A possibilistic interpretation of ensemble forecasts: experiments on the imperfect Lorenz 96 system

Le Carrer, Noémie ORCID: 0000-0002-6057-2057 and Green, Peter L (2020) A possibilistic interpretation of ensemble forecasts: experiments on the imperfect Lorenz 96 system. Advances in Science and Research, 17. pp. 39-45.

Access the full-text of this item by clicking on the Open Access link.

	Text asr-17-39-2020.pdf - Published Version Download (298kB) \| Preview
	Text asr-2020-10-manuscript-version1.pdf - Submitted version Download (389kB) \| Preview

Official URL: http://dx.doi.org/10.5194/asr-17-39-2020

Abstract

<jats:p>Abstract. Ensemble forecasting has gained popularity in the field of numerical medium-range weather prediction as a means of handling the limitations inherent to predicting the behaviour of high dimensional, nonlinear systems, that have high sensitivity to initial conditions. Through small strategical perturbations of the initial conditions, and in some cases, stochastic parameterization schemes of the atmosphere-ocean dynamical equations, ensemble forecasting allows one to sample possible future scenarii in a Monte-Carlo like approximation. Results are generally interpreted in a probabilistic way by building a predictive density function from the ensemble of weather forecasts. However, such a probabilistic interpretation is regularly criticized for not being reliable, because of the chaotic nature of the dynamics of the atmospheric system as well as the fact that the ensembles of forecasts are not, in reality, produced in a probabilistic manner. To address these limitations, we propose a novel approach: a possibilistic interpretation of ensemble predictions, taking inspiration from fuzzy and possibility theories. Our approach is tested on an imperfect version of the Lorenz 96 model and results are compared against those given by a standard probabilistic ensemble dressing. The possibilistic framework reproduces (ROC curve, resolution) or improves (ignorance, sharpness, reliability) the performance metrics of a standard univariate probabilistic framework. This work provides a first step to answer the question whether probability distributions are the right tool to interpret ensembles predictions. </jats:p>

Item Type:	Article
Depositing User:	Symplectic Admin
Date Deposited:	04 Jun 2020 08:20
Last Modified:	18 Jan 2023 23:50
DOI:	10.5194/asr-17-39-2020
Open Access URL:	https://doi.org/10.5194/asr-17-39-2020,%202020
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3089428