English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Journal Article

Evaluating a stochastic parametrization for a fast-slow system using the Wasserstein distance

MPS-Authors

Vissio ,  G.
IMPRS on Earth System Modelling, MPI for Meteorology, Max Planck Society;
Meteorological Institute, CEN, University of Hamburg;

External Resource
No external resources are shared
Fulltext (restricted access)
There are currently no full texts shared for your IP range.
Fulltext (public)

npg-25-413-2018.pdf
(Publisher version), 6MB

Supplementary Material (public)
There is no public supplementary material available
Citation

Vissio, G., & Lucarini, V. (2018). Evaluating a stochastic parametrization for a fast-slow system using the Wasserstein distance. Nonlinear Processes in Geophysics, 25, 413-427. doi:10.5194/npg-25-413-2018.


Cite as: https://hdl.handle.net/21.11116/0000-0001-9DEF-2
Abstract
Constructing accurate, flexible, and efficient parametrizations is one of the great challenges in the numerical modeling of geophysical fluids. We consider here the simple yet paradigmatic case of a Lorenz 84 model forced by a Lorenz 63 model and derive a parametrization using a recently developed statistical mechanical methodology based on the Ruelle response theory. We derive an expression for the deterministic and the stochastic component of the parametrization and we show that the approach allows for dealing seamlessly with the case of the Lorenz 63 being a fast as well as a slow forcing compared to the characteristic timescales of the Lorenz 84 model. We test our results using both standard metrics based on the moments of the variables of interest as well as Wasserstein distance between the projected measure of the original system on the Lorenz 84 model variables and the measure of the parametrized one. By testing our methods on reduced-phase spaces obtained by projection, we find support for the idea that comparisons based on the Wasserstein distance might be of relevance in many applications despite the curse of dimensionality. © Author(s) 2018.