Numerical weather prediction
Forecasting the atmosphere by integrating physical equations forward from an observed state, and the reason forecasts are a public good.
Numerical weather prediction models the atmosphere as a fluid on a three-dimensional grid, assimilates observations into a best estimate of its current state, and integrates the governing equations forward in time. Because small errors in the initial state grow, operational centres run ensembles - many perturbed forecasts at once - and the spread between members is itself the useful output, since it describes how confident the forecast deserves to be.
The models and much of their output are produced by national agencies and published, which has an important commercial consequence: the forecast is not the scarce thing. Everyone competent has it. What is scarce is knowing which of your assets, corridors and counterparties sit underneath it, which is a question about relationships rather than about meteorology.
Machine-learning forecast models have changed the economics of running one, not the nature of the problem. A faster forecast still has to be resolved onto specific plants, substations and ports before it says anything actionable - and it still has to carry its issue time, because a forecast without one cannot be tested against what actually happened.
Evidence that keeps pace with the decision.
Evaluate Nuclir against the systems, markets, and decisions that matter to your organization. The result is current intelligence with the context needed to use it responsibly.