[Stoch] Stochastic physics parameterization

[Lisa Bengtsson - NOAA Affiliate]

Dear all,

I would like to bring your attention to our recent study published in MWR:

Bengtsson, L., J. Bao, P. Pegion, C. Penland, S. Michelson, and J. Whitaker
: A model framework for stochastic representation of uncertainties
associated with physical processes in NOAA’s Next Generation Global
Prediction System (NGGPS).
<https://journals.ametsoc.org/doi/abs/10.1175/MWR-D-18-0238.1>

Here is the abstract:

In this study we propose a physical process based stochastic
parameterization scheme using cellular automata for NOAA’s next generation
global prediction system. The cellular automata, used to simulate
stochastic processes, such as the production and destruction of sub-grid
convective elements, are conditioned on unresolved vertical motion that
follows a prescribed stochastically generated skewed distribution (SGS).
The SGS is described by a stochastic differential equation and linked to
observations by taking into account the first four moments from an observed
data-set. In the proposed parameterization framework, we emphasize the need
for a dynamical memory term to be included in physical process based
stochastic parameterizations, and we illustrate the requirement for the
dynamical memory using the Mori-Zwanzig formalism. Although this paper
focuses on the methodology, early results indicate that if we apply our
stochastic framework to deep cumulus convection, it is found that the
frequency distribution of precipitation is improved in a single member
stochastic forecast, and that some improved spread/skill relationship in
ensemble runs can be found in state-variables in the tropics, as well as
sub-tropics.

Kind regards,
Lisa
---------------------------------
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Lisa Bengtsson
Phone : 1 (303) 497 5971
Research Scientist - Meteorologist
University of Colorado/NOAA Earth
System Research Lab
Email: lisa.bengtsson at noaa.gov
325 Broadway
Office: Skaggs Research Cntr 3B-307
Boulder, CO, USA 80303-3328
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