| Abstract: |
| Quantification of evolving uncertainties is required for both probabilistic forecasting and data assimilation in weather prediction. In current practice, the ensemble of model simulations is often used as primary tool to describe the required uncertainties. In idealized settings, using toy models that mimic convective situations, we explore two alternative approaches, so called stochastic Galerkin method which integrates uncertainties forward in time using a spectral approximation in the stochastic space and machine learning approach based on conformal prediction. |
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