Communication Dans Un Congrès Année : 2025

Visualizing uncertainty from meteo-oceanic ensemble data

Résumé

Accurate and timely forecasts represent a fundamental asset to mitigate damages due to natural disasters such as coastal flooding related to storm events. The production of such forecasts is based on the interpretation of data from various sources (local sensors, satellite imagery, simulated meteo-oceanic conditions) by experts of these meteorological phenomena. Visualization is a powerful tool for meteorological forecasters in order to cross these different information, make sense of large volumes of simulated meteooceanic data, while taking into account the uncertainty, inherent in natural phenomena or any dynamic complex system. The use of ensemble simulation (based on the generation of a set of possible future scenarios analyzed as a whole, instead of a single one) offers a way for forecasters to better integrate the uncertainty related to meteo-oceanic simulation models in the determination of vigilance levels, allowing to face the growing demand for ever more precise and higher-resolution local forecasts. However, the difficult task of visualizing spatio-temporal multivariate data for meteorological forecasters becomes even more complex when dealing with many ensemble scenarios, especially if forecasters have to get an overview of the phenomena through the entire set of scenarios. Furthermore, forecasters need bespoke visualizations that meet the demands of delivering vigilance warnings in daily operational settings. Uncertainty indicators can however be extracted by aggregating ensemble outputs, and be encoded through different cartographic approaches, allowing to visualize, for example, the spatial distribution of main trend among possible scenarios with a corresponding confidence interval. In this paper we present different geovisualization approaches, aiming to help the visual analysis of ensemble meteo-oceanic data in the context of coastal flooding forecast. These approaches use different ways to aggregate ensemble simulation outputs, and to encode the calculated indicators combining bivariate color scales and grid-distributed glyphs of different designs, which offers different points of view on the scenarios distribution. These different approaches were co-constructed with meteorological forecasters and other coastal flooding experts, and finally submitted to their manipulation. We present also the feedback of these experts users regarding these different propositions, which gives an insight about which type of representation could better answer their scientific or operational needs

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hal-05213756 , version 1 (18-08-2025)

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Julius M. Bañgate, Jacques Gautier, Sidonie Christophe, Déborah Idier, Denis Paradis, et al.. Visualizing uncertainty from meteo-oceanic ensemble data. 2025 IEEE Workshop on Uncertainty Visualization: Unraveling Relationships of Uncertainty, AI, and Decision-Making, Nov 2025, Vienna, Austria. pp.58-68, ⟨10.1109/uncertaintyvisualization68947.2025.00013⟩. ⟨hal-05213756⟩
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