rose-meta.conf

################################################################################
# Verification
################################################################################

[Verification]
ns=Verification
sort-key=sec-e
title=Verification

[template variables=RUN_METPLUS_POINT_STAT]
ns=Verification
description=Run METplus point stat in the workflow.
help=If True, it will enable the production of verification statistics against
    point observations (i.e. land-synop, sonde,...).
type=python_boolean
trigger=template variables=METPLUS_OPT_CONFIG_KEYS: True;
        template variables=METPLUS_OBS_DIR: True;
compulsory=true
sort-key=met1

[template variables=RUN_METPLUS_GRID_STAT]
ns=Verification
description=Run METplus grid stat in the workflow.
help=If True, it will enable the production of verification statistics against
    gridded data (i.e. radar, analysis,...).
type=python_boolean
trigger=template variables=METPLUS_OPT_CONFIG_KEYS: True;
        template variables=METPLUS_ANA_DIR: True;
compulsory=true
sort-key=met2

[template variables=RUN_METPLUS_ENSEMBLE_STAT]
ns=Verification
description=Run METplus ensemble stat in the workflow.
help=If True, it will enable the production of verification of ensemble statistics
    against point observations(i.e. land-synop, sonde,...).
type=python_boolean
trigger=template variables=METPLUS_OPT_CONFIG_KEYS: True;
        template variables=METPLUS_OBS_DIR: True;
compulsory=true
sort-key=met3

[template variables=METPLUS_OPT_CONFIG_KEYS]
ns=Verification
description=Which METplus configuration to run.
help=Selects the site/model specific configuration to use. The configuration must
    exist as restricted files in
    https://github.com/MetOffice/CSET-restricted-files/tree/main/app/{app_name}/file
type=quoted
value-hints="metoffice", "niwa", "nci-gadi"
sort-key=met4

[template variables=METPLUS_OBS_DIR]
ns=Verification
description=Path to directory containing observations in MET ASCII format.
type=quoted
sort-key=met5

[template variables=METPLUS_ANA_DIR]
ns=Verification
description=Path to directory containing analysis in MET netcdf format.
type=quoted
sort-key=met6

###################################

[template variables=SCORES_ALL]
ns=Verification
description=If selected, all scores shown below will be calculated.
            NOTE scores currently only available for model-model intercomparisons.
type=python_boolean
trigger=template variables=SCORES_SPATIAL_RMSE: False;
        template variables=SCORES_SPATIAL_MAE: False;
        template variables=SCORES_SPATIAL_AB: False;
        template variables=SCORES_TIMESERIES_RMSE: False;
        template variables=SCORES_TIMESERIES_MAE: False;
        template variables=SCORES_TIMESERIES_AB: False;
        template variables=SCORES_TIMESERIES_PC: False;
compulsory=true
sort-key=scoresspatial1


# Scores spatial plots
[Verification/ScoresSpatial]
ns=Verification/ScoresSpatial
sort-key=scoresspatial
title=Model vs model scores spatial

[template variables=SPATIAL_SCORES_FIELD_METHOD]
ns=Verification/ScoresSpatial
description=Select analysis method(s) for output mapped plots. Add all options required.
            Leave blank or set to "SEQ" (sequence) for plots each diagnostic output time.
            For time-collapsed outputs over each analysis period, set to "CASE", "MAX", "MIN" etc.
            Selecting "CASE" will compute the spatial scores metrics over the entire case study.
help=Quoted analysis methods. Settings should be based on available iris.analysis methods for collapsing cube dimensions.
type=python_list
compulsory=true
sort-key=sc-sp-0

[template variables=SCORES_SPATIAL_RMSE]
ns=Verification/ScoresSpatial
title=Root Mean Square Error
description=SCORES COUNTINOUS METRIC.
            Plots a 2D spatial plot of the RMSE between two models.
            The computation is implemented by the scores package.
            For the time coordinate, the method(s) set in
            SPATIAL_SCORES_FIELD_METHOD will be used.
help=This computes the metric using the scores package. Details of this can
     be found at https://scores.readthedocs.io/en/stable/included.html#continuous
type=python_boolean
compulsory=true
sort-key=sc-sp-1

[template variables=SCORES_SPATIAL_MAE]
ns=Verification/ScoresSpatial
title=Mean Absolute Error
description=SCORES COUNTINOUS METRIC.
            Plots a 2D spatial plot of the MAE between two models.
            The computation is implemented by the scores package.
            For the time coordinate, the method(s) set in
            SPATIAL_SCORES_FIELD_METHOD will be used.
help=This computes the metric using the scores package. Details of this can
     be found at https://scores.readthedocs.io/en/stable/included.html#continuous
type=python_boolean
compulsory=true
sort-key=sc-sp-2

[template variables=SCORES_SPATIAL_AB]
ns=Verification/ScoresSpatial
title=Additive Bias (Mean Error)
description=SCORES COUNTINOUS METRIC.
            Plots a 2D spatial plot of the Additive Bias between two models.
            The Additive Bias is also known as the Mean Error.
            The computation is implemented by the scores package.
            For the time coordinate, the method(s) set in
            SPATIAL_SCORES_FIELD_METHOD will be used.
help=This computes the metric using the scores package. Details of this can
     be found at https://scores.readthedocs.io/en/stable/included.html#continuous
type=python_boolean
compulsory=true
sort-key=sc-sp-3


###################################

# Scores timeseries plots
[Verification/ScoresTimeseries]
ns=Verification/ScoresTimeseries
sort-key=scorestimeseries
title=Model vs model scores timeseries

[template variables=SCORES_TIMESERIES_RMSE]
ns=Verification/ScoresTimeseries
title=Root Mean Square Error
description=SCORES COUNTINOUS METRIC.
            Plots a timeseries plot of the RMSE between two models.
            The computation is implemented by the scores package.
help=This computes the metric using the scores package. Details of this can
     be found at https://scores.readthedocs.io/en/stable/included.html#continuous
type=python_boolean
compulsory=true
sort-key=sc-ts-1

[template variables=SCORES_TIMESERIES_MAE]
ns=Verification/ScoresTimeseries
title=Mean Absolute Error
description=SCORES COUNTINOUS METRIC.
            Plots a timeseries plot of the MAE between two models.
            The computation is implemented by the scores package.
help=This computes the metric using the scores package. Details of this can
     be found at https://scores.readthedocs.io/en/stable/included.html#continuous
type=python_boolean
compulsory=true
sort-key=sc-ts-2

[template variables=SCORES_TIMESERIES_AB]
ns=Verification/ScoresTimeseries
title=Additive Bias (Mean Error)
description=SCORES COUNTINOUS METRIC.
            Plots a timeseries plot of the Additive Bias between two models.
            The Additive Bias is also known as the Mean Error.
            The computation is implemented by the scores package.
help=This computes the metric using the scores package. Details of this can
     be found at https://scores.readthedocs.io/en/stable/included.html#continuous
type=python_boolean
compulsory=true
sort-key=sc-ts-3

[template variables=SCORES_TIMESERIES_PC]
ns=Verification/ScoresTimeseries
title=Pearsons Correlation
description=SCORES COUNTINOUS METRIC.
            Plots a timeseries plot of the Pearson’s Correlation Coefficient between two models.
            The computation is implemented by the scores package.
help=This computes the metric using the scores package. Details of this can
     be found at https://scores.readthedocs.io/en/stable/included.html#continuous
type=python_boolean
compulsory=true
sort-key=sc-ts-4

[Verification/Scores Ensemble]
[template variables=SCORES_CRPS_FOR_ENSEMBLE]
ns=Verification/ScoresEnsemble
title=Scores Ensemble
description=Create a timeseries of the CRPS for comparing ensemble members to control.
type=python_boolean
compulsory=true
trigger=template variables=METHOD_FOR_CRPS: True;
        template variables=CONTROL_MEMBER;
sort-key=scores4

[template variables=METHOD_FOR_CRPS]
ns=Verification/ScoresEnsemble
title=Method for CRPS
description=What method CRPS for ensembles should use
help=ecdf assumes empirical CDF
    fair gives an approximated CRPS assuming that the ensemble values can be interpreted as a random sample from an underlying predictive distribution
values="ecdf", "fair"
value-titles=ecdf, fair
compulsory=true
sort-key=scores4a

[template variables=CONTROL_MEMBER]
ns=Verification/ScoresEnsemble
title=Control member for CRPS calculation
description=What index the control member is in the realization coordinate
help=Usually this should be set to 0, but in some case it may be 1.
type=integer
compulsory=true
sort-key=scores4b

[Verification/Scores vertical profiles]
ns=Verification/Scores vertical profiles
sort-key=scoresvertical
title=Scores vertical profiles

[Verification/Scores vertical profiles]
[template variables=SCORES_RMSE_VERTICAL_PROFILES]
ns=Verification/Scores vertical profiles
description=Create vertical profile RMSE plots for the specified level fields.
    RMSE profiles are calculated at each grid point for all timesteps within a case study at each pressure level.
type=python_boolean
compulsory=true
sort-key=scoresprofile1

[template variables=SCORES_RMSE_VERTICAL_PROFILES_SEQUENCE]
ns=Verification/Scores vertical profiles
description=Also produce one RMSE vertical profile per time step.
help=When True, the RMSE is calculated at each grid point and level for every
    time step, collapsing only the spatial (horizontal) dimensions. This produces
    a sequence of vertical RMSE profile plots, one per time step, in addition to
    the profile that is aggregated across the whole case study.

    When False (default), only the aggregated vertical RMSE profile is produced.
type=python_boolean
compulsory=true
#trigger=template variables=SCORES_RMSE_VERTICAL_PROFILES: True;
sort-key=scoresprofile2

[template variables=SCORES_ALL_MODEL_VS_OBS]
ns=Verification
description=If selected, all model vs obs scores shown below will be calculated.
type=python_boolean
trigger=template variables=SCORES_SPATIAL_RMSE_MODEL_VS_OBS: False;
        template variables=SCORES_TIMESERIES_RMSE_MODEL_VS_OBS: False;
compulsory=true
sort-key=scores1_model_vs_obs

##########################################################


# Model Vs Obs Scores timeseries plots
[Verification/ModelVsObsScoresTimeseries]
ns=Verification/ModelVsObsScoresTimeseries
sort-key=modelvsobsscorestimeseries
title=Model vs obs scores timeseries

[template variables=SCORES_TIMESERIES_RMSE_MODEL_VS_OBS]
ns=Verification/ModelVsObsScoresTimeseries
title=Root Mean Square Error Model Vs Obs
description=SCORES COUNTINOUS METRIC.
            Plots a timeseries plot of the RMSE between models and observations.
            The computation is implemented by the scores package.
help=This computes the metric using the scores package. Details of this can
     be found at https://scores.readthedocs.io/en/stable/included.html#continuous
type=python_boolean
compulsory=true
sort-key=sc-ts-1

#################################################

# Model Vs Obs Scores spatial plots
[Verification/ModelVsObsScoresSpatial]
ns=Verification/ModelVsObsScoresSpatial
sort-key=modelvsobsscoresspatial
title=Model vs obs scores spatial

[template variables=SPATIAL_SCORES_FIELD_METHOD_MODEL_VS_OBS]
ns=Verification/ModelVsObsScoresSpatial
description=Select analysis method(s) for output mapped plots. Add all options required.
            Leave blank or set to "SEQ" (sequence) for plots each diagnostic output time.
            For time-collapsed outputs over each analysis period, set to "CASE", "MAX", "MIN" etc.
            Selecting "CASE" will compute the spatial scores metrics over the entire case study.
help=Quoted analysis methods. Settings should be based on available iris.analysis methods for collapsing cube dimensions.
type=python_list
compulsory=true
sort-key=sc-sp-0_model_vs_obs

[template variables=SCORES_SPATIAL_RMSE_MODEL_VS_OBS]
ns=Verification/ModelVsObsScoresSpatial
title=Root Mean Square Error
description=SCORES COUNTINOUS METRIC.
            Plots a 2D spatial plot of the RMSE between two models.
            The computation is implemented by the scores package.
            For the time coordinate, the method(s) set in
            SPATIAL_SCORES_FIELD_METHOD will be used.
help=This computes the metric using the scores package. Details of this can
     be found at https://scores.readthedocs.io/en/stable/included.html#continuous
type=python_boolean
compulsory=true
sort-key=sc-sp-1_model_vs_obs


#####

# Categorical Scores metrics against observations.
[template variables=SCORES_CATEGORICAL_ENTRIES]
ns=Verification/Scores Categorical
description=List of variables, operators and thresholds
            i.e. ['<varname>,<operator>,<value>']
            See help for more information.
help=Supported operators:
     gt = greater than
     lt = less than
     Should be in format
     ['<varname>,<operator>,<value>','<varname2>,<operator2>,<value2>']
     which could be
     ['air_temperature,lt,280']
type=python_list
compulsory=true
sort-key=scoresPOD1

[Verification/Scores Categorical]
[template variables=SCORES_CATEGORICAL_POD]
ns=Verification/Scores Categorical
description=Compute Scores Probability of Detection.
type=python_boolean
compulsory=true
sort-key=scoresPOD2

[Verification/Scores Categorical]
[template variables=SCORES_CATEGORICAL_ETS]
ns=Verification/Scores Categorical
description=Compute Scores Equitable Threat Score.
type=python_boolean
compulsory=true
sort-key=scoresPOD3

[Verification/Scores Categorical]
[template variables=SCORES_CATEGORICAL_PFD]
ns=Verification/Scores Categorical
description=Compute Scores Probability of False Detection.
type=python_boolean
compulsory=true
sort-key=scoresPOD5