Visualization Techniques for Applications of High-Resolution Numerical Weather Models

Visualization is a method of computing by which the enormous bandwidth and processing power of the human visual (eye-brain) system becomes an integral part of extracting knowledge from complex data. In that regard, our previous work has discussed methods of appropriate mapping of user goals to the design of pictorial content by considering both the underlying data characteristics and the (human) perception of the visualization. However, the scaling of traditional data sources and introduction of new applications challenges the effectiveness of conventional visualization methods. Consider, for example, rapid execution (e.g., 10 to 30 times faster than real-time) of mesocale weather models operating at cloud-scale resolution. Earlier we illustrated that there is a mismatch between this rate of data generation and the ability to utilize the model results with traditional two-dimensional weather visualization techniques. Introduction of three-dimensional visualization is only a partial solution because typical methods can easily fail to capture the salient characteristics of such simulations. The resolution of the visualization must match that of the scale of the model to build usable products that are preceptually coherent. We have determined that realization needs to be based upon the integration of all computational nests with high-resolution topography in a three-dimensional cartographic coordinate system and sequencing consistent with the internal time step of the computation. The choice of realization geometry is also affected by the resolution of the data, especially for vector fields (e.g., predicted winds), so that perceptual artifacts do not dominate the presentation, especially in animation. For instance, we have adapted methods of flow visualization utilized in aerospace computational fluid dynamics for improved feature extraction. It has enabled us to depict properties predicted by the simulation such as vortex shedding of flows past mountains and other orographic effects as well as the influence of prevailing winds. We are extending these ideas for situations when high-resolution models can be utilized in variety of decision-making efforts such as emergency planning, energy production, airline operations, risk assessment, etc. These applications imply the coupling of weather simulations with other models, analyses and data. To enable effective assessment and appropriate decisions, focused visualizations must be designed to integrate these distinct data sources, yet still be driven by user goals. This leverages our past efforts in providing uniform access to a diversity of data to preserving their underlying fidelity despite variations in sampling and coordinate systems. In many cases, the resultant visualizations do not show forecasts of weather phenomena directly but the derived properties, which are influenced by weather.