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changes.mady.by.user Iain Russell

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changes.mady.by.user Sandor Kertesz

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If the MARS retrieval was successful the data is now cached locally. To save the ODB data from the cache to disk, right-click Save result on the Mars Retrieval icon and save as 'temp.odb'. A few seconds later an ODB Database icon with the given name will appear at the bottom of your folder.

Examine the ODB file

To see what was retrieved, right-click examine the icon. This brings up Metview's ODB Examiner tool.

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Here you can see the metadata (Columns tab) and the actual data values themselves as well (Data tab). Close the ODB Examiner.To save the ODB data from the cache to disk, right-click Save result on the Mars Retrieval icon and save as 'temp.odb'. A few seconds later an ODB Database icon with the given name will appear at the bottom of your folder.

Interactive Visualisation

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Right-click visualise the 'vis_temp' icon to generate the plot. Then drag the the provided Symbol Plotting, Coastlines, Legend and Text Plotting icons into the plot for further customisation (either one at a time, or all together). Keep the plot window open.

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Inspecting the Data Values in the Plot

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Now switch to the Values panel. This features a list showing all the plotted data.  In the bottom-left corner click on the button to activate the Data probe (this will appear in the plot). The probe is synchronised with the list. Try to drag it around in the plot, or change its position through the list. The Magnifier might help you position the Data probe more accurately.

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Python Examples

There are a few Python examples in the folder for you to study. Open and run these scripts.

plot_map.py

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This is the Python code to generate the same plot as we did interactively above. The title and the symbol plotting value range are automatically computed from the actual data values. In the script we:

  • used odb_visualiser() to generate the plot
  • called  odb_filter() (see ODB Filter) to extract additional data for the title
  • used the values() function to access a given ODB column as a numpy array.
plot_diff.py

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This script computes the the difference between the forecast fields stored in 'fc.grib' and our ODB observations. This is achieved by using the following steps:

  1. the ODB query is performed and the resulting data is converted into Geopoints
  2. the matching GRIB field is read and interpolated to the observation points
  3. the difference is computed between forecast and observation
to_pandas.py

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This script shows how to convert an ODB into a Pandas dataframe with the to_dataframe() function.
plot_wind.py

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This script shows how to plot wind data from ODB.

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