Applied/ACMS/absF12

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Revision as of 15:45, 13 August 2012 by Jeanluc (talk | contribs) (New page: = ACMS Abstracts: Fall 2012 = === Persi Diaconis (Stanford) === ''Spatial mixing: problems and progress'' One standard way of mixing (cards,domino's,Mahjong tiles) is to 'smoosh' them a...)
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ACMS Abstracts: Fall 2012

Persi Diaconis (Stanford)

Spatial mixing: problems and progress

One standard way of mixing (cards,domino's,Mahjong tiles) is to 'smoosh' them around on the table with two hands. I will introduce some models for this, present data (it's surprisingly effective) and some first theorems. The math involved is related to fluid flow and Baxendale-Harris random homeomorphisims.

Shane Keating (NYU)

Models and measures of turbulent mixing in the ocean

Ocean eddies play a critical role in an wide range of natural processes, from plankton dynamics to climate change. This reinforces the need for a detailed understanding of eddies and their role in transporting heat, carbon, and nutrients throughout the world's oceans. The challenges are significant, however: ocean turbulence is difficult to observe, and numerical models must parameterize subgrid transport, a notoriously difficult problem in inhomogeneous, anisotropic flows dominated by coherent structures such as jets and vortices.

In this talk, I will describe some mathematical approaches to modeling and measuring turbulent mixing in the ocean. First I will outline attempts to quantify uncertainty in satellite estimates of ocean mixing. Next I will describe inexpensive new data assimilation methods for estimating ocean transport that exploit the effect of aliasing to derive "superresolved" velocity fields with a nominal resolution increase of double or more. Finally, I will discuss efforts to develop parameterization schemes for ocean mixing for use in numerical ocean models. These include rigorous approaches based on homogenization theory, as well as adaptive stochastic schemes that efficiently parameterize unresolved scales with a model that can be learned adaptively from observations.