Working with Multidimensional Scientific Data Using Python

The availability and scale of scientific data is increasing exponentially.  Fortunately, ArcGIS provides functionality for reading, managing, analyzing and visualizing scientific data stored in three formats widely used in the scientific community – netCDF, HDF and GRIB.  Using satellite and model derived Earth science data, this session will present examples of data management, and global scale spatial and temporal analyses in ArcGIS. Finally, the session will discuss and demonstrate how to extend the data management and analytical capabilities in ArcGIS using ArcPy and other Python packages.