Toward Stochastic Parameterization Based on Profiler Measurements of Vertical Velocity
Abstract
Parameterizations in General Circulation Models (GCMs) that account for uncertainty due to both unresolved, sub-grid scale processes and errors in assumptions made in the formulation of the parameterization itself are needed to represent the full probability distribution function of resolved processes in the model. In this study, we develop a probabilistic description of vertical velocity based on profiler data collected at Darwin during the time period November 2005 to February 2006. Data collected at one-minute resolution are analyzed at the one-minute, ten-minute and hourly timescales, including fits to the Stochastically-Generated Skew (SGS) distributions. The SGS distributions are associated with linear dynamics, including correlated additive and multiplicative noise. As expected, we find that the stochastic approximation to nonlinear dynamics becomes more appropriate as the timescale is increased by coarse-graining.
- Publication:
-
AGU Fall Meeting Abstracts
- Pub Date:
- December 2016
- Bibcode:
- 2016AGUFMNG31A1829P
- Keywords:
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- 3325 Monte Carlo technique;
- ATMOSPHERIC PROCESSESDE: 3265 Stochastic processes;
- MATHEMATICAL GEOPHYSICSDE: 3275 Uncertainty quantification;
- MATHEMATICAL GEOPHYSICSDE: 4468 Probability distributions;
- heavy and fat-tailed;
- NONLINEAR GEOPHYSICS