This article explains how to extract data from a 3 dimension NetCDF file using different options and save the output as a CSV (comma separated variables) file.
You are expected to have installed python 2.7 or later, and the CDS API on a Linux machine before you continue.
First option : Python Script
The first option is to use a python script (below). The script allows you to covert data from NetCDF in two different ways, as explained in the workflow below:
- Retrieve data with the CDS API and store as a netCDF4 file in the working directory.
- Extract the variable from the NetCDF file and get the dimensions (i.e. time, latitudes and longitudes)
- Extract each time as a 2D pandas DataFrame and write it to the CSV file
- Write the data as a table with 4 columns: time, latitude, longitude, value
Please note that the netCDF file could contain an additional dimension when there is a mix of ERA5 and ERA5T data. For more details please have a look at the following link: ERA5 CDS requests which return a mixture of ERA5 and ERA5T data
Second option : Panoply
A second option is to convert the data using the NASA 'Panoply' software. User can find the option under File → Export data → As CSV. The data are saved in the file maintaining the structure of the lot/lan matrix, but different times are divided by an empty row.
Third option : Windows users
A third option to convert the data from NetCDF to CSV, for Windows users, is download and install netcdf4excel. The plug-in opens directly NetCDF files in Microsoft Excel maintaining conventions for the NetCDF variables. Please see the link for more details: http://netcdf4excel.github.io/.
For Unix users, there are others options provided by some common NetCDF software packages. Please the links for more details:
- ncdump → http://manpages.ubuntu.com/manpages/xenial/man1/ncdump-hdf.1.html
- cdo → https://code.mpimet.mpg.de/projects/cdo/wiki/Tutorial
- ncks → https://www.unidata.ucar.edu/support/help/MailArchives/netcdf/msg08405.html