These exercises demonstrate some basic functionality of Metview, showing how to retrieve data from MARS, examine the data's structure, compute the differences between different data sets and visualise them.

First start Metview; at ECMWF, the command to use is metview4 (see Metview at ECMWF for details of Metview versions). You should see the main Metview desktop which looks something like Figure 1 (except that the tabbed area at the bottom will not be open).

In Metview, all operations can be performed via icons. Most icons are available in the tabbed drawers at the bottom of the Metview desktop.

You will create some icons yourself, but some are supplied for you - please download the following file:

and save it in your $HOME/metview directory. You should see it appear on your main Metview desktop, from where you can right-click on it, then choose execute to extract the files. You should now (after a few seconds) see a solutions folder which contains the solutions and also some additional icons required by these exercises. You should work in the main Metview desktop, not the solutions folder.

This exercise shows how to retrieve GRIB data from MARS, examine its structure, compute the differences between fields and visualise the data in various ways.

Retrieving the analysis data

To perform a MARS retrieval in Metview, click the Data Access tab at the bottom of the Metview desktop to open it, and drag the Mars Retrieval icon onto your Metview desktop; this will create a copy of the icon for you to customise. Rename the new icon to temperature_analysis by clicking on its name. Edit your icon (right-click & edit, see Figure 2) and set the following parameters:

To save these settings, click the Apply button at the bottom-left of the icon editor.

Inspecting the analysis data

Perform the data retrieval by choosing execute from the icon's context menu. The icon name should turn orange whilst the retrieval takes place, then green to indicate success (if the name turns red, then the retrieval failed and you should look in the output log, available from the output entry in the context menu). The data is now cached locally. To see what was retrieved, right-click examine the icon. This brings up Metview's GRIB Examiner tool (Figure 3). Here we can see that we retrieved six vertical levels of data; this is as expected if we look at the Levelist parameter in the icon editor.

Now visualise the data, again using the icon's context menu. You will see a map plot with the default contouring style in the Display Window (Figure 4). To zoom controls in the toolbar give control over the area selection.

To plot the data with shaded colours, go to the Visual Definitions drawer (if this drawer is not visible, you may have to either expand your Metview desktop window to the right, or else click on the dots which appear to the right of the last visible drawer). Drop the Contouring icon onto your Metview desktop, rename it shade and edit it, providing these parameters:

Save the icon settings (Apply) and drop this into the Display Window (re-visualise the data if you have closed the Display Window). The result should resemble Figure 5. Metview's Contouring icon provides much flexibility in choosing how to display gridded fields; this tutorial uses only simple colour schemes.

The fields can be visualised using different views. These can be defined by a set of icons such as Geographical View and Cross Section View. In the solutions folder are 2 pre-prepared view icons for you to try. Visualise the polar_stereo_europe icon and drop your data icon into the resulting Display Window. If you edit this view icon, you will see how to define a geographical view. Now close the Display Window and visualise your data in the same way with the the cross_section_example view (Figure 6). This icon defines a geographical line along which a cross section of the data is computed (remember that the data consists of a number of vertical levels). You can also drop your shade icon into the plot.

Retrieving the forecast data

Create a copy of your temperature_analysis icon (right-click, duplicate) and rename the copy to temperature_forecast. Edit this icon and set the following parameters:

The analysis data was valid for 3 days ago; this new icon retrieves a 48-hour forecast data generated 5 days ago, so it is also valid for 3 days ago. You don't need to separately execute and visualise the icon - if you visualise it, the data will automatically be retrieved first. The plot title will verify that this data is valid for the same date and time as the analysis data. It also contains the same set of vertical levels.

Compute the forecast-analysis difference

Create a new Simple Formula icon by taking a copy from the Macros icon drawer. Rename the copy to fc_an_diff. Edit the icon, ensure that the first tab is selected (F+G) and that the operator is minus ( - ). Drop your temperature_forecast icon into the Parameter 1 box, and drop temperature_analysis into the Parameter 2 box. Save the icon and visualise it. The difference will be computed and the result plotted. Note that all 6 fields in each data icon are used in the computation - the result is a set of 6 fields. The solutions folder contains two Contouring icons which can be used to show the differences: select both pos_shade and neg_shade with the mouse and drop them both together into the Display Window (see Figure 7). It is also possible to drop them one at a time, but they do not accumulate - one will replace the other.

Automating the whole procedure

Ensure that the difference fieldset is visualised with the contouring applied. To generate a Metview Macro script from this plot, click the Generate Macro button (also available from the File menu). A new Macro script will be generated - have a look at it to confirm that it contains code to retrieve all the data, compute the difference and plot the result. Run the macro to obtain the plot, either by using the Run button from the Macro Editor, or by selecting visualise from the icon's context menu). By default, the macro is written so that it will produce an interactive plot window; it will generate a PostScript file if it is run with the execute command, or if it is run from the command line:

metview4_new -b  <macro-name>

This exercise builds on Exercise 1, but uses observation data in BUFR format instead of analysis fields.

Retrieving the observation data

Create a new Mars Retrieval icon and rename it to obs. Edit it and set the following parameters in order to retrieve BUFR observation data from 3 days ago:

Retrieve the data and examine it. Metview's BUFR Examiner displays the contents of the BUFR data (Figure 8). Each message contains many measurements. If you visualise the data, you will see a standard display of synoptic observations. Figure 9 shows this, using the shaded_coastlines icon from the solutions folder (this plot has also been zoomed to show a smaller area).

Extracting the 2 metre temperature

Create a new Observation Filter icon from the Filters icon drawer, and rename it to filter_obs_t2m. With this icon we will extract just the 2m temperature into Metview's custom ASCII format for scattered geographical data - geopoints. Set these parameters:

Note that 012004 is the code for 'Dry bulb temperature at 2m'. If you examine this icon now, you will see the result: a table of geo-located temperature values. When you visualise the data, you will see that the actual values are plotted as text on the screen; we can do better than this. From the solutions folder, drop the coloured_markers icon into the Display Window. The shaded_coastlines icon may also help make the points easier to see (Figure 10).

Retrieving the forecast data

Create a new Mars Retrieval icon, rename it to t2m_forecast, and set these parameters:

The retrieved data is the 48-hour forecast made 5 days ago for 2-metre temperature.

Computing the forecast-observation difference

This is just the same as in Exercise 1, using a Simple Formula icon; create a new one and rename it to fc_obs_diff. Drop t2m_forecast into the Parameter 1 box, and filter_obs_t2m into the Parameter 2 box. Visualise the result - you will see that the result of a field minus a scattered geopoints data set is another geopoints data set. For each geopoint location, the interpolated value from the field was extracted before performing the computation. From the solutions folder, drop both the diff_symb_hot and the diff_symb_cold icons together into the plot in order to get a more graphical representation of the result.

Overlaying data in the same plot

To plot the forecast field together with the observation differences, do the following. Visualise t2m_forecast and drop the shade icon into the plot. Now drop fc_obs_diff into the plot, followed by (or with) diff_symb_hot and diff_symb_cold. The observation differences don't stand out well against the strongly coloured field, so drop shade_light into the plot to obtain something like Figure 11.