Hyperlocal Forecasting

Scripps Institution of Oceanography researcher Masao Kanamitsu is using the Texas Advanced Computing Center's Ranger supercomputer to conduct downscaling experiments, which involves taking the output from the global climate model and adding data at scales smaller than the grid spacing.

"You're given large-scale, coarse-resolution data, and you have to find a way to get the small-scale detail," Kanamitsu says. The researchers are utilizing the technique to create regional weather models for California, by using data about the topography, vegetation, river flow, and other factors.

"We're finding that downscaling works very well, which is amazing because it doesn't use any small-scale observation data," Kanamitsu says. The researchers also are studying how the ocean affects California's regional climate.

"We're trying to simulate the ocean current and temperature in a high resolution ocean model, coupled with a high resolution atmospheric model, to find out what the impact of these small scale ocean states are," he says.

The research has led to breakthroughs in weather prediction models. "Kanamitsu's model simulations have enabled a much better resolved picture of mesoscale processes affecting wind flow and precipitation in the contemporary, historical period in California," says Scripps' Daniel Cayan.

From Texas Advanced Computing Center
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