Beyond probabilities: A possibilistic framework to interpret ensemble predictions and fuse imperfect sources of information

Le Carrer, Noemie ORCID: 0000-0002-6057-2057 and Ferson, Scott ORCID: 0000-0002-2613-0650
(2021) Beyond probabilities: A possibilistic framework to interpret ensemble predictions and fuse imperfect sources of information. QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY, 147 (739). pp. 3410-3433.

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<jats:title>Abstract</jats:title><jats:p>Ensemble forecasting is widely used in medium‐range weather predictions to account for the uncertainty that is inherent in the numerical prediction of high‐dimensional, nonlinear systems with high sensitivity to initial conditions. Ensemble forecasting allows one to sample possible future scenarios in a Monte‐Carlo‐like approximation through small strategical perturbations of the initial conditions, and in some cases stochastic parametrization schemes of the atmosphere–ocean dynamical equations. Results are generally interpreted in a probabilistic manner by turning the ensemble into a predictive probability distribution. Yet, due to model bias and dispersion errors, this interpretation is often not reliable and statistical postprocessing is needed to reach probabilistic calibration. This is all the more true for extreme events which, for dynamical reasons, cannot generally be associated with a significant density of ensemble members. In this work we propose a novel approach: a possibilistic interpretation of ensemble predictions, taking inspiration from possibility theory. This framework allows us to integrate in a consistent manner other imperfect sources of information, such as the insight about the system dynamics provided by the analogue method. We thereby show that probability distributions may not be the best way to extract the valuable information contained in ensemble prediction systems, especially for large lead times. Indeed, shifting to possibility theory provides more meaningful results without the need to resort to additional calibration, while maintaining or improving skills. Our approach is tested on an imperfect version of the Lorenz '96 model, and results for extreme event prediction are compared against those given by a standard probabilistic ensemble dressing.</jats:p>

Item Type: Article
Uncontrolled Keywords: ensemble prediction, extreme event, imprecise probabilities, possibility theory, probabilistic weather forecasting, recalibration, statistical post-processing, weather regimes
Divisions: Faculty of Science and Engineering > School of Engineering
Depositing User: Symplectic Admin
Date Deposited: 12 Aug 2021 08:17
Last Modified: 24 Aug 2023 09:43
DOI: 10.1002/qj.4135
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