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Stochastic parametrization of multiscale processes using a dual-grid approach.
Glenn Shutts1, Thomas Allen, Judith Berner
1Met Office, FitzRoy Road, Exeter EX1 3PB, UK. glen.shutts@metoffice.gov.uk
Summary
This study proposes using computer graphics techniques to improve weather forecast models by simulating sub-gridscale processes. This approach aims for computationally cheap yet realistic simulations for ensemble prediction systems.
Area of Science:
- Atmospheric Science
- Computational Science
- Computer Graphics
Background:
- Current stochastic sub-gridscale parametrization methods in weather forecasting have limitations.
- Integrating detailed physical processes into forecast models is computationally intensive.
Purpose of the Study:
- To explore novel methods for extending stochastic sub-gridscale parametrization.
- To leverage techniques from computer graphics and flow visualization for weather modeling.
Main Methods:
- Emulating sub-filter-scale physical process organization and time evolution on a fine grid.
- Coupling fine-grid tendencies with a coarse-grained forecast model.
- Utilizing computer graphics and visualization software principles for simulation.
Main Results:
- Demonstrated potential for computationally cheap yet realistic simulations.
- Highlighted the importance of algorithmic stability and visual realism over pointwise accuracy.
- Identified technical challenges in coupling fine-grid and coarse-grid models.
Conclusions:
- Computer graphics techniques offer a promising avenue for enhancing weather forecast models.
- The proposed methods could be essential for computationally efficient ensemble prediction.
- Further research is needed to address coupling challenges and optimize simulations.
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