Scaling of simulations challenge the effectiveness of conventional visualization methods. This problem becomes two-fold for mesoscale weather models that operate in near-real-time at cloud-scale resolution. For example, typical approaches to vector field visualization (e.g., wind) are based upon global methods, which may not illustrate detailed structure. In addition, such computations employ multi-resolution meshes to capture small-scale phenomena, which are not properly reflected in both vector and scalar realizations. To address the former, critical point analysis and simple bandpass filtering of wind fields is employed for better seed point identification for streamline calculations. For the latter, an encapsulation of nested computational meshes is developed for general realization. It is then combined with the seed point calculation for an improved vector visualization of multi-resolution weather forecasting data.
Non-traditional applications of scientific data challenge the typical approaches to visualization. In particular, popular scientific visualization strategies fail when the expertise ofthe data consumer is in a diflerentfield than the one that generated the data and data from the user b domain must be utilized as well. This problem occurs when predictive weather simulations are wedfor a number of weather-sensitive applications. A data firsion approach is adopted for visualization design and utilizedfor specific example problems.
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