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.
Zonal mean profile of ta including a reference dataset.
1D profile of ta including a reference dataset.
Zonal mean pr including a reference dataset.
Hovmoeller plot (pressure vs. time) of ta including a reference dataset.
Hovmoeller plot (time vs. latitude) of tas including a reference dataset