Numerical simulations of industrial and geophysical fluid flows cannot usually solve the exact Navier-Stokes equations. Accordingly, they encompass strong local errors. For some applications-like coupling models and measurements-these errors need to be accurately quantified, and ensemble forecast is a way to achieve this goal. This paper reviews the different approaches that have been proposed in this direction. A particular attention is given to the models under location uncertainty and stochastic advection by Lie transport. Besides, this paper introduces a new energy-budget-based stochastic subgrid scheme and a new way of parameterizing models under location uncertainty. Finally, new ensemble forecast simulations are presented. The skills of that new stochastic parameterization are compared to that of the dynamics under location uncertainty and of randomized-initial-condition methods.
<p style='text-indent:20px;'>Nowadays, most weather forecasting centers produce ensemble forecasts. Ensemble forecasts provide information about probability distribution of the weather variables. They give a more complete description of the atmosphere than a unique run of the meteorological model. However, they may suffer from bias and under/over dispersion errors that need to be corrected. These distribution errors may depend on weather regimes. In this paper, we propose various extensions of the Gaussian mixture model and its associated inference tools for ensemble data sets. The proposed models are then used to identify clusters which correspond to different types of distribution errors. Finally, a standard calibration method known as Non homogeneous Gaussian Regression (NGR) is applied cluster by cluster in order to correct ensemble forecast distributions. It is shown that the proposed methodology is effective, interpretable and easy to use. The clustering algorithms are illustrated on simulated and real data. The calibration method is applied to real data of temperature and wind medium range forecast for 3 stations in France.</p>
<p>Nowadays, most weather forecasting centers produce ensemble forecasts. &#160;Ensemble forecasts provide information about probability distribution of the weather variables. They give a more complete description of the atmosphere than a unique run of the meteorological model. However, they may suffer from bias and under/over dispersion errors that need to be corrected. These distribution errors may depend on weather regimes. In this paper, we propose various extensions of the Gaussian mixture model and its associated inference tools for ensemble data sets. &#160;The proposed models are then used to identify clusters which correspond to different types of distribution errors. Finally, a standard calibration method known as Non homogeneous Gaussian Regression (NGR) &#160;is applied cluster by cluster in order to correct ensemble forecast distributions. It is shown that the proposed methodology is effective, interpretable and easy to use. &#160;The clustering algorithms are illustrated on simulated and real data. The calibration method is applied to real data of temperature and wind medium range forecast for 3 stations in France.&#160;</p>
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.