東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Dynamic model for space-time weather...

Xu, Gang.

Linked to FindBook

Google Book

Amazon

博客來

Dynamic model for space-time weather radar observation and nowcasting.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Dynamic model for space-time weather radar observation and nowcasting./
Author:	Xu, Gang.
Description:	154 p.
Notes:	Adviser: Venkatachalam Chandrasekar.
Contained By:	Dissertation Abstracts International68-06B.
Subject:	Atmospheric Sciences. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3266342
ISBN:	9780549039426

Dynamic model for space-time weather radar observation and nowcasting.
Xu, Gang.

Dynamic model for space-time weather radar observation and nowcasting. - 154 p.

Adviser: Venkatachalam Chandrasekar.

Thesis (Ph.D.)--Colorado State University, 2007.

A general framework of the dynamic model for space-time radar observations has been developed in the current research. There exist three difficulties in modeling space-time radar observations: (1) high dimensionality due to the high-resolution radar measurements over a large area, (2) non-stationarity due to the storm motion, and (3) nonstationarity due to evolution (growth and decay). These difficulties are addressed in this research. To deal with the storm motion, an efficient radar storm tracking algorithm is developed in the spectral domain. Based on this new technique, the Dynamic and Adaptive Radar Tracking of Storms (DARTS) is developed and evaluated using the synthesized and the observed radar reflectivity. To tackle the high dimensionality and model the spatial variability of radar observations, a general modeling framework is formulated and the singular value decomposition (SVD) is used for dimension reduction. To deal with the dynamic evolution and model the temporal variability of radar observations, the motion-compensated temporal alignment (MCTA) transformation is developed. In this analysis the evolution of radar storm fields is modeled by the linear dynamic system (LDS) in the low-dimensional subspace. The applications of the dynamic model for space-time radar observations are further demonstrated. Spatial and dynamic characteristics are obtained based on the estimated model parameters using three months of radar observations. The characteristic temporal scales are quantified for this dataset. The correlation between the temporal characterization and the spatial characterization of observed radar fields are explored. The simulation capability of different spatiotemporal radar reflectivity fields is demonstrated. Evaluation of the space time variability is particularly important in the context of adaptive scanning of storm systems. The short-term prediction of radar reflectivity fields based on the space-time dynamic model is evaluated using observed radar data. The simulations of the DARTS for real-time applications are also conducted and evaluated.

ISBN: 9780549039426Subjects--Topical Terms:

1019179
Atmospheric Sciences.

Dynamic model for space-time weather radar observation and nowcasting.
LDR:03021nam 2200289 a 45 001 942119
005 20110519
008 110519s2007 ||||||||||||||||| ||eng d
020 $a 9780549039426
035 $a (UMI)AAI3266342
035 $a AAI3266342
040 $a UMI $c UMI
100 1 $a Xu, Gang. $3 1266216
245 1 0 $a Dynamic model for space-time weather radar observation and nowcasting.
300 $a 154 p.
500 $a Adviser: Venkatachalam Chandrasekar.
500 $a Source: Dissertation Abstracts International, Volume: 68-06, Section: B, page: 4043.
502 $a Thesis (Ph.D.)--Colorado State University, 2007.
520 $a A general framework of the dynamic model for space-time radar observations has been developed in the current research. There exist three difficulties in modeling space-time radar observations: (1) high dimensionality due to the high-resolution radar measurements over a large area, (2) non-stationarity due to the storm motion, and (3) nonstationarity due to evolution (growth and decay). These difficulties are addressed in this research. To deal with the storm motion, an efficient radar storm tracking algorithm is developed in the spectral domain. Based on this new technique, the Dynamic and Adaptive Radar Tracking of Storms (DARTS) is developed and evaluated using the synthesized and the observed radar reflectivity. To tackle the high dimensionality and model the spatial variability of radar observations, a general modeling framework is formulated and the singular value decomposition (SVD) is used for dimension reduction. To deal with the dynamic evolution and model the temporal variability of radar observations, the motion-compensated temporal alignment (MCTA) transformation is developed. In this analysis the evolution of radar storm fields is modeled by the linear dynamic system (LDS) in the low-dimensional subspace. The applications of the dynamic model for space-time radar observations are further demonstrated. Spatial and dynamic characteristics are obtained based on the estimated model parameters using three months of radar observations. The characteristic temporal scales are quantified for this dataset. The correlation between the temporal characterization and the spatial characterization of observed radar fields are explored. The simulation capability of different spatiotemporal radar reflectivity fields is demonstrated. Evaluation of the space time variability is particularly important in the context of adaptive scanning of storm systems. The short-term prediction of radar reflectivity fields based on the space-time dynamic model is evaluated using observed radar data. The simulations of the DARTS for real-time applications are also conducted and evaluated.
590 $a School code: 0053.
650 4 $a Atmospheric Sciences. $3 1019179
650 4 $a Computer Science. $3 626642
650 4 $a Engineering, Electronics and Electrical. $3 626636
690 $a 0544
690 $a 0725
690 $a 0984
710 2 $a Colorado State University. $3 675646
773 0 $t Dissertation Abstracts International $g 68-06B.
790 $a 0053
790 1 0 $a Chandrasekar, Venkatachalam, $e advisor
791 $a Ph.D.
792 $a 2007
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3266342