Weather forecasting shapes critical decisions daily, from carrying an umbrella to preparing for life-threatening cyclones. While forecasts have improved significantly over the years, inherent uncertainties remain due to incomplete weather observations and the complexity of atmospheric physics. These uncertainties often grow exponentially, making precise long-term predictions challenging. A new artificial intelligence-based weather model, GenCast, may hold the key to unprecedented forecasting accuracy and speed. Traditional weather forecasting relies on numerical weather prediction (NWP) algorithms, which simulate atmospheric dynamics using mathematical equations. While NWP methods have served as a global standard for decades, their accuracy is limited by computational complexity and the need for simplifications in modeling. To address this, meteorologists increasingly use ensemble forecasts, which run multiple simulations to provide a range of possible scenarios rather than a single deterministic outcome. Schematic of how GenCast produces a forecast. (CREDIT: Nature) The ECMWF’s ensemble forecast system, ENS, exemplifies state-of-the-art probabilistic modeling. Its ensemble members represent sharp, realistic weather trajectories and capture essential spatiotemporal structures, crucial for understanding large-scale phenomena like cyclones. However, even this advanced system …