Cyclone Remal struck the coast of Bangladesh on September 14, 2025, with sustained winds of 215 km/h. It was one of the strongest cyclones to hit the Bay of Bengal in a decade.
The death toll was 847. In a country where cyclones of similar magnitude killed 138,000 people in 1991 and 4,234 in 2007, this number - while every death is a tragedy - represents a transformation in disaster preparedness.
A significant factor in that transformation was GraphCast, Google DeepMind's AI weather prediction system, which was deployed operationally by the Bangladesh Meteorological Department for the first time in 2025.
GraphCast predicted Cyclone Remal's landfall location within 12 kilometres of the actual strike point, 72 hours in advance. The European Centre for Medium-Range Weather Forecasts (ECMWF), the global gold standard for weather prediction, achieved comparable accuracy only at the 36-hour mark.
Those 36 extra hours of reliable prediction time translated directly into action. The Bangladesh government issued evacuation orders for 2.1 million coastal residents 60 hours before landfall - compared to the 24-hour warning that would have been possible with traditional forecasting alone.
"In cyclone response, every hour of warning saves lives," said Dr. Saiful Islam, director of the Bangladesh Flood Forecasting and Warning Centre. "36 additional hours meant we could reach communities in the Sundarbans and on the char islands that historically receive warnings too late. The AI did not save those people. The evacuations saved those people. But the AI made the evacuations possible."
An analysis by the International Federation of Red Cross and Red Crescent Societies estimates that the early warnings enabled by GraphCast's predictions prevented approximately 12,000 deaths, based on mortality modelling from comparable historical cyclones hitting similar population densities.
The model runs on a single Google TPU and produces a 10-day global weather forecast in under 60 seconds - compared to hours of supercomputer time for traditional numerical weather prediction. This makes it deployable in countries that lack the computational infrastructure for conventional high-resolution forecasting.
Bangladesh, the Philippines, and Mozambique have all now integrated GraphCast into their national early warning systems.
What we know for certain
GraphCast predicted Cyclone Remal's path 36 hours earlier than traditional models. Bangladesh evacuated 2.1 million people. The death toll was 847, dramatically lower than comparable historical cyclones. IFRC estimates 12,000 lives saved.
What we are inferring
AI weather prediction will become the global standard within 2-3 years, given the cost and accuracy advantages. Countries most vulnerable to extreme weather stand to benefit most.
What we couldn't verify
The precise 12,000 figure is a modelled estimate by IFRC, not a direct count. The actual number of lives saved is inherently unknowable. We also could not independently verify the 12km accuracy claim, as BMD has not published the raw prediction data.