Until recently, weather forecasting was a fairly straightforward process. Scientists and meteorologists with a government agency developed computer forecast models, collected data about current weather conditions, input that data into their models, and then ran them on government hardware. A TV forecaster would next review the output of these models and give you the weather during the 6 and 10 o’clock news.

But more recently, the private US weather industry, valued at between $3 billion and $6 billion, has gone far beyond this traditional method of forecasting. Because the National Weather Service is federally funded, the agency makes both the basic code of its model, as well as the raw output, available to both research and commercial entities. Companies have taken the government’s models and “added value” for consumer and business customers.

In late January of this year, IBM finalized its acquisition of The Weather Company, buying all of its assets except for The Weather Channel television network. Both IBM and The Weather Company had been working separately with one of the government’s most popular models, the WRF, or Weather Research and Forecasting Model. Developed in the late 1990s, the WRF is tuned to provide more accurate local forecasts rather than predicting conditions across the globe. (Other companies, such as Panasonic, have developed their own global models based upon the government's code).

IBM had taken the WRF model and its regional weather forecasting ability and coupled that with other models. For example, IBM had layered energy demand models on top of weather forecasts to provide better information for renewable energy trading. The computer company also developed a more sophisticated data assimilation method, allowing it to incorporate all kinds of new variables into its models, like soil moisture.

Meanwhile prior to joining IBM, The Weather Company had focused on developing the ability of the WRF model—it called its proprietary version the Rapid Precision Mesoscale model—to produce forecasts at very high resolution. For example, it ran at a resolution 1km over the northeastern corridor of the United States to support aviation customers. This high resolution also appealed to TV forecasters and other customers seeking to provide “immediate” forecasts during severe weather events.

This week IBM announced that it is combining the best of each of these versions of the government’s WRF forecast system into a single model called Deep Thunder. “To our great pleasure, the two models turned out to be complementary,” Mary Glackin, head of science & forecast operations for The Weather Company, told Ars in an interview.

In the short term, Deep Thunder will serve mostly business customers. One example Glackin offered was the aviation industry. Traditionally aviation forecasts from the government have focused on cloud ceilings and other factors that pilots are concerned about. But airlines are also concerned about saving money, of course. Instead of ceilings, what they really want to know is how much fuel to put on airplanes (less fuel means less weight, which in turn saves money on fuel costs) and how weather will affect congestion at airports. Deep Thunder will use weather forecasts at its core, but its primary goal will be to anticipate future traffic problems and to optimize fueling and dispatch patterns in response. “That’s the kind of value chain we’re focused on,” Glackin said.

Deep Thunder will also have the capability to forecast retrospectively. That is, the computer system will use machine learning to look at past weather events for businesses and better predict how future variations in temperature, precipitation, wind, and other conditions will affect everything from consumer buying patterns to business supply chains. Increasingly, forecasts will not only tell a company about the weather to come but how best to manage its business interests in each community. To offer a very simplistic example: if a major snow storm is possible in two weeks’ time, company A should plan to increase its snow shovel production, company B should increase its shipping capability, and company C should plan a special display for said snow shovels.

All of these AI tools are part of IBM’s plan to continue automating the weather forecasting process. Since the merger five months ago, Glackin said it has proven extremely valuable for The Weather Company to have access to 3,000 scientists and engineers who work for IBM’s research arm, and this process has helped to accelerate the trend toward automation.

“These researchers are working on some really innovative things in the cognitive area,” she said. “Some of them will pan out, and some won’t. Suffice it to say there’s some innovative machine learning work that’s been going on.”