arXiv:2605.22242v1 Announce Type: new Abstract: Weather and climate forecasts are inherently uncertain due to chaotic dynamics, imperfect initial conditions, and incomplete representation of the underlying physical processes. Operational ensemble forecasts aim to represent these uncertainties through forecast spread, yet many approaches yield underdispersive estimates, with spread that grows too slowly relative to forecast error. Using the two-scale Lorenz 1996 system as a widely used, controlled testbed, we design a systematic approach to disentangle intrinsic variability, initial-condition p
The continuous drive to improve forecasting accuracy, especially in complex chaotic systems like weather and climate, is pushing the development of AI-driven parameterization techniques.
Improved predictive models, particularly in climate, have massive implications for resource management, disaster preparedness, and economic stability.
The ability to better decompose and manage ensemble spread in high-dimensional chaotic systems could lead to more reliable forecasts and better decision-making under uncertainty.
- · Weather and climate forecasting agencies
- · Insurance industry
- · Agriculture sector
- · AI/ML researchers in scientific computing
- · Sectors reliant on less accurate, traditional forecasting methods
More accurate long-range weather and climate predictions become possible.
Economic planning and risk assessment across various industries will gain significantly from enhanced predictability.
The application of these learned stochastic parameterizations could extend to other complex, chaotic systems beyond meteorology, such as financial markets or biological systems.
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Read at arXiv cs.LG