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Han-Wei Shen, OSU

Application

• Climate modeling by Dr. Ruby Leung and Dr. Samson Hagos at PNNL

Simulation Goal: Understand MJO

• A complex cloud system in multiple scales over the Indian and Pacific oceans
• An important weather phenomenon related to the tropical intra-seasonal change

Requirement: Time-varying Feature Tracking

• Visualizing the movement of MJO-related quantities (eg. cloud population and precipitation)
  • Understand how MJO is formed
  • Understand how MJO is related to different physical quantities
• Feature identification to filter the major cloud movement of MJO

Challenges

• Large data size generated by high resolution simulations
• Various temporal scales of MJO
  • make traditional techniques that perform per time step analysis and visualization ineffective
  • require global analysis over time other than local feature tracking

Time-Varying Data Analysis with Time Activity Curves

Technology

• Robust global time-varying feature descriptors
  • Model the time-varying feature based on the change of value over time at each location
• Visualize complex time-varying phenomena with detailed multivariate  temporal trend analysis

Result/Impact

• Flexible interface to allow scientists to interact with data at their desktop
• Replace tedious viewing of animations with more efficient visualization of spatio-temporal features in still images
• Assist in summarizing and verifying major temporal trends such as MJO in extreme scale data sets

(Top): TAC-based (Time Activity Curve) region clustering (Bottom): Clustering TACs from all 2D location.

Linked views between TAC-based overview and virtual globe. (Top Left): TACs per longitude as Hovmoller diagram. (Top Right) Cloud animation with Feature Tracking