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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 with 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.
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 Forecasts (ECMWF), the European intergovernmental organization seen by meteorologists as the leading provider of accurate weather prediction.
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.
WeatherMesh-6 produces a forecast every hour, as opposed to every six hours, as traditional models do, and its resolution is now down to 3 km in the continental U.S.
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 or predict as accurately over longer time horizons.
Still, weather AI is improving rapidly and already being used at major government agencies around the world. Researchers are working to integrate it into the systems used to aggregate weather data and produce public forecasts.
WindBorne benefits from its unique combination of model-building and data collection. The company now has about 400 balloons in flight gathering sensor readings at any given time, launched from 15 sites around the globe. The advances in its current model come from improvements in how the data collected by the balloons is fed into the models.
“I don’t understand, personally, the business model of being [an] AI based weather company without a dataset advantage,” WindBorne CEO John Dean told TechCrunch.
The ECMWF’s superiority is attributed to the organization’s skills at “data assimilation,” the work of turning disparate sensor readings into a comprehensive, machine-readable picture of the world. For now, AI weather models depend on datasets produced by the ECMWF and the U.S. National Oceanic and Atmospheric Administration (NOAA).
But WindBorne and other organizations are working to feed data directly into the models, and the company’s head of AI, Joan Creus-Costa, says the direct ingestion of data from their balloons and other sources is the key reason for improvement in the new version of WeatherMesh. It’s taken a year of tuning and re-architecting the transformer-based model for the model to deliver these forecasts without losing stability.