During this webinar, we will provide an overview of the netCDF data format and show participants how to open, read, write and plot data that is in the netCDF format using R and Python with Jupyter Notebook.The use case will focus on the Daymet meteorological variables data product archived at the NASA Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center. (DAAC)

Detailed Info:
Analyzing or producing higherlevel gridded data products stored in the netCDF file format can be challenging for researchers not experienced with netCDF data. The first section of this webinar will provide an introduction to the netCDF file format. Tutorials, demonstrated in R, will provide methods to open, read, write, and plot data from netCDF data files, and export them to other formats. The second section of the webinar will demonstrate tools available from the ORNL DAAC for working with the netCDFformatted Daymet data. The Daymet dataset provides daily gridded meteorological variables over the content of North America at a 1 km x 1 km spatial resolution.

Introductory level netCDF processing techniques will be demonstrated using netCDF Operators (NCO), GDAL, and Python, including operations that are of interest to ecological applications. These techniques, although demonstrated using Daymet data, should be broadly applicable to similarly structured data in netCDF file format.

To access additional R & Python tutorials,
https://daac.ornl.gov/workshops/NetCD...

For information about Daymet, https://daymet.ornl.gov
To learn more about NASA ORNL DAAC data, information, services and tools, https://daac.ornl.gov

To search and discover NASA Earth science data, https://search.earthdata.nasa.gov