Deep Thunder

IBM's high-resolution weather forecasting and modeling technology — called Deep Thunder — provides a predictive capability to map approaching weather events and model the anticipated impact. The system applies mathematical algorithms to understand the interaction of the atmosphere with the surface of the earth.

Detailed risk assessments are developed using data from soil saturation levels, rates and flow of water runoff, the region's topography, as well as historical rainfall and flood records. Using historical data, sophisticated analytics software and ever more powerful supercomputers, cities can get extremely accurate and detailed weather forecasts for very specific locations — such as a two-block radius — up to 48 hours in advance. With this predictive information, emergency response teams are able to be deployed close to where problems are likely to occur.

This technology can provide longer advance notice of adverse weather conditions, allowing more time for disaster prevention. Rather than monitor a storm, Deep Thunder assists governments and businesses with the ability to stage resources at the right place and time, prior to an event, to minimize the impact and save lives.

More on Deep Thunder.


Sean McKenna


Deep Thunder provides modeling technology to help people prepare for extreme weather events.

IBM Deep Thunder creates 24 to 48-hour forecasts at 1 – 2 km resolution with a lead time of three hours to three days. Weather data can be coupled with analytics and visualization tailored to individual business needs.

Airline industry

With the right combination of precision weather prediction and business analytics insights, airlines and airports could better manage the logistical nightmare of weather-generated delays. Flights could be re-routed or consolidated more efficiently.

Utility companies and renewable energy

Extreme weather patterns have also led global utilities and other businesses sensitive to weather to look to predictive analytics for creating more robust reaction plans. Companies like DTE Energy in the Detroit metropolitan area are now able to determine areas where incoming storms are likely to cause damage to their distribution system, and how to optimize their deployment of restoration crews.

Or a renewable energy producer can adjust the output of a wind turbine. Results of these calculations of renewable power prediction in the US and China are greater than 80 percent accurate.


Traditionally, agriculture is practiced by performing a particular task, such as planting or harvesting, against a predetermined schedule. But by collecting real-time data on weather, soil and air quality, crop maturity and even equipment and labor costs and availability, predictive analytics can be used to make smarter decisions. This is known as precision agriculture.

With precision agriculture, control centers collect and process data in real time to help farmers make the best decisions with regard to planting, fertilizing and harvesting crops. Sensors placed throughout the fields are used to measure temperature and humidity of the soil and surrounding air.

In addition, pictures of fields are taken using satellite imagery and robotic drones. The images over time show crop maturity and when coupled with predictive weather modeling showing pinpoint conditions 48 hours in advance, IBM Research is able to build models and simulations that can predict future conditions and help farmers make proactive decisions.

Supply chain and logisitics

Weather not only affects how crops grow, but also logistics around harvesting and transportation. When harvesting sugar cane, for example, the soil needs to be dry enough to support the weight of the harvesting equipment. If it’s humid and the soil is wet, the equipment can destroy the crop.

By understanding what the weather will be over several days and what fields will be affected, better decisions can be made in advance about which fields workers should be deployed to.

The ability to get accurate, detailed 24-hour weather forecasts, when coupled with business data such as asset management tracking, can transform the way any number of industries manage their processes.

They can tailor services, change routes and deploy equipment—anticipating and minimizing the effects of major weather events on clients and constituents, reducing costs, improving service and even saving lives.