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IBM leverages machine learning for hyper-local weather

Thor Olavsrud | June 21, 2016
New precision forecasts will help businesses in industries ranging from aviation and agriculture to energy generation and retail better respond to their environment.

It's been just about six months since IBM closed its acquisition of The Weather Company, but it's not resting on its laurels. This week Big Blue moved to leverage The Weather Company's go-to-market strength to launch Deep Thunder, a machine learning-driven weather model developed by IBM Research to help industries ranging from aviation and agriculture to retail better predict the business impact of weather.

"One of the greatest things about being part of IBM is having a relationship with IBM's Research arm," says Mary Glackin, head of Science & Forecast Operations for The Weather Company.

The Weather Company is actually merging its existing Rapid Precision Mesoscale (RPM) model — a numerical weather prediction system based on the Advanced Research Weather Research and Forecast System (WRF-ARW) — with Deep Thunder. RPM generates forecasts up to 24 hours ahead, with updates every three hours in the U.S. and every six hours outside the U.S. Precipitation forecasts are calculated from half-hourly instantaneous precipitation forecasts provided by RPM.

Beyond local forecasts

Deep Thunder brings hyper-local short-term forecasts to the table. Glackin says the combination of Deep Thunder's hyper-local forecasts with The Weather Company's existing global forecast capabilities represents a game-changer.

"What we're announcing is the merger of these two models," she says. "We think that in this case, one plus one really is going to equal three."

Deep Thunder is tuned for forecasts at a 0.2 to 1.2 mile resolution, allowing businesses to understand the exact weather conditions in their location. For example, in aviation, local inclement weather contributes in a direct and measurable way to congestion at airports. With accurate insight into local weather, airlines can better predict congestion to make more precise decisions about exactly how much fuel to put on any given plane.

"So many of their decisions are in the very near term," Glackin says. "Any improvement we can give them in terms of understanding what the convection will be this afternoon is dollars in their pockets. Knowing what fuel load they should put in the airplane because of what the congestion is going to be at LaGuardia at four in the afternoon goes directly to their bottom line."

Getting hyperlocal down on the farm

Another case in point is precision agriculture. Pesticides and fertilizers are sensitive to environmental factors like rain — some need periods of clear weather while others require rain immediately after application. In addition, Glackin says, farmers now want to apply pesticides and fertilizers at very particular points crop lifecycles to maximize yields. Better, more accurate forecasts allow them to hit those windows.

Energy, particularly renewables, are another area where precision weather forecasts can translate directly to the bottom line.


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