These recipes and diagnostics allow plotting arbitrary preprocessor output, i.e., arbitrary variables from arbitrary datasets. In addition, a base class is provided that allows a convenient interface for all monitoring diagnostics.
Available recipes and diagnostics
Recipes are stored in recipes/monitor
Diagnostics are stored in diag_scripts/monitor/
It is recommended to use a vector graphic file type (e.g., SVG) for the output
files when running this recipe, i.e., run the recipe with the command line
--output_file_type=svg or use
output_file_type: svg in your
User configuration file.
Note that map and profile plots are rasterized by default.
rasterize_maps: false or
rasterize: false (see Recipe settings)
in the recipe to disable this.
A list of all possible configuration options that can be specified in the recipe is given for each diagnostic individually (see previous section).
Monitor configuration file
In addition, the following diagnostics support the use of a dedicated monitor configuration file:
This file is a yaml file that contains map and variable specific options in two
Each entry in
maps corresponds to a map definition.
maps: global: # Map name, choose a meaningful one projection: PlateCarree # Cartopy projection to use projection_kwargs: # Dictionary with Cartopy's projection keyword arguments. central_longitude: 285 smooth: true # If true, interpolate values to get smoother maps. If not, all points in a cells will get the exact same color lon: [-120, -60, 0, 60, 120, 180] # Set longitude ticks lat: [-90, -60, -30, 0, 30, 60, 90] # Set latitude ticks colorbar_location: bottom extent: null # If defined, restrict the projection to a region. Format [lon1, lon2, lat1, lat2] suptitle_pos: 0.87 # Title position in the figure.
Each entry in
variables corresponds to a variable definition.
Use the default entry to apply generic options to all variables.
variables: # Define default. Variable definitions completely override the default # not just the values defined. If you want to override only the defined # values, use yaml anchors as shown default: &default colors: RdYlBu_r # Matplotlib colormap to use for the colorbar N: 20 # Number of map intervals to plot bad: [0.9, 0.9, 0.9] # Color to use when no data pr: <<: *default colors: gist_earth_r # Define bounds of the colorbar, as a list of bounds: 0-10.5,0.5 # Set colorbar bounds, as a list or in the format min-max,interval extend: max # Set extend parameter of mpl colorbar. See https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.colorbar.html sos: # If default is defined, entries are treated as map specific option. # Missing values in map definitionas are taken from variable's default # definition default: <<: *default bounds: 25-41,1 extend: both arctic: bounds: 25-40,1 antarctic: bounds: 30-40,0.5 nao: &nao <<: *default extend: both # Variable definitions can override map parameters. Use with caution. bounds: [-0.03, -0.025, -0.02, -0.015, -0.01, -0.005, 0., 0.005, 0.01, 0.015, 0.02, 0.025, 0.03] projection: PlateCarree smooth: true lon: [-90, -60, -30, 0, 30] lat: [20, 40, 60, 80] colorbar_location: bottom suptitle_pos: 0.87 sam: <<: *nao lat: [-90, -80, -70, -60, -50] projection: SouthPolarStereo projection_kwargs: central_longitude: 270 smooth: true lon: [-120, -60, 0, 60, 120, 180]
Any, but the variables’ number of dimensions should match the ones expected by each plot.
Global climatology of tas.
Seasonal climatology of pr, with a custom colorbar.
Monthly climatology of sivol, only for March and September.
Timeseries of Niño 3.4 index, computed directly with the preprocessor.
Annual cycle of tas.
Timeseries of tas including a reference dataset.
Annual cycle of tas including a reference dataset.
Global climatology of tas including a reference dataset.
Vertical profile of ta including a reference dataset.