Advances in Using Python in the Atmospheric and Oceanic Sciences

- Indicates paper has been withdrawn from meeting
- Indicates an Award Winner
Monday, 5 January 2015: 2:30 PM-4:00 PM
Host: Fifth Symposium on Advances in Modeling and Analysis Using Python
Chair:  Mary Haley, NCAR, Boulder, CO

This session covers the breadth of recent advances in using Python for data analysis, visualization, workflow integration, modeling, and teaching.

Toolbox for Evaluating Ensembles Using an Information Gain Measure
Hannah Aizenman, City College of New York, New York, NY; and M. Grossberg, I. Gladkova, and N. Krakauer

Handout (1.7 MB)

GeoJS: Web Geospatial Visualization Library for Climate and Geospatial Datasets
Aashish Chaudhary, Kitware, Clifton Park, NY; and C. Harris and J. D. Beezley

Parallel-UVCDAT / Python for Diurnal Cycle Analysis
Curt Covey, LLNL, Livermore, CA; and C. Doutriaux and D. Williams

Accessing NetCDF4 Data in Python
Ward Fisher, UCAR/NCAR, Boulder, CO

Dataflow of a Multiple Instrument On-Demand Processing Engine with Python and DPLKit
Joseph P. Garcia, Univ. of Wisconsin, Madison, WI; and E. Eloranta and R. K. Garcia

Ensemble Model Visualization and Decision Support Tools with Python
Steven J. Greybush, Penn State Univ., University Park, PA; and R. H. Grumm

Examples of Python-based Ensemble Displays for Decision Support
Richard Grumm, NOAA/NWSFO, State College, PA; and S. J. Greybush

SHARPpy: Fueling the Python Cult
Kelton T. Halbert, University of Oklahoma, Norman, OK; and W. G. Blumberg and P. Marsh
Manuscript (607.2 kB)

Handout (38.6 MB)

Cartopy and Tephi: Open source Python packages for visualising geospatial and thermodynamic data
Bill Little, Met Office, Exeter, United Kingdom; and R. Hattersley

Handout (3.0 MB)

Iris and pyugrid: Consistent access to structured and unstructured grids
Bill Little, Met Office, Exeter, United Kingdom; and R. Hattersley

Handout (2.0 MB)

Poster 406 will now be presented as 3.4A

Using Python as an Integrated Software Platform for the PACRAIN Program
Michael D. Klatt, University of Oklahoma, Norman, OK; and J. S. Greene and M. L. Morrissey

Handout (660.8 kB)

Computation, Analysis and Visualization of In-Situ and Remote Sensing Data using Python
Jared Rennie, Cooperative Institute for Climate and Satellites/North Carolina State University, Asheville, NC; and A. Buddenberg, K. Gassert, R. D. Leeper, L. E. Stevens, and S. E. Stevens

Handout (3.5 MB)

Accessing McIDAS ADDE Satellite Data Servers in Python
Jerrold O. Robaidek, CIMSS/Univ. of Wisconsin, Madison, WI; and R. Garcia, D. A. Santek, D. Parker, and D. Stettner

A Python-Based Plotter for Model-Derived Polarimetric Radar Variables
Timothy A. Supinie, CAPS/Univ. of Oklahoma, Norman, OK; and D. T. Dawson II and Y. Jung

Pyodec: Streamline and standardize methods for decoding non-structured data files
Joseph S. Young, University of Utah, Salt Lake City, UT