Report of the

U.S. GLOBEC Georges Bank

Science Meeting

18 - 20 November 2003, Rhode Island

 


Cover Page

Acknowledgements

Introduction

Narrative

Presentation Abstracts

Poster Presentations

Appendix I: Agenda

Appendix II: List of Participants

Appendix III: List of Planned Publications


Wavelet Transforms of Greene Bomber data for Multiscale Characterization (LA-UR-03-8711)

Fisher, K.E.1 and P.H. Wiebe2
1Los Alamos National Laboratory, Los Alamos, NM 87545
2Biology Department, Woods Hole Oceanographic Institution, Woods Hole, MA 02543
3TBA

The Greene Bomber tow body was towed at 3 meters depth between stations on a number of US GLOBEC broad-scale surveys, providing millions of observations of temperature, salinity, fluorescence, and acoustic backscatter. Our goal is to provide an overview of multiscale analysis, as applied to this alongtrack data for characterization of patch structures. This approach is closely related to the work going on at Los Alamos National Laboratory that aims to provide physics-based characterization of non-linear systems, specifically to compare simulations and observations of stochastic processes. Multiscale approaches require large amounts of data, and characterize patterns statistically; they are limited in scope by the scales of resolution ranging from the separation of observations (minima) to that of the survey (maxima); they provide possible links of pattern to process that must then be carefully assessed using ancillary information. We use wavelet analysis as our initial assessment tool. The wavelet transform localizes contributions to variance for (mono) fractal analysis. The first steps are: 1) use transform values to construct power spectra; 2) use local slope of spectra to get local fractal dimension; and 3) use local fractal dimension to produce stochastic simulation. This approach works for some applications but using variance alone does not take advantage of information contained in higher order moments. Contributions of intermittency are understood by considering increasingly higher order moments, which characterize the power contained in events within the signal that are increasingly rare, and increasingly extreme.

A powerpoint version and an html version of this presentation are available on-line.