Python for 4-D Visualization of Air Quality Data

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Monday, 5 January 2015: 5:15 PM
129B (Phoenix Convention Center - West and North Buildings)
Cesunica Ivey, Georgia Institute of Technology, Atlanta, GA; and S. Pearse, A. Norton, M. C. Barth, G. Pfister, and F. Flocke

Python programming language was used to develop a model rendering script for visualizing air quality data in VAPOR (Visualization and Analysis Platform for Ocean, Atmosphere and Solar Researchers). VAPOR is an interactive, 4-D tool for visualizing, animating, and rendering videos of various types of data, such as meteorological events and microbiological species. In this work, modeled WRF-CHEM data and aircraft measurements from NCAR's FRAPPE (Front Range Air Pollution and Photochemistry Experiment) flight campaign were rendered in the 4-D VAPOR space, along with cross-sectional views of the WRF-CHEM results and a bird's eye view of the flight path. Also rendered are time series plots of the aircraft's elevation, as well as the observed and modeled pollutant concentrations. Several python modules were used to generate the VAPOR model rendering file, including Nio for parsing NetCDF files, Numpy for algebraic operations, Proj4 for geographic projection tranformations, as well as Matplotlib for plotting time series. All programming and rendering was performed at the NCAR Wyoming Supercomputer Center (NWSC). Modeling was run on the Yellowstone 1.5 petaflops IBM iDataPlex Cluster, and visualization was run on the Geyser data analysis and visualization cluster, accessing data through the GLobally Accessible Data Environment (GLADE). This routine allows for simultaneous visualization of observed and modeled data in a 4-D environment, providing a platform for in-depth spatial and temporal characterization of air quality data.