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Data assimilation for dispersion models.

Konda Venkata, Umamaheswara Reddy.

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Data assimilation for dispersion models.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Data assimilation for dispersion models./
Author:	Konda Venkata, Umamaheswara Reddy.
Description:	68 p.
Notes:	Adviser: Tarunraj Singh.
Contained By:	Masters Abstracts International45-06.
Subject:	Atmospheric Sciences. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1444001

Data assimilation for dispersion models.
Konda Venkata, Umamaheswara Reddy.

Data assimilation for dispersion models. - 68 p.

Adviser: Tarunraj Singh.

Thesis (M.S.)--State University of New York at Buffalo, 2007.

The design of an effective data assimilation environment for atmospheric chem-bio dispersion models is considered. Filtering techniques are studied for the purpose of data assimilation in the case of dispersion models. Atmospheric dispersion models usually lead to large scale state space models. The linear Kalman filter theory fails to meet the requirements of such applications due to high dimensionality leading to high computational costs, strong non-linearities, non-Gaussian driving disturbances and model parameter uncertainties. Two dispersion models, increasing in complexity, are developed incorporating some of the main characteristics of the actual dispersion models used in the field. Various sampling based filtering techniques are studied for implementation in the case of these models, focusing on Ensemble filtering and Particle filtering approaches. The potential of sampling based techniques is discussed in the context of variable and high state dimensionality, and nonlinear observation models. The performance of various filters is illustrated.Subjects--Topical Terms:

1019179
Atmospheric Sciences.

Data assimilation for dispersion models.
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035 $a (UMI)AAI1444001
035 $a AAI1444001
040 $a UMI $c UMI
100 1 $a Konda Venkata, Umamaheswara Reddy. $3 1280909
245 1 0 $a Data assimilation for dispersion models.
300 $a 68 p.
500 $a Adviser: Tarunraj Singh.
500 $a Source: Masters Abstracts International, Volume: 45-06, page: 3278.
502 $a Thesis (M.S.)--State University of New York at Buffalo, 2007.
520 $a The design of an effective data assimilation environment for atmospheric chem-bio dispersion models is considered. Filtering techniques are studied for the purpose of data assimilation in the case of dispersion models. Atmospheric dispersion models usually lead to large scale state space models. The linear Kalman filter theory fails to meet the requirements of such applications due to high dimensionality leading to high computational costs, strong non-linearities, non-Gaussian driving disturbances and model parameter uncertainties. Two dispersion models, increasing in complexity, are developed incorporating some of the main characteristics of the actual dispersion models used in the field. Various sampling based filtering techniques are studied for implementation in the case of these models, focusing on Ensemble filtering and Particle filtering approaches. The potential of sampling based techniques is discussed in the context of variable and high state dimensionality, and nonlinear observation models. The performance of various filters is illustrated.
590 $a School code: 0656.
650 4 $a Atmospheric Sciences. $3 1019179
650 4 $a Engineering, Environmental. $3 783782
650 4 $a Engineering, Mechanical. $3 783786
690 $a 0548
690 $a 0725
690 $a 0775
710 2 $a State University of New York at Buffalo. $3 1017814
773 0 $t Masters Abstracts International $g 45-06.
790 $a 0656
790 1 0 $a Singh, Tarunraj, $e advisor
791 $a M.S.
792 $a 2007
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1444001