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        "title": "Real-Time Volumetric Rendering of Meteorological Cloud Data",
        "date": "2026-03-25",
        "abstract": "Traditional meteorological visualizations collapse the vertical structure of the atmosphere\ninto two-dimensional map overlays, losing precisely the information most relevant in\ncomplex terrain. Expert tools for three-dimensional atmospheric exploration exist, but\ntarget domain scientists on dedicated workstation infrastructure. This thesis presents a\npipeline for the real-time volumetric rendering of meteorological cloud data within the\nweBIGeo web-based geographic visualization platform, with the goal of making cloud\nstructure intuitively readable to general users in a standard web browser.\nThe pipeline transforms hourly ICON-D2 forecast output into a compressed, streamable\ntile hierarchy that a WebGPU ray-marcher samples at interactive frame rates. Preprocessing\na single forecast timestamp completes in approximately 33 s of compute time,\nproducing a tile hierarchy of roughly 86MiB on average. Rendering cost is well within\ninteractive bounds: even under high cloud coverage, the cloud pass consumes around\n2.25ms of GPU time, a small fraction of the 33ms budget for 30 fps. Qualitative evaluation\nagainst EUMETSAT satellite imagery shows that large-scale cloud patterns are\nreproduced with reasonable fidelity. Comparison against webcam imagery reveals three\nconcrete limitations: the coarse and non-uniform vertical resolution of the source data\nis insufficient to resolve sharp fog layer boundaries, the sub-grid density distribution\ndoes not preserve the character of small cloud elements such as wispy puffs, and the tile\nresolution is too coarse to encode the surface texture of individual cumulus cells. The\nsystem is best understood as a large-scale atmospheric context layer rather than a precise\nlocal forecast tool.",
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        "title": "Data-Driven Compute Overlays for Interactive Geographic Simulation and Visualization",
        "date": "2025-12-30",
        "abstract": "We present interactive data-driven compute overlays for native and web-based 3D geographic map applications based on WebGPU. Our data-driven overlays are generated in a multi-step compute workflow from multiple data sources on the GPU. We demonstrate their potential by showing results from snow cover and avalanche simulations, where simulation parameters can be adjusted interactively and results are visualized instantly. Benchmarks show that our approach can compute large-scale avalanche simulations in milliseconds to seconds, depending on the size of the terrain and the simulation parameters, which is multiple orders of magnitude faster than a state-of-the-art Python implementation.",
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        "booktitle": "2025 IEEE Visualization and Visual Analytics (VIS)",
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        "date_to": "2025-11-07",
        "doi": "10.1109/VIS60296.2025.00043",
        "event": "IEEE VIS 2025",
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        "publisher": "IEEE",
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                "href": "https://youtu.be/pZq0H_l-8Bs?t=1580",
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        "id": "komon-2025-webigeo",
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        "title": "weBIGeo: Interaktive Lawinensimulation im Web",
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