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Numerical weather prediction ensembles are routinely used for operational weather forecasting. The members of these ensembles are individual simulations with either slightly perturbed initial conditions or different model parameterizations, or occasionally both. Multi-member ensemble output is usually large, multivariate, and challenging to interpret(More)
This paper presents an efficient visualization and exploration approach for modeling and characterizing the relationships and uncertainties in the context of a multidimensional ensemble dataset. Its core is a novel dissimilarity-preserving projection technique that characterizes not only the relationships among the mean values of the ensemble data objects(More)
Weather Research and Forecasting (WRF) models simulate weather conditions by generating 2D numerical weather prediction ensemble members either through perturbing initial conditions or by changing different parameterization schemes, e.g., cumulus and mi-crophysics schemes. These simulations are often used by weather analysts to analyze the nature of(More)
Dynamic numerical weather prediction models have been designed to deal with large-scale, highly predictable midlatitude atmospheric patterns. However, the capability of these models to simulate thermodynamically driven warm-season rainfall events, such as afternoon airmass thunderstorm formation in subtropical summers, is highly limited. Current methods of(More)
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