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Atmospheric Sciences & Global Change
Research Highlights

April 2018

Mastering Clouds: A New Approach for Modeling Convective Storm Systems

Researchers developed a probability-based framework for representing storm clouds and rainfall in climate models.

convective cells
An example radar reflectivity snapshot at 2.5-kilometer height shows the C-band polarimetric (C-POL) radar site at Darwin, Australia. The black dot indicates the site and the red circle marks the approximate 150-kilometer range of the radar. (Hagos et al., 2018)

The Science   

As the resolution of climate and weather models continues to improve, researchers can zoom in and see finer details of cloud processes across space and time. Because clouds are often much smaller than the grid size, existing model representations of intense storm clouds include several assumptions that are not correct for the new generation of models.

Using radar observations and convection-permitting models, scientists at the U.S. Department of Energy's Pacific Northwest National Laboratory led the development of a new probability-based framework for representing convective storm clouds and rainfall in current and future generations of climate models.

The Impact

The proposed framework holds promise for addressing several challenges and biases in climate models related to cloud size, interactions among clouds, and evolution. This could lead to more accurate models representing the inner workings and evolution of convective storm systems, and better predictions of clouds, rainfall, and global circulation.

Summary

Key challenges in the treatment of convective clouds in climate and weather models include representing the size continuum of convective clouds, interactions among clouds, and the evolution of clouds over time. To address these challenges, researchers proposed a novel framework for developing more realistic cloud parameters. Based on the Master Equation, a probability formulation for population dynamics, the framework predicts the growth and decay of the number of convective clouds of a given size.

Under this framework, researchers analyzed observations and used theoretical arguments to build simplified cloud population models. They then evaluated the performance of the simplified models against radar observations and convection-permitting models. The results demonstrated the potential of this probability-based approach to represent the evolution of convective cloud systems, such as internal fluctuations and diurnal cycle.

Future work will involve generalizing this approach to include physical processes such as cold pools and stratiform cloud formation, followed by implementation and testing in a climate model.

Acknowledgments

Sponsors: This research is based on work supported by the U.S. Department of Energy (DOE) Office of Science, Biological and Environmental Research as part of the Atmospheric System Research program.

User Facility: The National Energy Research Scientific Computing Center (NERSC), a DOE Office of Science user facility, provided computing resources for the model simulations.

Research Area: Climate and Earth Systems Science

Research Team: Samson Hagos, Zhe Feng, and Heng Xiao, PNNL; Robert A. Houze Jr., PNNL/University of Washington, and Robert S. Plant, University of Reading (United Kingdom)

Reference: S. Hagos, Z. Feng, R.S. Plant, R.A. Houze Jr., H. Xiao, "A Stochastic Framework for Modeling Population Dynamics of Convective Clouds." Journal of Advances in Modeling Earth Systems 10, 448-465 (2018). [DOI: 10.1002/2017MS001214]


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