diagnostic.functions.calc_metrics_dt

calc_metrics_dt(dt_mod: DataTree, da_obs: Dataset, metrics=None, pss_binwidth=None)

Calculate statistical performance metrics for model data against observed data.

This function computes various metrics between the model data stored in the DataTree object dt_mod and the observed data da_obs. Default metrics include Mean Bias, Mean Absolute Error (MAE) at different percentiles, Root Mean Square Error (RMSE), Spearman Correlation, and Perkins Skill Score (PSS).

Parameters:

dt_modDataTree

A DataTree containing the model data for different members. The function loops through each member to calculate the metrics.

da_obsxr.DataSet

The observed data to compare against the model data.

metricsdict, optional

A dictionary containing the names of the metrics to calculate and the corresponding functions. Default is the below specified metrics.

pss_binwidthfloat, optional

The bin width to use for the Perkins Skill Score (PSS) calculation. If not provided, the optimal bin width is calculated.a

Returns:

df_metricpd.DataFrame

A DataFrame containing the calculated metrics and corresponding rank per metric for each member and variable in the data tree.

Metrics:

• Mean Bias

• Mean Absolute Error

• MAE at 90th Percentile

• MAE at 99th Percentile

• MAE at 10th Percentile

• MAE at 1st Percentile

• Root Mean Square Error

• Spearman Correlation

• Perkins Skill Score