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IBM's The Weather Company partners with GoGo for real-time turbulence reports

According to the companies, this marks the first time a non-traditional system on an aircraft will be used to help enhance flight safety.
Written by Natalie Gagliordi, Contributor

IBM-owned The Weather Company is lending its patented Turbulence Auto PIREP System (TAPS) to GoGo Business Aviation in an effort to boost turbulence detection on flights.

In a nutshell, the TAPS system will take data generated by the aircraft and put it through a turbulence-detecting algorithm, which will reside inside software on GoGo's aircraft-based communications server.

GoGo's servers will send the real-time turbulence reports to pilots in the cockpit and aviation meteorologists on the ground for immediate action. According to the companies, this marks the first time a non-traditional system on an aircraft will be used to help enhance flight safety.

Traditionally, turbulence predictions are sent and received via coded verbal reports, known as PIREPS. These contain limited information on flight conditions, The Weather Company said, and it's sometimes difficult to transmit them to pilots due to a lack of cockpit data connectivity.

For The Weather Company, the GoGo partnership reflects the business strategy it adopted since it was acquired by IBM last year: Use The Weather Company's data and sensors in combination with IBM's analytics and Watson platform. Ultimately, this strategy works to bolster Big Blue's Internet of things efforts.

"It is a great example of the Internet of Things in action, where we are collecting massive amounts of data very quickly and then using that insight to provide guidance to all flights that will be traveling through impacted air space," said Mark Gildersleeve, president of business solutions at The Weather Company.

Earlier this week, The Weather Company announced the launch of a hyper-local, short-term custom forecaster called Deep Thunder. The idea is to use a predictive model on historical weather data to train machine learning models, which could help businesses better predict the real-world effects of weather.

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