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Strongly Coupled Ocean-Atmosphere Data Assimilation with the Local Ensemble Transform Kalman Filter
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Strongly Coupled Ocean-Atmosphere Data Assimilation with the Local Ensemble Transform Kalman Filter. |
| 개인저자 | Sluka, Travis C. |
| 단체저자명 | University of Maryland, College Park. Atmospheric and Oceanic Sciences. |
| 발행사항 | [S.l.]: University of Maryland, College Park., 2018. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2018. |
| 형태사항 | 165 p. |
| 기본자료 저록 | Dissertations Abstracts International 81-02B. Dissertation Abstract International |
| ISBN | 9781085557573 |
| 학위논문주기 | Thesis (Ph.D.)--University of Maryland, College Park, 2018. |
| 일반주기 |
Source: Dissertations Abstracts International, Volume: 81-02, Section: B.
Advisor: Kalnay, Eugenia. |
| 이용제한사항 | This item must not be sold to any third party vendors. |
| 요약 | Current state-of-the-art coupled data assimilation systems handle the ocean and atmosphere separately when generating an analysis, even though ocean atmosphere models are subsequently run as a coupled system for forecasting. Previous research using simple 1-dimensional coupled models has shown that strongly coupled data assimilation (SCDA), whereby a coupled system is treated as a single entity when creating the analysis, reduces errors for both domains when using an ensemble Kalman filter. A prototype method for SCDA is developed with the local ensemble transform Kalman filter (LETKF). This system is able to use the cross-domain background error covariance from the coupled model ensemble to enable assimilation of atmospheric observations directly into the ocean. This system is tested first with the intermediate complexity SPEEDYNEMO model in an observing system simulation experiment (OSSE), and then with real observations and an operational coupled model, the Climate Forecasting System v2 (CFSv2). Finally, the development of a major upgrade to ocean data assimilation used at NCEP (the Hybrid-GODAS) is presented, and shown how this new system could help present a path forward to operational strongly coupled DA. |
| 일반주제명 | Atmospheric sciences. Physical oceanography. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
