Rainbow Weather Raises $1.5 Million for AI Hyperlocal Weather Forecasting

StartupHub.ai Staff

Rainbow Weather

AI-driven weather forecasting startup, Rainbow Weather, secured $1.5 million in funding from Kolos Ventures, Verras Capital, Melnichek Investments, and the European Bank for Reconstruction and Development.

In 2022, weather-related disasters caused $42 billion in damages globally, highlighting the need for more precise weather forecasting. Rainbow Weather’s AI system, which integrates data from satellites, meteorology stations, and user inputs, delivers faster and more accurate forecasts than traditional methods and even outperforms Apple’s weather app.

Their own internal analysis reveals a substantial advantage over competitors. In the US, their rain prediction accuracy surpasses Apple Weather by 3.5 percentage points and outperforms AccuWeather by 9.5 percentage points globally. Precision was calculated based on the share of rain accurately predicted within a 30-minute forecast window. That difference in precision means considerably more individuals receive accurate weather notifications, enhancing preparedness, and potentially saving lives.

Their mobile app, available for iOS and Android with over half a million downloads, provides super-accurate hyperlocal forecasts notifying users about upcoming rain and snowfalls, storms and hurricanes and their movements on the map. They also participated in EBRD’s Star Venture program.

Rainbow Weather was founded in 2021 by Yury Melnichek and Alexander Matveenko.

DeepMind’s newly released GraphCast model also advances AI-driven weather forecasting, capable of providing highly accurate, medium-range weather forecasts. Its use of deep learning and Graph Neural Networks to process vast datasets enables it to outperform traditional weather prediction models. According to their team, GraphCast is 99.7% more accurate in some cases compared to the European Centre for Medium-Range Weather Forecasts’ (ECMRWF) High RESolution forecast (HRES) system. It excels in predicting extreme weather events, cyclone movements, and potential flooding through atmospheric data.

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