We try to demonstrate the effectiveness of our 3D feature tracking algorithms on the dataset of a weather simulation. The dataset is courtesy of Dr. Bill Kuo and Dr. Wei Wang at Mesoscale Prediction Group, Mesoscale and Microscale Meteorology Division, National Center for Atmospheric Research, and the dataset is generated using the fifth generation of Penn State/NCAR Mesoscale Model Verion 2.
The first visualization is a standard isosurface technique rendered by Vis5D on cloud water at the threshold of 0.00036. It is difficult to follow the merging and bifurcations of the clouds over time and no quantitative information is available about the evolution.
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for
an mpeg movie.
To solve this problem, we apply our feature tracking algorithm to the datasets. Features are first extracted at the threshold of 0.00036 with the object segmentation routine. The segmentation routine has the option to filter small objects (in this example, we removed small regions with volume <5). The feature tracking algorithm is then used to automatically correlate all of the features over time. The tracking information is used to color objects, providing visual cues on object evolutions. (These images are rendered with a modified version of Vis5D, which incorporates our tracking information.) Volume rendering can also be performed.
For more information, please see the paper, "Tracking and Visualizing Turbulent 3D Features", by D. Silver and X. Wang, IEEE Transactions on Visualization and Computer Graphics, June 1997.
click for
an mpeg movie.
The properties of the orange object from t=2 is computed as a function of time with the objects tracked. Followings are some of the examples. Click on the images for larger version.
Integrated
Content (mass) of Object vs Time
Centroid
(x) of Object vs Time
Centroid
(y) of Object vs Time
