Hosted by: NASA EOSDIS and the ORNL DAAC
Date: August 30, 2017
Contact for the ORNL DAAC: support-ornl@earthdata.nasa.gov

Keywords: R, Python, netCDF, THREDDS, Web Service

Overview

Analyzing or producing higher-level 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 introduce 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 Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center (DAAC) for working with the netCDF-formatted Daymet data. The Daymet dataset provides daily gridded meteorological variables over the North American continent at a 1 km x 1 km spatial resolution. NetCDF processing techniques will be demonstrated using Python 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.

Source Data

Daily weather data: https://daymet.ornl.gov

Trends in vegetation greenness in the Arctic: https://doi.org/10.3334/ORNLDAAC/1275

Prerequisites

Python 2.7 or later. Python modules: netCDF, numpy, matplotlib

R Statistics Package

Sign up for a NASA Earthdata account: https://urs.earthdata.nasa.gov/users/new

Procedure

Links to R and Python procedures presented in this webinar:

How to open and work with NetCDF data in R: https://github.com/ornldaac/netCDF_data_in_R

Python Methodology to Derive a Climatology from netCDF Daily Data: https://github.com/ornldaac/daymet_netcdf_season-avg

Related Tutorials/Other e-learning content

More tutorials related to ORNL DAAC data and web services can be found at the ORNL DAAC's github site.