Current practices in weather forecasting usually involve predicting temperature and precipitation over a five to seven day forecast, and on a regional level. However, the information fails to account for short-term weather phenomenon and for changes over smaller geographical areas. To address that need, IBM developed “Deep Thunder,” a weather model that attempts to make forecasting a lot more granular. Now the company wants to deploy it to local governments and businesses that depend on high-resolution forecasts.
Deep Thunder’s forecasting model has a variety of uses. In some cases it can predict flooding and downed power lines up to 84 hours in advance for areas as small as 1 square kilometer. To achieve these predictions with accuracy, Deep Thunder combines weather observations from the National Oceanic and Atmospheric Administration, NASA, the U.S. Geological Survey, WeatherBug, and ground sensors.
A recent Ars Technica article reports that the application could be especially useful for organizations with weather-sensitive needs. For example, in the event of a storm, a power utility could learn what areas to prepare for outages. With that knowledge, they could reduce downtime by scheduling maintenance employees to fix a line they expect to fail.
In 2011, New York City experienced record snowfalls in the month of January. The result was interruptions of public transportation and a shut down to the city’s airports. Utilities did not suffer as much because there was a lack of wind. Also, snow did not stick to the lines, which kept them from being weighed down. But the standard forecast did not provide that level of detail for government and power company officials, so utilities may have been over prepared while the MTA may have been under prepared.
The city of Rio de Janeiro in Brazil deployed Blue Thunder system late in 2010 as they look to improve emergency readiness ahead of the 2014 World Cup and 2016 Olympics. The city is hoping advanced predictions will help avoid the outcome experienced earlier in 2010, when flash floods left over 200 people dead.
In the U.S., IBM recently deployed a test system in the New York City metro area. They even created an iPad app that could tap into Deep Thunder’s data. Although the app is not commercially available, IBM reps used it to demonstrate their technology to legislators in Washington and press in New York.
The computing power behind these high-res forecasts can be considerable. According to the Ars Technica report, the Rio deployment required only a few Power7-based servers hooked up with InfiniBand, but another installation at the University of Brunei Darussalam, which is intended to for national coverage, runs on an IBM Blue Gene supercomputer.
Unfortunately though, the nature of predicting weather is not an exact science. Con Edison tested Deep Thunder over a two-year period and determined in 2011 that the application was not practical at the time.
In the US alone, extreme weather events cost the country tens of billions of dollars and thousands of lives per year. If forecasting can help avert even a fraction of that, it’s money well spent. Current high-resolution forecasting, like Deep Thunder, is still trying to prove its worth, but it represents the kind of technology that many government agencies and businesses would love to have in their arsenal.