Theses and Dissertations

Issuing Body

Mississippi State University

Advisor

Moorhead, Robert J.

Committee Member

Fowler, James E.

Committee Member

Jankun-Kelly, T.J.

Committee Member

Luke, Edward A.

Date of Degree

12-15-2012

Document Type

Dissertation - Open Access

Major

Computer Engineering

Degree Name

Doctor of Philosophy

College

James Worth Bagley College of Engineering

Department

Department of Electrical and Computer Engineering

Abstract

Numerical oceanographic models are constantly improving and must be validated when improvements are made. One means of determining how to improve these models and performing validations is to compare model predictions to the future observed outcome, which is measured many ways, including satellite imagery. Comparisons of model forecasts to future satellite images result in error measurements. One common problem with modern oceanographic models is spatial error, i.e., the incorrect placement and shape of ocean features, rendering traditional error metrics such as mean-square and cross-correlation ineffective. Such problems are common in meteorological forecast verification as well, so the application of spatial error metrics have been a recently popular topic in that field of study. Spatial error metrics separate model error into a displacement component and an amplitude component, providing a more reliable assessment of numerical model inaccuracies and a more descriptive portrayal of model prediction skill.The application of spatial error metrics to oceanographic models has been sparse, and significantly further advances exist in the medical imaging and registration field. These advances are presented, along with modifications necessary for application to oceanographic model output and satellite imagery. Standard approaches and options for those methods in the literature are explored, and where the best arrangements of options are unclear, comparison studies are conducted. The first of these trials require the reproduction of synthetic displacements in conjunction with synthetic amplitude perturbations across 480 Navy Coastal Ocean Model (NCOM) temperature fields from various regions of the globe throughout 2009. Results revealed the success of certain approaches novel to both meteorology and oceanography, including B-spline transforms and mutual information. That, combined with other common methods, such as quasi-Newton optimization and land masking, could best recover the synthetic displacements under various synthetic intensity changes. The second set of trials compare temperature fields from NCOM and Navy Layered Ocean Model (NLOM), both 1/16-degree and 1/32-degree, to Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery. Lessons learned from the first trials were applied and extended. The resulting methods algorithmically reproduced portions of a previous hand-analyzed study and were successful in separating spatial from amplitude (temperature) errors.

URI

https://hdl.handle.net/11668/19623

Comments

modeling and simulation||image registration||error analysis

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