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Covariance Localization in Strongly Coupled Data Assimilation
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Covariance Localization in Strongly Coupled Data Assimilation. |
| 개인저자 | Yoshida, Takuma. |
| 단체저자명 | University of Maryland, College Park. Atmospheric and Oceanic Sciences. |
| 발행사항 | [S.l.]: University of Maryland, College Park., 2019. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2019. |
| 형태사항 | 219 p. |
| 기본자료 저록 | Dissertations Abstracts International 81-04B. Dissertation Abstract International |
| ISBN | 9781687915009 |
| 학위논문주기 | Thesis (Ph.D.)--University of Maryland, College Park, 2019. |
| 일반주기 |
Source: Dissertations Abstracts International, Volume: 81-04, Section: B.
Advisor: Kalnay, Eugenia |
| 이용제한사항 | This item must not be sold to any third party vendors. |
| 요약 | The recent development of accurate coupled models of the Earth system and enhanced computation power have enabled numerical prediction with the coupled models in weather, sub-seasonal, seasonal, and interannual time scales as well as climate projection. In the shorter timescales, the initial condition, or the estimate of the present state of the system, is essential for accurate prediction. Coupled data assimilation (DA) based on an ensemble of forecasts seems to be a promising approach for this state estimate due to its inherent ability to estimate flow-dependent error covariance.Strongly coupled DA tries to incorporate more observations of the other subsystems into an analysis (e.g., ocean observations into the atmospheric analysis) using the coupled error covariances |
| 일반주제명 | Atmospheric sciences. Ocean engineering. Statistics. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
