arXiv:2606.27001v1 Announce Type: new Abstract: Quantifying the evolution of uncertainty is critical to both probabilistic forecasting and data assimilation in numerical weather prediction. In this study, we investigate the applicability of conformal prediction (CP), a recent machine learning (ML) method, to quantify uncertainty in a controlled, idealized setting. We use the one dimensional modified shallow water model, designed to mimic the convective process. CP provides a set of possible outcomes with a chosen confidence level. Here, we compare and evaluate the average empirical coverage, t
The increasing sophistication and integration of AI across critical domains like climate modeling necessitates robust uncertainty quantification methods.
Accurate prediction of uncertainty in complex systems is crucial for decision-making in high-stakes fields such as disaster preparedness and resource management.
The application of machine learning techniques like conformal prediction offers a new avenue for enhancing the reliability of probabilistic forecasts beyond traditional statistical methods.
- · Numerical Weather Prediction Centers
- · Climate Modeling Research
- · Machine Learning Researchers
- · Risk Management Firms
- · Traditional Uncertainty Quantification Methods (if not adapted)
- · Systems Reliant on Imprecise Forecasts
Improved accuracy in weather and climate predictions through better uncertainty quantification.
More reliable early warning systems for extreme weather events, leading to better disaster response.
Enhanced trust in AI-driven forecasting models, accelerating their adoption in critical infrastructure planning.
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