: | , , , , , , |
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| Бусад зохиолчид: | |
| Формат: | Өгүүллэг |
| Хэл сонгох: | en_US |
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American Meteorological Society
2011
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| Онлайн хандалт: | http://hdl.handle.net/1721.1/66998 |
| _version_ | 1826199091908968448 |
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| author | Xie, Y. Koch, S. McGinley, J. Albers, S. Bieringer, P. E. Wolfson, Marilyn M. Chan, Michael T. |
| author2 | Lincoln Laboratory |
| author_facet | Lincoln Laboratory Xie, Y. Koch, S. McGinley, J. Albers, S. Bieringer, P. E. Wolfson, Marilyn M. Chan, Michael T. |
| author_sort | Xie, Y. |
| collection | MIT |
| description | As new observation systems are developed and deployed, new and presumably more precise information is becoming available for weather forecasting and climate monitoring. To take advantage of these new observations, it is desirable to have schemes to accurately retrieve the information before statistical analyses are performed so that statistical computation can be more effectively used where it is needed most. The authors propose a sequential variational approach that possesses advantages of both a standard statistical analysis [such as with a three-dimensional variational data assimilation (3DVAR) or Kalman filter] and a traditional objective analysis (such as the Barnes analysis). The sequential variational analysis is multiscale, inhomogeneous, anisotropic, and temporally consistent, as shown by an idealized test case and observational datasets in this study. The real data cases include applications in two-dimensional and three-dimensional space and time for storm outflow boundary detection (surface application) and hurricane data assimilation (three-dimensional space application). Implemented using a multigrid technique, this sequential variational approach is a very efficient data assimilation method. |
| first_indexed | 2024-09-23T11:14:16Z |
| format | Article |
| id | mit-1721.1/66998 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T11:14:16Z |
| publishDate | 2011 |
| publisher | American Meteorological Society |
| record_format | dspace |
| spelling | mit-1721.1/669982022-09-27T18:04:42Z A Space-Time Multiscale Analysis System: A Sequential Variational Analysis Approach Xie, Y. Koch, S. McGinley, J. Albers, S. Bieringer, P. E. Wolfson, Marilyn M. Chan, Michael T. Lincoln Laboratory Wolfson, Marilyn M. Wolfson, Marilyn M. Chan, Michael T. As new observation systems are developed and deployed, new and presumably more precise information is becoming available for weather forecasting and climate monitoring. To take advantage of these new observations, it is desirable to have schemes to accurately retrieve the information before statistical analyses are performed so that statistical computation can be more effectively used where it is needed most. The authors propose a sequential variational approach that possesses advantages of both a standard statistical analysis [such as with a three-dimensional variational data assimilation (3DVAR) or Kalman filter] and a traditional objective analysis (such as the Barnes analysis). The sequential variational analysis is multiscale, inhomogeneous, anisotropic, and temporally consistent, as shown by an idealized test case and observational datasets in this study. The real data cases include applications in two-dimensional and three-dimensional space and time for storm outflow boundary detection (surface application) and hurricane data assimilation (three-dimensional space application). Implemented using a multigrid technique, this sequential variational approach is a very efficient data assimilation method. 2011-11-10T15:05:02Z 2011-11-10T15:05:02Z 2011-04 2010-11 Article http://purl.org/eprint/type/JournalArticle 0027-0644 1520-0493 http://hdl.handle.net/1721.1/66998 Xie, Y., S. Koch, J. McGinley, S. Albers, P. E. Bieringer, M. Wolfson, M. Chan, 2011: A Space–Time Multiscale Analysis System: A Sequential Variational Analysis Approach. Mon. Wea. Rev., 139, 1224–1240.© 2011 American Meteorological Society. en_US http://dx.doi.org/10.1175/2010mwr3338.1 Monthly Weather Review Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. application/pdf American Meteorological Society AMS |
| spellingShingle | Xie, Y. Koch, S. McGinley, J. Albers, S. Bieringer, P. E. Wolfson, Marilyn M. Chan, Michael T. A Space-Time Multiscale Analysis System: A Sequential Variational Analysis Approach |
| title | A Space-Time Multiscale Analysis System: A Sequential Variational Analysis Approach |
| title_full | A Space-Time Multiscale Analysis System: A Sequential Variational Analysis Approach |
| title_fullStr | A Space-Time Multiscale Analysis System: A Sequential Variational Analysis Approach |
| title_full_unstemmed | A Space-Time Multiscale Analysis System: A Sequential Variational Analysis Approach |
| title_short | A Space-Time Multiscale Analysis System: A Sequential Variational Analysis Approach |
| title_sort | space time multiscale analysis system a sequential variational analysis approach |
| url | http://hdl.handle.net/1721.1/66998 |
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