Part 3 - BUFR

Note

BUFR and Geopoints data are represented as Bufr and Geopoints objects in Metview.

Setup

Navigate into the 3_bufr folder within Metview.

Examine the BUFR file

Right-click and examine the supplied BUFR file obs.bufr, bringing up the BUFR Examiner tool.

Have a look around to see what’s in the file, then find the key airTemperatureAt2M in the Data Tree tab. Right-click and Copy item to get this key name into the clipboard.

Filtering the BUFR file

We will now extract the 2m temperature values from the file. Create a new Observation Filter icon and edit it, setting the following parameters:

Parameter	Value	Notes
Data	obs.bufr	Drop the obs.bufr icon into the editor box
Output	Geopoints
Parameter	airTemperatureAt2M	Can put ecCodes key names or numeric descriptors here

The editor should look like this:

Click OK, then examine the Obervation Filter icon. The filtering step will be executed automatically and the resulting Geopoints data will appear in the examiner. You could also right-click on the icon and save the data, or drop the icon into a Python script file and add a write() command to write it to disk.

There are various possible output formats, but the standard geopoints output is a text file that looks like this:

Now visualise the result of the Observation Filter and drop the supplied icons symbol_plotting and coast_dark into the plot window.

Scriptify it

Filter, write and plot

Create a new Python Script icon, edit it and drop the Observation Filter (and rename the variable to t2m; also, absolute paths can be shortened to relative paths), coast_dark and symbol_plotting icons into the editor. Add the following commands to the end of the script and run it:

mv.plot(coast_dark, t2m, symbol_plotting)
mv.write('obs_t2m.gpt', t2m)

Extract values and compute statistics

Create a copy of the script, remove the plotting commands and add the following lines. The first two return numpy arrays, the third line returns a pandas dataframe.

print(mv.latitudes(t2m))
print(mv.values(t2m))
df = t2m.to_dataframe()
print(df)
print(df.describe())

If you have extra time…

Have a look in the solutions folder - there is a script called field_values_at_gpts_locations.py. This does several things, including using geopoints as a set of locations to extract values from a GRIB field, computing the differences between geopoints and GRIB fields, and plotting the differences to a PDF file.