A new AI weather forecasting tool released today by the startup WindBorne Systems offers more frequent and accurate predictions on key variables than the world-leading system developed by European governments, thanks to advancements in how sensor readings are fed into deep learning models.

Founded by a group of Stanford students in 2019, WindBorne began by building a better weather balloon, with the idea of selling weather data. But the arrival of the weather-forecasting deep learning models in 2022, the team realized they could capture more value by building their own model as well.

WindBorne says the new version of its model offers a more accurate forecast than the ECMWF’s traditional and AI systems across several variables. One simple way to understand it, WindBorne’s chief product officer Kai Marshland says, is that WeatherMesh 6 “is as accurate five days out as a traditional forecast is the day before,” particularly on surface temperature measurements.

Today marks the release of the sixth version of that model, WeatherMesh, which the company says is more accurate than traditional and AI forecasts produced by the European Centre for Medium-Range Weather Forecasting (ECMWF), the European intergovernmental organization seen by meteorologists as the leading provider of accurate weather prediction today.

WeatherMesh 6 produces a forecast every hour, as opposed to every six hours, as traditional models do. Its resolution is now down to 3 km in Europe and the continental US, where the quality of data is highest.

Traditional weather forecasts are generated by complex physics models that require expensive super computers to run, and take a long time to do it. AI models — being built by startups and major labs like Google DeepMind—tend to move faster than physics models, but for now don’t have as high a resolution, as many variables and or predict as accurately over longer time horizons.