Rainbow Weather raises $5.5M to refine real-time weather forecasting

"Rainbow Weather has raised $5.5 million in seed funding to push weather forecasting further into the short-term, high-precision territory it believes the industry still underserves. The Warsaw-based climate tech startup focuses on hyperlocal, minute-by-minute forecasts, zeroing in on what happens in the next few hours rather than days out. The round was backed by a syndicate of investors, including Yuri Gurski, founder of Flo Health, one of Europe's best-known consumer tech unicorns."

"Rainbow Weather's core product is a mobile app that delivers four-hour precipitation forecasts calculated from the exact moment a user checks the weather. Open the app at 3:51 am, and it forecasts conditions through 7:51am, refreshed every 10 minutes and mapped down to a one-square-kilometre grid. That level of temporal and spatial precision is what the company says sets it apart from mainstream weather apps."

"Many legacy forecasting providers rely on optical flow for short-term precipitation forecasting. That's a fast but simplistic method that treats clouds as shapes in motion, without any understanding of atmospheric physics. A second category of services uses large-scale mathematical models that do incorporate physical principles, but they're so cumbersome and slow that they can't respond quickly to real-time weather changes."

Rainbow Weather raised $5.5 million in seed funding to develop hyperlocal, minute-by-minute forecasts focused on the next few hours. The Warsaw-based startup's mobile app delivers four-hour precipitation forecasts calculated from the exact moment a user checks the weather, refreshed every ten minutes and mapped to a one-square-kilometre grid. The company uses machine learning to fuse high-resolution radar, satellite, weather station and smartphone barometer data to reduce noise and improve short-term accuracy. The approach targets limitations of optical-flow methods and large-scale numerical models, which struggle with rapid changes or are too cumbersome for real-time responsiveness.

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