Visualizing Time-Specific Hurricane Predictions, with Uncertainty, from Storm Path Ensembles

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dc.contributor.authorLiu, Leen_US
dc.contributor.authorMirzargar, Mahsaen_US
dc.contributor.authorKirby, Robert M.en_US
dc.contributor.authorWhitaker, Rossen_US
dc.contributor.authorHouse, Donald H.en_US
dc.contributor.editorH. Carr, K.-L. Ma, and G. Santuccien_US
dc.date.accessioned2015-05-22T12:51:51Z
dc.date.available2015-05-22T12:51:51Z
dc.date.issued2015en_US
dc.description.abstractThe U.S. National Hurricane Center (NHC) issues advisories every six hours during the life of a hurricane. These advisories describe the current state of the storm, and its predicted path, size, and wind speed over the next five days. However, from these data alone, the question ''What is the likelihood that the storm will hit Houston with hurricane strength winds between 12:00 and 14:00 on Saturday?'' cannot be directly answered. To address this issue, the NHC has recently begun making an ensemble of potential storm paths available as part of each storm advisory. Since each path is parameterized by time, predicted values such as wind speed associated with the path can be inferred for a specific time period by analyzing the statistics of the ensemble. This paper proposes an approach for generating smooth scalar fields from such a predicted storm path ensemble, allowing the user to examine the predicted state of the storm at any chosen time. As a demonstration task, we show how our approach can be used to support a visualization tool, allowing the user to display predicted storm position - including its uncertainty - at any time in the forecast. In our approach, we estimate the likelihood of hurricane risk for a fixed time at any geospatial location by interpolating simplicial depth values in the path ensemble. Adaptivelysized radial basis functions are used to carry out the interpolation. Finally, geometric fitting is used to produce a simple graphical visualization of this likelihood. We also employ a non-linear filter, in time, to assure frame-toframe coherency in the visualization as the prediction time is advanced. We explain the underlying algorithm and definitions, and give a number of examples of how our algorithm performs for several different storm predictions, and for two different sources of predicted path ensembles.en_US
dc.description.number3en_US
dc.description.sectionheadersEngineering and Physical Sciencesen_US
dc.description.seriesinformationComputer Graphics Forumen_US
dc.description.volume34en_US
dc.identifier.doi10.1111/cgf.12649en_US
dc.identifier.pages371-380en_US
dc.identifier.urihttps://doi.org/10.1111/cgf.12649en_US
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectI.3.3 [Computer Graphics]en_US
dc.subjectPicture/Image Generationen_US
dc.subjectViewing algorithmsen_US
dc.subjectinformation visualizationen_US
dc.subjectuncertaintyen_US
dc.subjectensemblesen_US
dc.subjecthurricane predictionen_US
dc.titleVisualizing Time-Specific Hurricane Predictions, with Uncertainty, from Storm Path Ensemblesen_US
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34-Issue 3
EuroVis15: Eurographics Conference on Visualization