In the AI of the storm
The climate challenge
Industries like agriculture, aviation and insurance rely on accurate weather data in a world of increasing weather volatility.
Morecasting is the new forecasting
Energy trading and emergency management need more frequent forecasts. AI weather models have upended a discipline that ran on supercomputers for fifty years.
Predictions in minutes, not hours
Machine-learning forecast models now produce skilful global predictions in minutes on GPUs, work that takes hours on conventional HPC and at a fraction of the energy.
Meteorological departments are under the weather
National meteorological agencies face an awkward transition.
CPU supercomputers are ageing, capital-intensive and energy-hungry, while AI forecasting demands a GPU estate most agencies don't have and can't justify building.
Nowcasting; the hyperlocal, minutes-ahead prediction that saves lives in flash floods and severe storms, requires compute near the radar and sensor networks feeding it.
Many agencies operate under mandates dictating that forecast infrastructure remains nationally controlled.
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The eye on the storm
AI models let agencies run forecasts more often, at higher resolution, with larger ensembles - turning a twice-daily global forecast into a continuously updating, regionally downscaled prediction engine. Earlier warnings mean protected crops, rerouted aircraft, pre-positioned emergency services and, ultimately, lives saved and productivity losses avoided.
Why Radian Arc is at the forefront of forecasting
AI forecasting is the textbook case for intelligence per Watt: the same skill as a numerical model for a small fraction of the energy, but only if the GPU infrastructure itself is efficient.
Radian Arc's orchestration layer schedules forecast cycles, ensemble runs and nowcasting inference for maximum GPU utilization; the federation places nowcasting compute in-region, next to the observation networks; and the sovereign control plane keeps a nation's forecasting capability under national control.
Our OPEX models mean LLM training can be done on existing infrastructure, giving underfunded meteorological departments access to the latest compute in days, not after the next annual budget cycle. More forecasts, more data, more lead time - per Watt, per dollar, per day.
Radian Arc: More storm detections per Watt.
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