HR: 11:05h
AN: NG22A-04    [Abstracts]
TI: Matching physics-based models and data using wavelet-based fractal characterizations
AU: * Fisher, K E
EM: kefisher@lanl.gov
AF: Los Alamos National Laboratory, CCS-2, ISR-2 PO Box 1663 MS B296, Los Alamos, NM 87545 United States
AU: Davis, A B
EM: adavis@lanl.gov
AF: Los Alamos National Laboratory, CCS-2, ISR-2 PO Box 1663 MS B296, Los Alamos, NM 87545 United States
AU: Kamm, J R
EM: kammj@lanl.gov
AF: Los Alamos National Laboratory, CCS-2, ISR-2 PO Box 1663 MS B296, Los Alamos, NM 87545 United States
AB: Characterizing complex physical processes using the wavelet transform provides valuable insight into both the processes themselves, and the efficacy of simulation methods. Analyzing physical systems ranging from experimental systems exhibiting instabilities driven by shock dynamics to global ocean models, we use scaling information derived from 2-D wavelet transforms of images to determine where observations and simulations match, and how patterns evolve in space and time. The variety of fractal and multifractal techniques that are available to us through this wavelet-based approach has been crucial, as we have obtained useful metrics ranging from changes in monofractal (power-law) behavior to changes in higher-order moments of multifractal behavior, and taken full advantage of the spatial resolution to obtain estimates of local Holder characteristics. These metrics are critical to the process of determining where observations and simulations of diverse highly non-linear systems match. Having honed the techniques in repeatable, experimentally driven systems, we are now applying the same approach to comparing geophysical satellite data and simulations from the Global Ocean models run on massively parallel systems at Los Alamos National Laboratory (LANL). In particular, we are interested in how the temporal and spatial grid scales of the model and resolution of the satellite affects matches in dynamic regions of the ocean known to have substantial influence on global carbon fluxes. These methods of deriving physics-based metrics that allow data characterizations to inform simulations are a step toward ultimately improving predictive capability, scale by scale.
DE: 4568 Turbulence, diffusion, and mixing processes
DE: 4572 Upper ocean processes
DE: 1620 Climate dynamics (3309)
DE: 1635 Oceans (4203)
SC: Nonlinear Geophysics [NG]
MN: 2004 AGU Fall Meeting