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Numerical Weather Prediction


Weather forecasting and prediction of global climate change utilize models of the circulation of the earth's atmosphere. These Global Circulation Models (GCMs) are a system of highly nonlinear partial differential equations that include terms to model the effects of the rotation of the earth, and the interactions of land masses and oceans with the atmosphere.

A common approach to the numerical simulation of solutions to this system of partial differential equations is to use a fixed reference frame (latitude and longitude) and compute the air flow as it passes this fixed frame. The number of data points of these simulations is quite large. For example, to obtain the resolution of 1 degree in latitude and longitude with 10 points in the vertical direction would require more than 10^6 points. Further difficulties occur due to the high speeds in the atmosphere, such as those in the jet stream. Due to these large velocities, it is necessary to take very small increments in time when using such models. A consequence of the large number of data points with the small time steps demands computing resources beyond even the most advanced parallel supercomputers.

This CRSC project involves developing new methods that utilize a moving reference frame (called the Lagrangian formulation) to compute variations in the air along the paths taken by particles in the air. The methods take advantage of some aspects of both the fixed and the Lagrangian frames of reference; thus, they are called Semi-Lagrangian Global Circulation Models. These methods have been demonstrated for weather prediction, and CRSC researchers are currently studying how to modify these methods for climate change modeling, and how to utilize state-of-the art parallel supercomputers for their implementation.

Investigators include J. S. Scroggs and Nelson Settumba (CRSC), F. H. H. Semazzi and George Pouliot (MEAS), and Andy Smith (CS).





 

 

 

 

 

 

 

 

 

 

 

 



 

 

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