Weather service to boost its computer power for forecasting

The National Weather Service is about to boost its computing power by more than tenfold, which officials hope will translate to better forecasts.

The National Oceanic and Atmospheric Administration's two supercomputers will more than triple in computational ability this month and more than triple again by October. Computers will go from now being able to handle 426 trillion operations a second to 5,000 trillion calculations in the fall.

The upgrade costs $44.5 million.

NOAA chief Kathryn Sullivan, in a press release, said the computer boost "will lead to more timely, accurate, and reliable forecasts." The weather service's main computer forecast model this month will double its resolution for forecasts of less than 10 days.

The supercomputers are in Northern Virginia and Orlando, Florida.

After a European computer model predicted the track of 2012's Superstorm Sandy and other storms so well, meteorologists started talking about whether the U.S. was behind Europe on forecasting models, said University of Georgia meteorology professor J. Marshall Shepherd, a former president of the American Meteorological Society.

Now this should help the U.S. catch up, said Shepherd and Jeff Masters, meteorology director of the private company Weather Underground.

Nonetheless, preliminary data shows U.S. computer models beat the European forecasts in 2014, according to NOAA spokesman Chris Vaccaro.

Outsiders praised the increase in computer power.

"In this era of advanced satellite data and increasingly complex weather information, faster computers are essential for providing the type of weather forecasts needed for decision-making that saves lives and property. If our current capacity is a horse and buggy, we just got a Corvette," Shepherd said.


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Jan 05, 2015
The performance of computer models cannot exceed the quality of the data fed into them. The NWS will have to address the various siting issues (such as described at www.surfacestations.org) associated with their data collection stations first, if they wish to improve forecasting quality.

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