By combining hyper-local, short-term custom forecasts developed by IBM Research with The Weather Company’s global forecast model the team hope to improve the accuracy of weather forecasting. Deep Thunder will lean on the capabilities of IBM’s machine learning technologies to aggregate a variety of historical data sets and future forecasts to provide fresh new guidance every three hours.
“The Weather Company has relentlessly focused on mapping the atmosphere, while IBM Research has pioneered the development of techniques to capture very small scale features to boost accuracy at the hyper local level for critical decision making,” said Mary Glackin, Head of Science & Forecast Operations for The Weather Company. “The new combined forecasting model we are introducing today will provide an ideal platform to advance our signature services – understanding the impacts of weather and identifying recommended actions for all kinds of businesses and industry applications.”
The platform itself will combine more than 100 terabytes of third-party data daily, as well as data collected from the company’s 195,000 personal weather stations. The offering can be customized to suit the location of various businesses, with IBM execs claiming hyper-local forecasts can be reduced to between a 0.2 to 1.2 mile resolution, while also taking into account other factors for the locality such as vegetation and soil conditions.
Applications for the new proposition can vary from the agriculture to city planning & maintenance to validating insurance claims, however IBM has also stated consumer influences can also be programmed into the platform, meaning retailers could manage their supply chains and understand what should be stocked on shelves with the insight.