Part 5 - NetCDF

Note

NetCDF data is represented by the NetCDF object in Metview.

Setup

Navigate into the 4_netcdf folder within Metview where you will find some data files and other icons.

Examine and visualise a geomatrix style netCDF file

Right-click on the file era5_2000_aug.nc and choose examine to see the structure of the file:

For more complete information, click on the NcDump tab.

You will need to tell Metview how to visualise this data, as there are multiple variables. Create a new NetCDF Visualiser icon, edit it and set the following parameters:

Parameter	Value
Netcdf Data	Drop the era5_2000_aug.nc icon into this box
Netcdf Plot Type	Geomatrix
Netcdf Latitude Variable	latitude
Netcdf Longitude Variable	longitude
Netcdf Value Variable	t2m

Save the icon and visualise it. For fun, drop the supplied icons contour_t2m and mollweide into the plot window to obtain the following:

Examine and visualise a geopoints style netCDF file

Examine the file madis-maritime.nc. We will plot the temperature variable. As you can see, there are 1-dimensional variables for temperature, latitude, and longitude. Create a copy of your previous NetCDF Visualiser icon and edit it as follows:

Parameter	Value
Netcdf Data	Right-click/remove the existing netCDF file from there, then drop madis-maritime.nc into this box
Netcdf Plot Type	Geo Points
Netcdf Latitude Variable	latitude
Netcdf Longitude Variable	longitude
Netcdf Value Variable	temperature

Visualise it to get a default plot. In the solutions folder is a script called gallery_example_nc_maritime_obs.py, which converts from Kelvin to Celcius and adds some stying to the plot (generated as PDF - simply remove the setoutput() command to get an on-screen visualisation).

Extract data and convert to pandas

Have a look in the solutions folder and edit and run the script netcdf_to_pandas.py. This shows how to extract some metadata from the previous netCDF file, and also some value arrays and convert into a pandas dataframe. The code is also here:

import metview as mv
import pandas as pd

nc = mv.read("madis-maritime.nc")

# print some global fields
print('Variables: \n', nc.variables())
print('Global attributes: \n', nc.global_attributes())

# extract certain variables - setcurrent() followed by values()
nc.setcurrent('latitude')
lats = nc.values()
nc.setcurrent('longitude')
lons = nc.values()
nc.setcurrent('temperature')
temps = nc.values()
print('temperature attributes: \n', nc.attributes())

# create a dictionary in order to convert to pandas
pddict = {'latitude'    : lats,
          'longitude'   : lons,
          'temperature' : temps}

df = pd.DataFrame(pddict)
print('Dataframe: \n', df)
print('temperature describe: \n', df.temperature.describe())