Metview's documentation is now on readthedocs!
Download the icons for this session from the link below. Create a sub-directory called training inside your Metview home directory, $HOME/metview, and save the .tar.gz file there. Then, from Metview, navigate to this folder and right-click on the tar.gz icon; choose Extract to uncompress the files into their folder. Work from there. The main instructions are written for participants of the Data analysis and visualisation using Metview training course at ECMWF. Any files to be copied from the file system will also be downloadable from this page.

  File Modified
ZIP Archive data 2.tar.gz Sep 15, 2016 by Iain Russell


Metview has much functionality for meteorological data types stored in ECMWF's MARS archive, for example GRIB, BUFR and ODB. But not all data comes in these formats. Therefore Metview has facilities to handle various other data types, which we will explore here.

Visualiser Icons

Some formats, such as GRIB, are easy to visualise in Metview - just right-click, Visualise. This is because they are quite constrained in their contents and have enough standardised meta-data for a program to understand how they should be plotted. Some other formats, such as netCDF and tables of ASCII data are not easily interpreted for automatic plotting (which variables/columns should be selected and what do they represent?). Metview introduces the concept of the Visualiser icon, which we will use in some of the following examples.


NetCDF is a binary file format for storing multi-dimensional arrays of data and enjoys wide academic usage.

Examining netCDF

Right-click on the supplied icon and choose examine to see its structure. It consists of multi-dimensional variables, each of which has its own set of attributes; the file also has a set of global attributes. The actual fields are t2m and d2m - find their long_name attributes to see what these represent.

Visualising netCDF

Create a new NetCDF Visualiser icon. Edit it and drop the icon into the NetCDF Data field. Set the following parameters:

Netcdf Plot TypeGeo Matrix
Netcdf Latitude Variablelatitude
Netcdf Longitude Variablelongitude
Netcdf Value Variablet2m

Save the changes, and visualise this new icon. See how the settings in the visualiser icon correspond to the variable names in the data. Now visualise another field from the same file. Use the supplied shading_20_levels icon on the plots.

Extracting netCDF meta-data

Write the following code into a new Macro (you can exclude the comments):

# read the netCDF file and print its list of variables
nc = read("")
vars = variables(nc)
print(vars)   #  we could also do:  print(variables(nc))

# set the current variable to be t2m and print its attributes
setcurrent(nc, 't2m')
atts = attributes(nc)

Run the macro - this is what it should print:

ATTRIBUTES(scale_factor:0.001611,add_offset:254.569370,missing_value:-32767,units:K,long_name:2 metre temperature)

The variables() function returns a list of all the variable names in the netCDF file.

Most of the netCDF functions work on the current variable, set in the setcurrent() function. The attributes() function returns a definition - a set of named members, similar to a Python dictionary, relevant to the current variable. A definition's elements can be accessed either using the 'dot' operator, e.g. atts.units,  or using indexing notation, e.g. atts["units"].

Adapt this macro so that it plots the t2m field and gives it a title based on its long_name attribute. Here are some hints:

  • drop your NetCDF Visualiser icon into the Macro Editor
  • also drop the Contouring icon
  • create a Text Plotting icon and enter a random title string, then drop this into the Macro Editor
  • replace the value to the right of text_line_1 with the value of the long_name attribute
  • add a plot() command which contains the NetCDF Visualiser variable, the Contouring variable and the Text Plotting variable
  • feel free to add the units attribute to the title as well, using the ampersand operator & to concatenate parts of the string

Computations on netCDF data

Simple computations between netCDFs can be performed in a similar way to fieldsets, but they are only performed on the current variable for each netCDF. The folder contains two netCDF files: and, representing a 12-hour and a 36-hour forecast valid at the same time. Write a small macro which reads both files, sets their current variable to t2m and computes the difference. The last line should look something like:

diff = fc_36 - fc_12

The variable diff is also a netCDF variable - confirm with this line: "print(type(diff))". Its contents should be identical to the first netCDF in the computation (fc_36), but with the values of its t2m variable updated to be the differences between the two fields. Adapt some of the code from the previous exercise to plot the difference field (and use the supplied Contouring icons pos_shade and neg_shade).

Note that in these netCDF files, the data values are scaled in the netCDF file. The actual values for the t2m variable are encoded as 16-bit integers, but they have scale_factor and add_offset attributes which Metview applies by default.

We can see this by extracting the values. Try the following macro, which will print the 'real world' values from t2m:

# read the netCDF file
nc = read("")

# set the current variable to be t2m and print its values
setcurrent(nc, 't2m')
vals = values(nc)
print('max: ', maxvalue(vals))
print('min: ', minvalue(vals))

Now add the following line before the call to values() :

netcdf_auto_scale_values(0) # 1 means 'on', 0 means 'off'

Now the results should look different and will reflect the values as they are packed in the file.

Try something similar with the time variable:

# select time as the current variable and print its values
setcurrent(nc, 'time')
times = values(nc)

The result is a list of date variables. These will be explained in more detail in the session Handling Time in Metview.

To get the 'packed' values for this variable, put this line before the call to values() :

netcdf_auto_translate_times(0)  # 1 means 'on', 0 means 'off'


ASCII Table Data

Metview incorporates functionality to read, process and visualise data stored in ASCII table files, including the commonly-used CSV (comma-separated value) format.

Visualising ASCII table data

Look at the supplied file t2_20120304_1400_1200.csv. This is a standard CSV file, with a header row at the top, followed by one row per observation, one column per field.

. . .

A CSV file can have any number of columns, but this is a simple example.

To plot the data, we need to tell Metview which columns contain the coordinates and which contain the values. Create a new Table Visualiser icon and edit it. Drop the CSV icon into the Table Data field and set the following parameters:

Table Plot TypeGeo Points
Table Longitude VariableLon
Table Latitude VariableLat
Table Value VariableT2m

Notice that this icon contains several parameters at the bottom which allow you to read differently-formatted ASCII table files. The question-mark buttons beside the parameter names give brief information on what they mean. The defaults are set up to read a standard CSV file, so we don't need to touch these parameters in this example.

Visualise this icon to plot the data, and apply the supplied symb_colours icon to get a nicer plot.

Converting ASCII Table data to geopoints format

Although Metview has some functionality for handling this type of data in Macro, it can do much more with the geopoints format. Therefore, if the data points are in geographic coordinates, one useful exercise is to read one of these files and convert it to geopoints.

Create a new Table Reader icon - this is purely a helper icon which exists only to aid the generation of Macro code. Drop the CSV file into the Data field. We do not need to touch the other parameters since this is a standard CSV file.

Drop the icon into a new Macro to generate the code to read the file. Rename the resulting variable to data. The following lines of code will print some information about the data:

print('Num cols: ', count(data))
print('Col 4: ', name(data, 4))


Now we will create a new geopoints variable, and set its lats, lons and values to those from the CSV data.

First, use the values() function to extract arrays of lats, lons and T2m from the CSV data. These will be returned in variables of type vector - this is an in-memory array of double-precision numbers.

vector or list values( table, number )
vector or list values( table, string )

Returns the given column specified either by an index (starting at 1) or a name (only valid if the table has a header row). If the column type is number, a vector is returned; if it is string, then a list of strings is returned. If the column cannot be found, an error message is generated.

Next, find out how many values there are, using the count() function on one of the returned vectors.

Finally, the following code shows how to construct a simple geopoints variable using only these columns (i.e. it will be in XYV format):

geo = create_geo(num_vals, 'xyv')
geo = set_latitudes (geo, lats)
geo = set_longitudes(geo, lons)
geo = set_values    (geo, vals)

The macro can now write this to disk, return it to the user interface or process it further using all the available geopoints functions.

ASCII Lat / Lon Matrices

Have a look at the supplied Lat Lon Matrix file with the edit action. This is a simple text format for storing regularly-spaced geographical matrix data, which Metview can directly import. As soon as you do anything with this file (e.g. visualise or examine), Metview internally converts it into GRIB format (leaving the original file untouched). In this way, we can import such data into Metview and have access to all its GRIB/fieldset functionality.

Reading/Writing General ASCII Data to/from Disk

ASCII files that are not in Geopoints, ASCII Table or Lat/Long Matrix format can be read using the read() function. It will return a list of strings - one string will contain the contents of one line of the file. Look at the supplied text file, params.txt, and see that it contains a list of codes for meteorological parameters:


Create a new Macro and type the following code to read and parse this data:

lines = read('params.txt')
print(lines) # lines is a list of strings

params = lines[2] # take the second line; params is a string
param_list = parse(params, '/') # split the string into a list of strings

There are many more string functions available.

Now do the reverse: write this list of parameters into another text file. The new file should look exactly like the original. Here are some hints:

  • the write() function always takes a filename as its first argument, and it can take a string as its second argument
  • it always overwrites an existing file of the same name, so there exists another function, append() which will add your string to a new line on an existing file
  • so you will need to call write() once with the first line of text, and append() once with the list of parameters
  • the list of parameters will need to be flattened out into a string with '/' as the separator - this will need to be done in a loop with a string variable initialised to '', and each element added with the & operator
  • the global variable newline can be used to add a newline character between the lines


ODB stands for Observational DataBase and is developed at ECMWF to manage very large observational data volumes through the ECMWF IFS/4DVAR-system. The data structure of an ODB database can be seen as a table of variables called columns. Right-click examine the ODB Database icon AMSUA.odb to see a list of the variables in the data. The Data tab provides access to the actual data itself. ODB data can be filtered using ODB/SQL queries. The supplied ODB Filter icon contains an ODB/SQL query to retrieve certain columns of data. Edit it - note that this pre-prepared icon is using the AMSUA.odb icon as its data input. Look at the ODB Query field to get an idea of what data will be filtered. Now close the editor and examine the icon to see the filtered subset of data it has produced. The ODB Visualiser icon tb_plot tells Metview which columns of data to use for the visualisation; visualise it and apply the symb_colours icon to obtain a nice plot.

There is a dedicated tutorial for handling ODB data in Metview on the Tutorials page.

Extra Work


Modify your first netCDF macro which plots the t2m variable and make it compute the temperature in degrees Celcius by subtracting 273.15 from it before plotting.

Optimisations to file writing

The last ASCII example could be made more optimal, which could be important if dealing with large amounts of data:

  • in fact, it could be done with a single write() function if we just build up a string representing the whole file with newline characters between lines
  • if writing many many lines, there is another syntax which avoids multiple file open and close operations:
    • fh = file('output.txt')  # open a file handle
      for i = 1 to 100 do
          write(fh, 'Line ' & i & newline)
      end for
      fh = 0 # close the file handle


Visualise different columns of data in the supplied ODB file.

See if you can write a macro which extracts lat, lon and value columns into vectors and creates a new geopoints variable from the data.