Managing data and computational complexity for immersive wildfire visualization

Wildfires are a concern for communities throughout the world. They cause millions of dollars in damage and lead to loss of lives. The development of computational models to predict wildfire behavior is necessary to minimize wildfire damages and casualties. Visualizing the data generated from these computational models has many applications including training, strategic planning, data analysis, and model validation. The complexity of visualizing wildfire brings many challenges, further complicated by large datasets and specialized hardware used to drive immersive systems. This paper present methods for managing the large datasets and computational complexity involved in visualizing large wildfires in immersive environments.