WeatherMesh transforms raw balloon observations into the world’s fastest, most accurate weather forecasts. By combining cutting-edge AI models with a unique global network of weather balloons, we deliver insights at a speed and scale that traditional physics-based forecasting cannot match.
WeatherMesh (WM-4) is powered by transformer-based AI models running on GPUs, trained on decades of atmospheric data and continuously updated by real-time balloon observations. Unlike traditional Numerical Weather Prediction (NWP), which requires vast supercomputing resources and hours of runtime,
WeatherMesh produces forecasts in seconds—up to 100,000x faster and with a fraction of the compute cost.
WeatherMesh is more accurate than ECMWF's HRES, the gold-standard physics-based global forecasting system, across all variables and lead times from 1-10 days. The accuracy improvement is particularly notable for surface variables like 2-meter temperature and in the 7-10 day range.
WeatherMesh is uniquely informed by our global balloon network. AI-based data assimilation means forecasts refresh every 10 minutes, providing the most up-to-date picture of evolving weather systems.
More accurate at predicting temperature at ground weather stations than leading AI models such as AIFS
WeatherMesh is able to forecast in hourly and 3-hourly timesteps (vs. 12-hourly from other models like GenCast), enabling more actionable insights
We publish at conferences, maintain realtime benchmarks, and open-source our code, ensuring transparency and advancing the field—unlike closed competitors
State-of-the-art neural network architecture optimized for processing weather data
Encoder-processor-decoder structure enables flexible training and efficient inference
Weather states are mapped into latent space representations for efficient pattern recognition
Trained on 50+ years of historical weather data
For a complete technical deep-dive into WM-4's architecture, training methodology, and performance benchmarks, read our detailed technical blog post.