東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Multivariate error covariance estima...

Borovikov, Anna.

FindBook

Google Book

Amazon

博客來

Multivariate error covariance estimates by Monte Carlo simulation for oceanographic assimilation studies.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Multivariate error covariance estimates by Monte Carlo simulation for oceanographic assimilation studies./
作者:	Borovikov, Anna.
面頁冊數:	123 p.
附註:	Source: Dissertation Abstracts International, Volume: 66-08, Section: B, page: 4251.
Contained By:	Dissertation Abstracts International66-08B.
標題:	Mathematics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3184324
ISBN:	9780542262074

Multivariate error covariance estimates by Monte Carlo simulation for oceanographic assimilation studies.
Borovikov, Anna.

Multivariate error covariance estimates by Monte Carlo simulation for oceanographic assimilation studies. - 123 p.

Source: Dissertation Abstracts International, Volume: 66-08, Section: B, page: 4251.

Thesis (Ph.D.)--University of Maryland, College Park, 2005.

One of the most difficult aspects of ocean state estimation is the prescription of the model forecast error covariances. Simple covariances are usually prescribed, rarely are cross-covariances between different model variables used. A multivariate model of the forecast error covariance is developed for an Optimal Interpolation (OI) assimilation scheme (MvOI) and compared to simpler Gaussian univariate model (UOI).

ISBN: 9780542262074Subjects--Topical Terms:

515831
Mathematics.

Multivariate error covariance estimates by Monte Carlo simulation for oceanographic assimilation studies.
LDR:03437nmm 2200337 4500 001 1822709
005 20061129144931.5
008 130610s2005 eng d
020 $a 9780542262074
035 $a (UnM)AAI3184324
035 $a AAI3184324
040 $a UnM $c UnM
100 1 $a Borovikov, Anna. $3 1911843
245 1 0 $a Multivariate error covariance estimates by Monte Carlo simulation for oceanographic assimilation studies.
300 $a 123 p.
500 $a Source: Dissertation Abstracts International, Volume: 66-08, Section: B, page: 4251.
500 $a Adviser: James A. Carton.
502 $a Thesis (Ph.D.)--University of Maryland, College Park, 2005.
520 $a One of the most difficult aspects of ocean state estimation is the prescription of the model forecast error covariances. Simple covariances are usually prescribed, rarely are cross-covariances between different model variables used. A multivariate model of the forecast error covariance is developed for an Optimal Interpolation (OI) assimilation scheme (MvOI) and compared to simpler Gaussian univariate model (UOI).
520 $a For the MvOI an estimate of the forecast error statistics is made by Monte Carlo techniques from an ensemble of model forecasts. An important advantage of using an ensemble of ocean states is that it provides a natural way to estimate cross-covariances between the fields of different physical variables constituting the model state vector, at the same time incorporating the model's dynamical and thermodynamical constraints. The robustness of the error covariance estimates as well as the analyses has been established by comparing multiple populations of the ensemble.
520 $a Temperature observations from the Tropical Atmosphere-Ocean (TAO) array have been assimilated in this study. Data assimilation experiments are validated with a large independent set of subsurface observations of salinity, zonal velocity and temperature. The performance of the UOI and MvOI is similar in temperature. The salinity and velocity fields are greatly improved in the MvOI, as evident from the analyses of the rms differences between these fields and independent observations. The MvOI assimilation is found to improve upon the control (no assimilation) run in generating water masses with properties close to those observed, while the UOI fails to maintain the temperature-salinity relationship.
520 $a The feasibility of representing a reduced error subspace through empirical orthogonal functions (EOFs) is discussed and a method proposed to substitute the local noise-like variability by a simple model. While computationally efficient, this method produces results only slightly inferior to the MvOI with the full set of EOFs.
520 $a An assimilation scheme with a multivariate forecast error model has the capability to simultaneously process observations of different types. This was tested using temperature data and synthetic salinity observations. The resulting subsurface structures both in temperature and salinity are the closest to the observed, while the currents structure is maintained in dynamically consistent manner.
590 $a School code: 0117.
650 4 $a Mathematics. $3 515831
650 4 $a Physical Oceanography. $3 1019163
650 4 $a Statistics. $3 517247
690 $a 0405
690 $a 0415
690 $a 0463
710 2 0 $a University of Maryland, College Park. $3 657686
773 0 $t Dissertation Abstracts International $g 66-08B.
790 1 0 $a Carton, James A., $e advisor
790 $a 0117
791 $a Ph.D.
792 $a 2005
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3184324