Visualizing Uncertain Tropical Cyclone Predictions using Representative Samples from Ensembles of Forecast Tracks

Area of Science:

• Data Visualization
• Meteorology
• Scientific Computing

Background:

• Current ensemble visualizations for predictions like hurricane tracks often confuse uncertainty with other storm attributes (size, strength).
• Multivariate information in simulation ensembles is difficult to display clearly in summary visualizations.
• Existing methods of displaying ensembles as annotated trajectories can be ineffective if overdrawn or disorganized.

Purpose of the Study:

• To develop a novel visualization technique for ensembles of tropical cyclone forecast tracks.
• To create a smaller, representative, and spatially organized ensemble for direct display as paths.
• To enable clearer revelation of spatial uncertainty, temporal predictions, and storm characteristics (size, intensity) without visual confusion.

Main Methods:

• Selective sampling of original ensembles to construct a smaller, representative, and spatially organized ensemble.
• Directly displaying the sampled ensemble as a set of paths to implicitly reveal spatial uncertainty.
• Developing a visualization for tropical cyclone forecast tracks incorporating spatial, temporal, size, and intensity data.

Main Results:

• The proposed visualization method effectively displays spatial uncertainty and additional storm characteristics without visual confusion.
• A cognitive study demonstrated the impact of track density and annotation presence on storm damage estimates.
• The approach allows for clearer communication of complex ensemble prediction data.

Conclusions:

• Selective ensemble sampling offers an effective solution for visualizing complex prediction spaces, particularly for tropical cyclone forecasts.
• This method enhances the clarity and interpretability of ensemble predictions by avoiding visual channel confounds.
• The developed visualization aids in better understanding storm behavior and potential impacts.