IBM > Case Studies > IBM GRAF from The Weather Company

IBM GRAF from The Weather Company

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 IBM GRAF from The Weather Company - IoT ONE Case Study
Technology Category
  • Analytics & Modeling - Predictive Analytics
  • Data Science Services
The Customer

The Weather Company

About The Customer


The Weather Company is an IBM business – bringing together IBM’s advanced AI and cloud capabilities with The Weather Company’s high volume of weather data. This powerful combination helps people, businesses, and communities around the world prepare for and mitigate the cost of weather. The company offers the most accurate weather forecasts globally–more than 25 billion per day–with personalized and actionable weather data and insights. The Weather Company is committed to trust and transparency, and The Weather Channel app and, as well as Weather Underground and are trusted by hundreds of millions of people to provide accurate, timely forecasts that help them make critical decisions every day. 

The Challenge

The Weather Company, an IBM Business, offers the most accurate forecasts globally – more than 25 billion per day – with personalized and actionable weather data and insights. The Weather Company is challenged to accelerate the computations in weather models in order to provide more precise and accurate forecasts.

Weather forecasting in the United States and other developed nations is more accurate than in other more remote parts of the world.  The Weather Company  saw an opportunity to advance accurate weather predictions globally by leveraging the IBM GRAF.

The Solution

The Weather Company produces more precise and often more accurate weather forecasts by running higher resolution and more computationally intensive weather models on the latest technology that incorporates IBM Power System AC922 equipped with NVIDIA V100 GPUs.

Operational Impact
  • [Data Management - Data Accuracy]

    The Weather Company can make significantly more calculations within the weather model, and thus provide more frequent and accurate weather predictions globally, in locations that typically don’t have access to detailed forecasts.

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