January 5, Feature Tracking in VR for Cumulus Cloud Life-Cycle Studies E. J. Griffith, F. H. Post, M. Koutek, T. Heus and H. J. J. Jonker 11 th Eurographics Workshop on Virtual Environments Eric Griffith

January 5, Motivation – Data Visualization “The purpose of computation is insight, not numbers.” - Richard Hamming Insight is facilitated by a human in the loop Data visualization puts the human in the loop

January 5, Motivation – Virtual Reality Why data visualization in VR? An extra visible dimension Improved perception High interactivity Dynamic viewpoint Dynamic data view Simplified, direct interaction with 3D data

January 5, Overview 1)Project Overview 2)Data Preprocessing 3)Interactive Visualization 4)Results 5)Conclusions

January 5, Overview I.Project Overview II.Data Preprocessing III.Interactive Visualization IV.Results V.Conclusions

January 5, Project Overview Cumulus cloud studies Largest source of uncertainty in climate models Use LES to explore cloud behavior in 3D Provide a clearer description of cloud dynamics Exploration in VR Interactive visualization of very large, time dependent data sets Meaningful visual representations of multivariate, 3D, time-dependent data sets

January 5, Large Eddy Simulation - LES Provides insight into flows in the Atmospheric Boundary Layer Solves for large-scale flows models small scale flows Used to simulate atmospheric conditions over tropical ocean Typically 6.4 x 6.4 x 3.2 km domains with 2 second time steps Grid sizes of produce 112 GB of data for 1 simulated hour

January 5, Cloud Life-Cycles What is the “typical” cloud life-cycle? How do the clouds behave in each stage of the life-cycle? What causes the transition between stages?

January 5, The First Step – Cloud Selection Suitable clouds must be selected for study The challenges: Very large data sets Unpredictable and dynamic cloud behavior Qualitative selection criteria

January 5, Overview I.Project Overview II.Data Preprocessing III.Interactive Visualization IV.Results V.Conclusions

January 5, Data Preprocessing The objective of preprocessing is to convert the data into a format that can be meaningfully visualized interactively It is divided into two stages: Cloud tracking Isosurface creation

January 5, Cloud Tracking Cloud tracking involves the detection and tracking of features, which are clouds Tracking is simplified through two key observations: Clouds move and develop slowly Clouds that merge or split are either the same cloud or uninteresting to atmospheric scientists Detection is combined with tracking, and they are done via a 4D connected components algorithm

January 5, Cloud Tracking

January 5, Cloud Tracking

January 5, Cloud Tracking

January 5, Cloud Tracking

January 5, Cloud Tracking

January 5, Cloud Tracking

January 5, Isosurface Creation Isosurface creation transforms the raw data into visually meaningful representations of the clouds The creation follows a 5 stage pipeline process

January 5, Isosurface Pipeline 1.Prepare the data for isosurface creation 2.Create initial triangle meshes with Marching Cubes 3.Identify which meshes correspond with which clouds 4.Refine the meshes with a series of filters 5.Convert the meshes into triangle strips

January 5, Isosurface Pipeline 1.Prepare the data for isosurface creation 2.Create initial triangle meshes with Marching Cubes 3.Identify which meshes correspond with which clouds 4.Refine the meshes with a series of filters 5.Convert the meshes into triangle strips

January 5, Isosurface Pipeline 1.Prepare the data for isosurface creation 2.Create initial triangle meshes with Marching Cubes 3.Identify which meshes correspond with which clouds 4.Refine the meshes with a series of filters 5.Convert the meshes into triangle strips

January 5, Isosurface Pipeline 1.Prepare the data for isosurface creation 2.Create initial triangle meshes with Marching Cubes 3.Identify which meshes correspond with which clouds 4.Refine the meshes with a series of filters 5.Convert the meshes into triangle strips

January 5, Isosurface Pipeline 1.Prepare the data for isosurface creation 2.Create initial triangle meshes with Marching Cubes 3.Identify which meshes correspond with which clouds 4.Refine the meshes with a series of filters 5.Convert the meshes into triangle strips

January 5, Overview I.Project Overview II.Data Preprocessing III.Interactive Visualization IV.Results V.Conclusions

January 5, Cloud Explorer Cloud Explorer is our prototype of a VR visualization environment for cloud data It has been designed to enable atmospheric scientists to identify suitable clouds for study

January 5, Cloud Explorer Cloud Explorer components: a)Cloud field b)Volume graph c)World-in-Miniature d)Buttons e)Time control panel

January 5, Cloud Explorer

January 5, Overview I.Project Overview II.Data Preprocessing III.Interactive Visualization IV.Results V.Conclusions

January 5, Results – Data Preprocessing The preprocessing results are in terms of cpu time, output size and composition, and data compression The data sets were processed twice: once for all clouds and once for “complete” clouds GB GB (256 x 256 x 160) x 2169 Total Sizeq l SizeGrid Size x Time Steps 1011 MB1600 MB2h 4m 58s 1836 MB2557 MB11h 38m 22s Avgerage Memory Usage Peak Memory Usage CPU Time 64 : 1384 : 1679 MB46,824,994 6 : 136 : 16.9 GB517,110,152 Data Compression Ratios (vs q l ) Output Size Number of Triangles

January 5, Results – Cloud Explorer Depending on the number and size of visible clouds, Cloud Explorer provides frame rates between 20 and 30 FPS in stereo Currently, a session with Cloud Explorer results in the set of grid cells for each interesting cloud This data is then post processed to analyze the properties of the selected clouds “Fluffiness”, velocity profiles, mass flux, etc

January 5, Results – Cloud Mass Flux

January 5, Overview I.Project Overview II.Data Preprocessing III.Interactive Visualization IV.Results V.Conclusions

January 5, Conclusions Data preprocessing can provide sufficient data reduction to allow interactive VR visualization VR visualization enables scientists to combine theoretical and observational considerations Feature tracking with VR visualization can help scientists make sense out of very large, time- dependent data sets In depth studies of cloud behavior are made possible through cloud selection in VR

January 5, Future Directions The primary goal is to increase the amount and quality of time atmospheric scientists can spend using Cloud Explorer for cloud studies Large data handling facilities (e.g. out-of-core and multiresolution) for arbitrarily sized data sets Enhanced selection and interaction methods Visual information about multiple variables More context information about selected clouds

January 5, Acknowledgements Netherlands Organization for Scientific Research Netherlands National Computer Facility at SARA René Molenaar

January 5, Questions