EnvirVis18

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https://diglib.eg.org/handle/10.2312/2632396

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Browsing EnvirVis18 by Subject "Environmental sciences"

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    Developing a Concept to Visualize Object-based Weather Forecasting Ensembles
    (The Eurographics Association, 2018) Feige, Kathrin; Posada, Rafael; Blahak, Ulrich; Karsten Rink and Dirk Zeckzer and Roxana Bujack and Stefan Jänicke
    Operational weather forecasters face the challenge of having to process and interpret a large amount of available information. Therefore, condensation of extensive information is required. Research and development of forecasting techniques will on the one hand improve the forecast quality and on the other hand lead to an increased amount of data. A new extensive and valuable data set will emerge from the SINFONY project at Deutscher Wetterdienst (DWD). It aims at a seamless forecast of upcoming convective events from actual time up to some hours by combining observation-based nowcasting techniques and numerical weather prediction (NWP) ensembles into a single system. In this context, a group of products will comprise features ("cell objects") that were extracted from three-dimensional radar measurements and NWP ensemble simulations. A user-oriented intuitive visualization of the new meteorological data is crucial for weather warning and forecasting. Before including new data into forecast operation, extensive tests and evaluations have to be performed. It therefore requires a careful iterative development process with continuous evaluation by the users. To facilitate this process, an initial visualization mock-up is created, which will be used to prototype and refine visualization and data product concepts. The browser-based nature of the tool allows to quickly share an interactive design with the users which, in turn, will help to have in-depth discussions and to collect visualization requirements, before the final concept is implemented into the meteorological workstation. This paper presents the first use-case for this approach: The development of a concept to visualize object-based severe convective events based on matching observed and simulated features.