[GTP] GTP Seminar at NCAR- Ian Grooms, CIMS

[Silvia Gentile]

Dear All,
This session will be webcast and recorded
The link will be http://www.fin.ucar.edu/it/mms/ml-live.htm
Thank you,

Silvia Gentile
NCAR IMAGe
1850 Table Mesa Drive
Boulder, CO 80305
303 497 2480
www2.image.ucar.edu

On 6/11/2013 12:18 PM, Silvia Gentile wrote:
> GTP Seminar
> STOCHASTIC SUPERPARAMETERIZATION
> Ian Grooms
> Center for Atmosphere Ocean Science
> Courant Institute of Mathematical Sciences, New York University
> Monday, June 24, 2013
> NCAR - Mesa Laboratory, Main Seminar Room
> Lecture at 2:30pm
> This seminar may be recorded and webcast, TBD
>
> Efficient modeling of unresolved small-scale turbulence is of primary 
> importance in simulations of large-scale geophysical fluid dynamics. 
> In many geophysical and astrophysical settings the unresolved 
> turbulence is not homogeneous/isotropic, being affected by rotation, 
> stratification, moist processes, magnetism, etc., and the multiscale 
> interactions with the resolved large scales are complex and consist of 
> more than inertial-range energy transfer. Furthermore, small-scale 
> feedback to the resolved scales is not completely determined by the 
> resolved large scales. The random nature of the small scales requires 
> stochastic models, which in turn can improve the robustness of 
> ensemble-based prediction and state estimation algorithms.
> Superparameterization is a multiscale framework that models unresolved 
> scales by PDEs evolving on pseudo-physical domains embedded into the 
> coarse grid of a general circulation model. Although the small-scale 
> PDEs are deterministic, their chaotic/turbulent dynamics generate an 
> effectively stochastic feedback to the large scales. Though successful 
> in modeling tropical atmospheric moist convection, 
> superparameterization remains computationally costly, and of limited 
> generality.
> We develop an improved framework for superparameterization that models 
> the small-scale turbulent dynamics by stochastic, quasilinear PDEs 
> rather than nonlinear, deterministic ones. This greatly improves the 
> efficiency of the algorithm, and our mathematical framework for 
> developing the large- and small-scale PDEs increases the generality of 
> superparameterization. The resulting algorithm is developed and tested 
> in two idealized turbulent models: the one-dimensional complex-scalar 
> MMT equation, and two-layer quasigeostrophic turbulence. In both 
> settings the algorithm achieves several orders of magnitude of 
> reduction in computational cost compared to direct simulation of all 
> scales, and produces qualitatively accurate results. This is 
> particularly impressive in the quasigeostrophic tests where the 
> algorithm successfully parameterizes the inverse cascade of kinetic 
> energy from unresolved to resolved scales. Future directions include 
> more realistic applications, and optimization of the numerical 
> algorithm."
>
>
>