Metrics¶
All highlevel functions have an optional metric
argument that can be called to
determine which metric is used in computing predictability.
Note
We use the phrase ‘observations’ o
here to refer to the ‘truth’ data to which
we compare the forecast f
. These metrics can also be applied relative
to a control simulation, reconstruction, observations, etc. This would just
change the resulting score from quantifying skill to quantifying potential
predictability.
Internally, all metric functions require forecast
and observations
as inputs.
The dimension dim
is set internally by
compute_hindcast()
or
compute_perfect_model()
to specify over which dimensions
the metric
is applied. See Comparisons for more on the dim
argument.
Deterministic¶
Deterministic metrics assess the forecast as a definite prediction of the future, rather than in terms of probabilities. Another way to look at deterministic metrics is that they are a special case of probabilistic metrics where a value of one is assigned to one category and zero to all others [Jolliffe2011].
Correlation Metrics¶
The below metrics rely fundamentally on correlations in their computation. In the
literature, correlation metrics are typically referred to as the Anomaly Correlation
Coefficient (ACC). This implies that anomalies in the forecast and observations
are being correlated. Typically, this is computed using the linear
Pearson ProductMoment Correlation.
However, climpred
also offers the
Spearman’s Rank Correlation.
Note that the p value associated with these correlations is computed via a separate
metric. Use pearson_r_p_value
or spearman_r_p_value
to compute p values assuming
that all samples in the correlated time series are independent. Use
pearson_r_eff_p_value
or spearman_r_eff_p_value
to account for autocorrelation
in the time series by calculating the effective_sample_size
.
Pearson ProductMoment Correlation Coefficient¶
# Enter any of the below keywords in ``metric=...`` for the compute functions.
In [1]: print(f"\n\nKeywords: {metric_aliases['pearson_r']}")
Keywords: ['pearson_r', 'pr', 'acc', 'pacc']

climpred.metrics.
_pearson_r
(forecast, verif, dim=None, **metric_kwargs)[source]¶ Pearson productmoment correlation coefficient.
A measure of the linear association between the forecast and verification data that is independent of the mean and variance of the individual distributions. This is also known as the Anomaly Correlation Coefficient (ACC) when correlating anomalies.
where and represent the standard deviation of the forecast and verification data over the experimental period, respectively.
Note
Use metric
pearson_r_p_value
orpearson_r_eff_p_value
to get the corresponding p value.Parameters:  forecast (xarray object) – Forecast.
 verif (xarray object) – Verification data.
 dim (str) – Dimension(s) to perform metric over. Automatically set by compute function.
 weights (xarray object, optional) – Weights to apply over dimension. Defaults to
None
.  skipna (bool, optional) – If True, skip NaNs over dimension being applied to.
Defaults to
False
.
 Details:
minimum 1.0 maximum 1.0 perfect 1.0 orientation positive
See also
 xskillscore.pearson_r
 xskillscore.pearson_r_p_value
 climpred.pearson_r_p_value
 climpred.pearson_r_eff_p_value
Pearson Correlation p value¶
# Enter any of the below keywords in ``metric=...`` for the compute functions.
In [2]: print(f"\n\nKeywords: {metric_aliases['pearson_r_p_value']}")
Keywords: ['pearson_r_p_value', 'p_pval', 'pvalue', 'pval']

climpred.metrics.
_pearson_r_p_value
(forecast, verif, dim=None, **metric_kwargs)[source]¶ Probability that forecast and verification data are linearly uncorrelated.
Twotailed p value associated with the Pearson productmoment correlation coefficient (
pearson_r
), assuming that all samples are independent. Usepearson_r_eff_p_value
to account for autocorrelation in the forecast and verification data.Parameters:  forecast (xarray object) – Forecast.
 verif (xarray object) – Verification data.
 dim (str) – Dimension(s) to perform metric over. Automatically set by compute function.
 weights (xarray object, optional) – Weights to apply over dimension. Defaults to
None
.  skipna (bool, optional) – If True, skip NaNs over dimension being applied to.
Defaults to
False
.
 Details:
minimum 0.0 maximum 1.0 perfect 1.0 orientation negative
See also
 xskillscore.pearson_r
 xskillscore.pearson_r_p_value
 climpred.pearson_r
 climpred.pearson_r_eff_p_value
Effective Sample Size¶
# Enter any of the below keywords in ``metric=...`` for the compute functions.
In [3]: print(f"\n\nKeywords: {metric_aliases['effective_sample_size']}")
Keywords: ['effective_sample_size', 'n_eff', 'eff_n']

climpred.metrics.
_effective_sample_size
(forecast, verif, dim=None, **metric_kwargs)[source]¶ Effective sample size for temporally correlated data.
Note
Weights are not included here due to the dependence on temporal autocorrelation.
Note
This metric can only be used for hindcasttype simulations.
The effective sample size extracts the number of independent samples between two time series being correlated. This is derived by assessing the magnitude of the lag1 autocorrelation coefficient in each of the time series being correlated. A higher autocorrelation induces a lower effective sample size which raises the correlation coefficient for a given p value.
The effective sample size is used in computing the effective p value. See
pearson_r_eff_p_value
andspearman_r_eff_p_value
.where and are the lag1 autocorrelation coefficients for the forecast and verification data.
Parameters:  forecast (xarray object) – Forecast.
 verif (xarray object) – Verification data.
 dim (str) – Dimension(s) to perform metric over. Automatically set by compute function.
 skipna (bool, optional) – If True, skip NaNs over dimension being applied to.
Defaults to
False
.
 Details:
minimum 0.0 maximum ∞ perfect N/A orientation positive  Reference:
 Bretherton, Christopher S., et al. “The effective number of spatial degrees of freedom of a timevarying field.” Journal of climate 12.7 (1999): 19902009.
Pearson Correlation Effective p value¶
# Enter any of the below keywords in ``metric=...`` for the compute functions.
In [4]: print(f"\n\nKeywords: {metric_aliases['pearson_r_eff_p_value']}")
Keywords: ['pearson_r_eff_p_value', 'p_pval_eff', 'pvalue_eff', 'pval_eff']

climpred.metrics.
_pearson_r_eff_p_value
(forecast, verif, dim=None, **metric_kwargs)[source]¶ Probability that forecast and verification data are linearly uncorrelated, accounting for autocorrelation.
Note
Weights are not included here due to the dependence on temporal autocorrelation.
Note
This metric can only be used for hindcasttype simulations.
The effective p value is computed by replacing the sample size in the tstatistic with the effective sample size, . The same Pearson productmoment correlation coefficient is used as when computing the standard p value.
where is computed via the autocorrelation in the forecast and verification data.
where and are the lag1 autocorrelation coefficients for the forecast and verification data.
Parameters:  forecast (xarray object) – Forecast.
 verif (xarray object) – Verification data.
 dim (str) – Dimension(s) to perform metric over. Automatically set by compute function.
 skipna (bool, optional) – If True, skip NaNs over dimension being applied to.
Defaults to
False
.
 Details:
minimum 0.0 maximum 1.0 perfect 1.0 orientation negative
See also
 climpred.effective_sample_size
 climpred.spearman_r_eff_p_value
 Reference:
 Bretherton, Christopher S., et al. “The effective number of spatial degrees of freedom of a timevarying field.” Journal of climate 12.7 (1999): 19902009.
Spearman’s Rank Correlation Coefficient¶
# Enter any of the below keywords in ``metric=...`` for the compute functions.
In [5]: print(f"\n\nKeywords: {metric_aliases['spearman_r']}")
Keywords: ['spearman_r', 'sacc', 'sr']

climpred.metrics.
_spearman_r
(forecast, verif, dim=None, **metric_kwargs)[source]¶ Spearman’s rank correlation coefficient.
This correlation coefficient is nonparametric and assesses how well the relationship between the forecast and verification data can be described using a monotonic function. It is computed by first ranking the forecasts and verification data, and then correlating those ranks using the
pearson_r
correlation.This is also known as the anomaly correlation coefficient (ACC) when comparing anomalies, although the Pearson productmoment correlation coefficient (
pearson_r
) is typically used when computing the ACC.Note
Use metric
spearman_r_p_value
orspearman_r_eff_p_value
to get the corresponding p value.Parameters:  forecast (xarray object) – Forecast.
 verif (xarray object) – Verification data.
 dim (str) – Dimension(s) to perform metric over. Automatically set by compute function.
 weights (xarray object, optional) – Weights to apply over dimension. Defaults to
None
.  skipna (bool, optional) – If True, skip NaNs over dimension being applied to.
Defaults to
False
.
 Details:
minimum 1.0 maximum 1.0 perfect 1.0 orientation positive
See also
 xskillscore.spearman_r
 xskillscore.spearman_r_p_value
 climpred.spearman_r_p_value
 climpred.spearman_r_eff_p_value
Spearman’s Rank Correlation Coefficient p value¶
# Enter any of the below keywords in ``metric=...`` for the compute functions.
In [6]: print(f"\n\nKeywords: {metric_aliases['spearman_r_p_value']}")
Keywords: ['spearman_r_p_value', 's_pval', 'spvalue', 'spval']

climpred.metrics.
_spearman_r_p_value
(forecast, verif, dim=None, **metric_kwargs)[source]¶ Probability that forecast and verification data are monotonically uncorrelated.
Twotailed p value associated with the Spearman’s rank correlation coefficient (
spearman_r
), assuming that all samples are independent. Usespearman_r_eff_p_value
to account for autocorrelation in the forecast and verification data.Parameters:  forecast (xarray object) – Forecast.
 verif (xarray object) – Verification data.
 dim (str) – Dimension(s) to perform metric over. Automatically set by compute function.
 weights (xarray object, optional) – Weights to apply over dimension. Defaults to
None
.  skipna (bool, optional) – If True, skip NaNs over dimension being applied to.
Defaults to
False
.
 Details:
minimum 0.0 maximum 1.0 perfect 1.0 orientation negative
See also
 xskillscore.spearman_r
 xskillscore.spearman_r_p_value
 climpred.spearman_r
 climpred.spearman_r_eff_p_value
Spearman’s Rank Correlation Effective p value¶
# Enter any of the below keywords in ``metric=...`` for the compute functions.
In [7]: print(f"\n\nKeywords: {metric_aliases['spearman_r_eff_p_value']}")
Keywords: ['spearman_r_eff_p_value', 's_pval_eff', 'spvalue_eff', 'spval_eff']

climpred.metrics.
_spearman_r_eff_p_value
(forecast, verif, dim=None, **metric_kwargs)[source]¶ Probability that forecast and verification data are monotonically uncorrelated, accounting for autocorrelation.
Note
Weights are not included here due to the dependence on temporal autocorrelation.
Note
This metric can only be used for hindcasttype simulations.
The effective p value is computed by replacing the sample size in the tstatistic with the effective sample size, . The same Spearman’s rank correlation coefficient is used as when computing the standard p value.
where is computed via the autocorrelation in the forecast and verification data.
where and are the lag1 autocorrelation coefficients for the forecast and verification data.
Parameters:  forecast (xarray object) – Forecast.
 verif (xarray object) – Verification data.
 dim (str) – Dimension(s) to perform metric over. Automatically set by compute function.
 skipna (bool, optional) – If True, skip NaNs over dimension being applied to.
Defaults to
False
.
 Details:
minimum 0.0 maximum 1.0 perfect 1.0 orientation negative
See also
 climpred.effective_sample_size
 climpred.pearson_r_eff_p_value
 Reference:
 Bretherton, Christopher S., et al. “The effective number of spatial degrees of freedom of a timevarying field.” Journal of climate 12.7 (1999): 19902009.
Distance Metrics¶
This class of metrics simply measures the distance (or difference) between forecasted values and observed values.
Mean Squared Error (MSE)¶
# Enter any of the below keywords in ``metric=...`` for the compute functions.
In [8]: print(f"\n\nKeywords: {metric_aliases['mse']}")
Keywords: ['mse']

climpred.metrics.
_mse
(forecast, verif, dim=None, **metric_kwargs)[source]¶ Mean Sqaure Error (MSE).
The average of the squared difference between forecasts and verification data. This incorporates both the variance and bias of the estimator. Because the error is squared, it is more sensitive to large forecast errors than
mae
, and thus a more conservative metric. For example, a single error of 2°C counts the same as two 1°C errors when usingmae
. On the other hand, the 2°C error counts double formse
. See Jolliffe and Stephenson, 2011.Parameters:  forecast (xarray object) – Forecast.
 verif (xarray object) – Verification data.
 dim (str) – Dimension(s) to perform metric over. Automatically set by compute function.
 weights (xarray object, optional) – Weights to apply over dimension. Defaults to
None
.  skipna (bool, optional) – If True, skip NaNs over dimension being applied to.
Defaults to
False
.
 Details:
minimum 0.0 maximum ∞ perfect 0.0 orientation negative
See also
 xskillscore.mse
 Reference:
 Ian T. Jolliffe and David B. Stephenson. Forecast Verification: A Practitioner’s Guide in Atmospheric Science. John Wiley & Sons, Ltd, Chichester, UK, December 2011. ISBN 9781119960003 9780470660713. URL: http://doi.wiley.com/10.1002/9781119960003.
Root Mean Square Error (RMSE)¶
# Enter any of the below keywords in ``metric=...`` for the compute functions.
In [9]: print(f"\n\nKeywords: {metric_aliases['rmse']}")
Keywords: ['rmse']

climpred.metrics.
_rmse
(forecast, verif, dim=None, **metric_kwargs)[source]¶ Root Mean Sqaure Error (RMSE).
The square root of the average of the squared differences between forecasts and verification data.
Parameters:  forecast (xarray object) – Forecast.
 verif (xarray object) – Verification data.
 dim (str) – Dimension(s) to perform metric over. Automatically set by compute function.
 weights (xarray object, optional) – Weights to apply over dimension. Defaults to
None
.  skipna (bool, optional) – If True, skip NaNs over dimension being applied to.
Defaults to
False
.
 Details:
minimum 0.0 maximum ∞ perfect 0.0 orientation negative
See also
 xskillscore.rmse
Mean Absolute Error (MAE)¶
# Enter any of the below keywords in ``metric=...`` for the compute functions.
In [10]: print(f"\n\nKeywords: {metric_aliases['mae']}")
Keywords: ['mae']

climpred.metrics.
_mae
(forecast, verif, dim=None, **metric_kwargs)[source]¶ Mean Absolute Error (MAE).
The average of the absolute differences between forecasts and verification data. A more robust measure of forecast accuracy than
mse
which is sensitive to large outlier forecast errors.Parameters:  forecast (xarray object) – Forecast.
 verif (xarray object) – Verification data.
 dim (str) – Dimension(s) to perform metric over. Automatically set by compute function.
 weights (xarray object, optional) – Weights to apply over dimension. Defaults to
None
.  skipna (bool, optional) – If True, skip NaNs over dimension being applied to.
Defaults to
False
.
 Details:
minimum 0.0 maximum ∞ perfect 0.0 orientation negative
See also
 xskillscore.mae
 Reference:
 Ian T. Jolliffe and David B. Stephenson. Forecast Verification: A Practitioner’s Guide in Atmospheric Science. John Wiley & Sons, Ltd, Chichester, UK, December 2011. ISBN 9781119960003 9780470660713. URL: http://doi.wiley.com/10.1002/9781119960003.
Median Absolute Error¶
# Enter any of the below keywords in ``metric=...`` for the compute functions.
In [11]: print(f"\n\nKeywords: {metric_aliases['median_absolute_error']}")
Keywords: ['median_absolute_error']

climpred.metrics.
_median_absolute_error
(forecast, verif, dim=None, **metric_kwargs)[source]¶ Median Absolute Error.
The median of the absolute differences between forecasts and verification data. Applying the median function to absolute error makes it more robust to outliers.
Parameters:  forecast (xarray object) – Forecast.
 verif (xarray object) – Verification data.
 dim (str) – Dimension(s) to perform metric over. Automatically set by compute function.
 skipna (bool, optional) – If True, skip NaNs over dimension being applied to.
Defaults to
False
.
 Details:
minimum 0.0 maximum ∞ perfect 0.0 orientation negative
See also
 xskillscore.median_absolute_error
Normalized Distance Metrics¶
Distance metrics like mse
can be normalized to 1. The normalization factor
depends on the comparison type choosen. For example, the distance between an ensemble
member and the ensemble mean is half the distance of an ensemble member with other
ensemble members. See _get_norm_factor()
.
Normalized Mean Square Error (NMSE)¶
# Enter any of the below keywords in ``metric=...`` for the compute functions.
In [12]: print(f"\n\nKeywords: {metric_aliases['nmse']}")
Keywords: ['nmse', 'nev']

climpred.metrics.
_nmse
(forecast, verif, dim=None, **metric_kwargs)[source]¶ Normalized MSE (NMSE), also known as Normalized Ensemble Variance (NEV).
Mean Square Error (
mse
) normalized by the variance of the verification data.where is 1 when using comparisons involving the ensemble mean (
m2e
,e2c
,e2o
) and 2 when using comparisons involving individual ensemble members (m2c
,m2m
,m2o
). See_get_norm_factor()
.Note
climpred
uses a singlevalued internal reference forecast for the NMSE, in the terminology of Murphy 1988. I.e., we use a single climatological variance of the verification data within the experimental window for normalizing MSE.Parameters:  forecast (xarray object) – Forecast.
 verif (xarray object) – Verification data.
 dim (str) – Dimension(s) to perform metric over. Automatically set by compute function.
 weights (xarray object, optional) – Weights to apply over dimension. Defaults to
None
.  skipna (bool, optional) – If True, skip NaNs over dimension being applied to.
Defaults to
False
.  comparison (str) – Name comparison needed for normalization factor fac, see
_get_norm_factor()
(Handled internally by the compute functions)
 Details:
minimum 0.0 maximum ∞ perfect 0.0 orientation negative better than climatology 0.0  1.0 worse than climatology > 1.0  Reference:
 Griffies, S. M., and K. Bryan. “A Predictability Study of Simulated North Atlantic Multidecadal Variability.” Climate Dynamics 13, no. 7–8 (August 1, 1997): 459–87. https://doi.org/10/ch4kc4.
 Murphy, Allan H. “Skill Scores Based on the Mean Square Error and Their Relationships to the Correlation Coefficient.” Monthly Weather Review 116, no. 12 (December 1, 1988): 2417–24. https://doi.org/10/fc7mxd.
Normalized Mean Absolute Error (NMAE)¶
# Enter any of the below keywords in ``metric=...`` for the compute functions.
In [13]: print(f"\n\nKeywords: {metric_aliases['nmae']}")
Keywords: ['nmae']

climpred.metrics.
_nmae
(forecast, verif, dim=None, **metric_kwargs)[source]¶ Normalized Mean Absolute Error (NMAE).
Mean Absolute Error (
mae
) normalized by the standard deviation of the verification data.where is 1 when using comparisons involving the ensemble mean (
m2e
,e2c
,e2o
) and 2 when using comparisons involving individual ensemble members (m2c
,m2m
,m2o
). See_get_norm_factor()
.Note
climpred
uses a singlevalued internal reference forecast for the NMAE, in the terminology of Murphy 1988. I.e., we use a single climatological standard deviation of the verification data within the experimental window for normalizing MAE.Parameters:  forecast (xarray object) – Forecast.
 verif (xarray object) – Verification data.
 dim (str) – Dimension(s) to perform metric over. Automatically set by compute function.
 weights (xarray object, optional) – Weights to apply over dimension. Defaults to
None
.  skipna (bool, optional) – If True, skip NaNs over dimension being applied to.
Defaults to
False
.  comparison (str) – Name comparison needed for normalization factor fac, see
_get_norm_factor()
(Handled internally by the compute functions)
 Details:
minimum 0.0 maximum ∞ perfect 0.0 orientation negative better than climatology 0.0  1.0 worse than climatology > 1.0  Reference:
 Griffies, S. M., and K. Bryan. “A Predictability Study of Simulated North Atlantic Multidecadal Variability.” Climate Dynamics 13, no. 7–8 (August 1, 1997): 459–87. https://doi.org/10/ch4kc4.
 Murphy, Allan H. “Skill Scores Based on the Mean Square Error and Their Relationships to the Correlation Coefficient.” Monthly Weather Review 116, no. 12 (December 1, 1988): 2417–24. https://doi.org/10/fc7mxd.
Normalized Root Mean Square Error (NRMSE)¶
# Enter any of the below keywords in ``metric=...`` for the compute functions.
In [14]: print(f"\n\nKeywords: {metric_aliases['nrmse']}")
Keywords: ['nrmse']

climpred.metrics.
_nrmse
(forecast, verif, dim=None, **metric_kwargs)[source]¶ Normalized Root Mean Square Error (NRMSE).
Root Mean Square Error (
rmse
) normalized by the standard deviation of the verification data.where is 1 when using comparisons involving the ensemble mean (
m2e
,e2c
,e2o
) and 2 when using comparisons involving individual ensemble members (m2c
,m2m
,m2o
). See_get_norm_factor()
.Note
climpred
uses a singlevalued internal reference forecast for the NRMSE, in the terminology of Murphy 1988. I.e., we use a single climatological variance of the verification data within the experimental window for normalizing RMSE.Parameters:  forecast (xarray object) – Forecast.
 verif (xarray object) – Verification data.
 dim (str) – Dimension(s) to perform metric over. Automatically set by compute function.
 weights (xarray object, optional) – Weights to apply over dimension. Defaults to
None
.  skipna (bool, optional) – If True, skip NaNs over dimension being applied to.
Defaults to
False
.  comparison (str) – Name comparison needed for normalization factor fac, see
_get_norm_factor()
(Handled internally by the compute functions)
 Details:
minimum 0.0 maximum ∞ perfect 0.0 orientation negative better than climatology 0.0  1.0 worse than climatology > 1.0  Reference:
 Bushuk, Mitchell, Rym Msadek, Michael Winton, Gabriel Vecchi, Xiaosong Yang, Anthony Rosati, and Rich Gudgel. “Regional Arctic Sea–Ice Prediction: Potential versus Operational Seasonal Forecast Skill.” Climate Dynamics, June 9, 2018. https://doi.org/10/gd7hfq.
 Hawkins, Ed, Steffen Tietsche, Jonathan J. Day, Nathanael Melia, Keith Haines, and Sarah Keeley. “Aspects of Designing and Evaluating SeasonaltoInterannual Arctic SeaIce Prediction Systems.” Quarterly Journal of the Royal Meteorological Society 142, no. 695 (January 1, 2016): 672–83. https://doi.org/10/gfb3pn.
 Murphy, Allan H. “Skill Scores Based on the Mean Square Error and Their Relationships to the Correlation Coefficient.” Monthly Weather Review 116, no. 12 (December 1, 1988): 2417–24. https://doi.org/10/fc7mxd.
Mean Square Error Skill Score (MSESS)¶
# Enter any of the below keywords in ``metric=...`` for the compute functions.
In [15]: print(f"\n\nKeywords: {metric_aliases['msess']}")
Keywords: ['msess', 'ppp', 'msss']

climpred.metrics.
_msess
(forecast, verif, dim=None, **metric_kwargs)[source]¶ Mean Squared Error Skill Score (MSESS).
where is 1 when using comparisons involving the ensemble mean (
m2e
,e2c
,e2o
) and 2 when using comparisons involving individual ensemble members (m2c
,m2m
,m2o
). See_get_norm_factor()
.This skill score can be intepreted as a percentage improvement in accuracy. I.e., it can be multiplied by 100%.
Note
climpred
uses a singlevalued internal reference forecast for the MSSS, in the terminology of Murphy 1988. I.e., we use a single climatological variance of the verification data within the experimental window for normalizing MSE.Parameters:  forecast (xarray object) – Forecast.
 verif (xarray object) – Verification data.
 dim (str) – Dimension(s) to perform metric over. Automatically set by compute function.
 weights (xarray object, optional) – Weights to apply over dimension. Defaults to
None
.  skipna (bool, optional) – If True, skip NaNs over dimension being applied to.
Defaults to
False
.  comparison (str) – Name comparison needed for normalization factor fac, see
_get_norm_factor()
(Handled internally by the compute functions)
 Details:
minimum ∞ maximum 1.0 perfect 1.0 orientation positive better than climatology > 0.0 equal to climatology 0.0 worse than climatology < 0.0  Reference:
 Griffies, S. M., and K. Bryan. “A Predictability Study of Simulated North Atlantic Multidecadal Variability.” Climate Dynamics 13, no. 7–8 (August 1, 1997): 459–87. https://doi.org/10/ch4kc4.
 Murphy, Allan H. “Skill Scores Based on the Mean Square Error and Their Relationships to the Correlation Coefficient.” Monthly Weather Review 116, no. 12 (December 1, 1988): 2417–24. https://doi.org/10/fc7mxd.
 Pohlmann, Holger, Michael Botzet, Mojib Latif, Andreas Roesch, Martin Wild, and Peter Tschuck. “Estimating the Decadal Predictability of a Coupled AOGCM.” Journal of Climate 17, no. 22 (November 1, 2004): 4463–72. https://doi.org/10/d2qf62.
 Bushuk, Mitchell, Rym Msadek, Michael Winton, Gabriel Vecchi, Xiaosong Yang, Anthony Rosati, and Rich Gudgel. “Regional Arctic Sea–Ice Prediction: Potential versus Operational Seasonal Forecast Skill. Climate Dynamics, June 9, 2018. https://doi.org/10/gd7hfq.
Mean Absolute Percentage Error (MAPE)¶
# Enter any of the below keywords in ``metric=...`` for the compute functions.
In [16]: print(f"\n\nKeywords: {metric_aliases['mape']}")
Keywords: ['mape']

climpred.metrics.
_mape
(forecast, verif, dim=None, **metric_kwargs)[source]¶ Mean Absolute Percentage Error (MAPE).
Mean absolute error (
mae
) expressed as the fractional error relative to the verification data.Parameters:  forecast (xarray object) – Forecast.
 verif (xarray object) – Verification data.
 dim (str) – Dimension(s) to perform metric over. Automatically set by compute function.
 weights (xarray object, optional) – Weights to apply over dimension. Defaults to
None
.  skipna (bool, optional) – If True, skip NaNs over dimension being applied to.
Defaults to
False
.
 Details:
minimum 0.0 maximum ∞ perfect 0.0 orientation negative
See also
 xskillscore.mape
Symmetric Mean Absolute Percentage Error (sMAPE)¶
# Enter any of the below keywords in ``metric=...`` for the compute functions.
In [17]: print(f"\n\nKeywords: {metric_aliases['smape']}")
Keywords: ['smape']

climpred.metrics.
_smape
(forecast, verif, dim=None, **metric_kwargs)[source]¶ Symmetric Mean Absolute Percentage Error (sMAPE).
Similar to the Mean Absolute Percentage Error (
mape
), but sums the forecast and observation mean in the denominator.Parameters:  forecast (xarray object) – Forecast.
 verif (xarray object) – Verification data.
 dim (str) – Dimension(s) to perform metric over. Automatically set by compute function.
 weights (xarray object, optional) – Weights to apply over dimension. Defaults to
None
.  skipna (bool, optional) – If True, skip NaNs over dimension being applied to.
Defaults to
False
.
 Details:
minimum 0.0 maximum 1.0 perfect 0.0 orientation negative
See also
 xskillscore.smape
Unbiased Anomaly Correlation Coefficient (uACC)¶
# Enter any of the below keywords in ``metric=...`` for the compute functions.
In [18]: print(f"\n\nKeywords: {metric_aliases['uacc']}")
Keywords: ['uacc']

climpred.metrics.
_uacc
(forecast, verif, dim=None, **metric_kwargs)[source]¶ Bushuk’s unbiased Anomaly Correlation Coefficient (uACC).
This is typically used in perfect model studies. Because the perfect model Anomaly Correlation Coefficient (ACC) is strongly state dependent, a standard ACC (e.g. one computed using
pearson_r
) will be highly sensitive to the set of start dates chosen for the perfect model study. The Mean Square Skill Score (MSSS
) can be related directly to the ACC asMSSS = ACC^(2)
(see Murphy 1988 and Bushuk et al. 2019), so the unbiased ACC can be derived asuACC = sqrt(MSSS)
.where is 1 when using comparisons involving the ensemble mean (
m2e
,e2c
,e2o
) and 2 when using comparisons involving individual ensemble members (m2c
,m2m
,m2o
). See_get_norm_factor()
.Note
Because of the square root involved, any negative
MSSS
values are automatically converted to NaNs.Parameters:  forecast (xarray object) – Forecast.
 verif (xarray object) – Verification data.
 dim (str) – Dimension(s) to perform metric over. Automatically set by compute function.
 weights (xarray object, optional) – Weights to apply over dimension. Defaults to
None
.  skipna (bool, optional) – If True, skip NaNs over dimension being applied to.
Defaults to
False
.  comparison (str) – Name comparison needed for normalization factor
fac
, see_get_norm_factor()
(Handled internally by the compute functions)
 Details:
minimum 0.0 maximum 1.0 perfect 1.0 orientation positive better than climatology > 0.0 equal to climatology 0.0  Reference:
 Bushuk, Mitchell, Rym Msadek, Michael Winton, Gabriel Vecchi, Xiaosong Yang, Anthony Rosati, and Rich Gudgel. “Regional Arctic Sea–Ice Prediction: Potential versus Operational Seasonal Forecast Skill.” Climate Dynamics, June 9, 2018. https://doi.org/10/gd7hfq.
 Allan H. Murphy. Skill Scores Based on the Mean Square Error and Their Relationships to the Correlation Coefficient. Monthly Weather Review, 116(12):2417–2424, December 1988. https://doi.org/10/fc7mxd.
Murphy Decomposition Metrics¶
Metrics derived in [Murphy1988] which decompose the MSESS
into a correlation term,
a conditional bias term, and an unconditional bias term. See
https://wwwmiklip.dkrz.de/about/murcss/ for a walk through of the decomposition.
Standard Ratio¶
# Enter any of the below keywords in ``metric=...`` for the compute functions.
In [19]: print(f"\n\nKeywords: {metric_aliases['std_ratio']}")
Keywords: ['std_ratio']

climpred.metrics.
_std_ratio
(forecast, verif, dim=None, **metric_kwargs)[source]¶ Ratio of standard deviations of the forecast over the verification data.
where and are the standard deviations of the forecast and the verification data over the experimental period, respectively.
Parameters:  forecast (xarray object) – Forecast.
 verif (xarray object) – Verification data.
 dim (str) – Dimension(s) to perform metric over. Automatically set by compute functions.
 Details:
minimum 0.0 maximum ∞ perfect 1.0 orientation N/A  Reference:
Conditional Bias¶
# Enter any of the below keywords in ``metric=...`` for the compute functions.
In [20]: print(f"\n\nKeywords: {metric_aliases['conditional_bias']}")
Keywords: ['conditional_bias', 'c_b', 'cond_bias']

climpred.metrics.
_conditional_bias
(forecast, verif, dim=None, **metric_kwargs)[source]¶ Conditional bias between forecast and verification data.
where and are the standard deviations of the forecast and verification data over the experimental period, respectively.
Parameters:  forecast (xarray object) – Forecast.
 verif (xarray object) – Verification data.
 dim (str) – Dimension(s) to perform metric over. Automatically set by compute functions.
 Details:
minimum ∞ maximum 1.0 perfect 0.0 orientation negative  Reference:
Unconditional Bias¶
# Enter any of the below keywords in ``metric=...`` for the compute functions.
In [21]: print(f"\n\nKeywords: {metric_aliases['unconditional_bias']}")
Keywords: ['unconditional_bias', 'u_b', 'bias']
Simple bias of the forecast minus the observations.

climpred.metrics.
_unconditional_bias
(forecast, verif, dim=None, **metric_kwargs)[source]¶ Unconditional bias.
Parameters:  forecast (xarray object) – Forecast.
 verif (xarray object) – Verification data.
 dim (str) – Dimension(s) to perform metric over. Automatically set by compute functions.
 Details:
minimum ∞ maximum ∞ perfect 0.0 orientation negative  Reference:
Bias Slope¶
# Enter any of the below keywords in ``metric=...`` for the compute functions.
In [22]: print(f"\n\nKeywords: {metric_aliases['bias_slope']}")
Keywords: ['bias_slope']

climpred.metrics.
_bias_slope
(forecast, verif, dim=None, **metric_kwargs)[source]¶ Bias slope between verification data and forecast standard deviations.
where is the Pearson productmoment correlation between the forecast and the verification data and and are the standard deviations of the verification data and forecast over the experimental period, respectively.
Parameters:  forecast (xarray object) – Forecast.
 verif (xarray object) – Verification data.
 dim (str) – Dimension(s) to perform metric over. Automatically set by compute functions.
 Details:
minimum 0.0 maximum ∞ perfect 1.0 orientation negative  Reference:
Murphy’s Mean Square Error Skill Score¶
# Enter any of the below keywords in ``metric=...`` for the compute functions.
In [23]: print(f"\n\nKeywords: {metric_aliases['msess_murphy']}")
Keywords: ['msess_murphy', 'msss_murphy']

climpred.metrics.
_msess_murphy
(forecast, verif, dim=None, **metric_kwargs)[source]¶ Murphy’s Mean Square Error Skill Score (MSESS).
where represents the Pearson productmoment correlation coefficient between the forecast and verification data and represents the standard deviation of the verification data over the experimental period. See
conditional_bias
andunconditional_bias
for their respective formulations.Parameters:  forecast (xarray object) – Forecast.
 verif (xarray object) – Verification data.
 dim (str) – Dimension(s) to perform metric over. Automatically set by compute function.
 weights (xarray object, optional) – Weights to apply over dimension. Defaults to
None
.  skipna (bool, optional) – If True, skip NaNs over dimension being applied to.
Defaults to
False
.
 Details:
minimum ∞ maximum 1.0 perfect 1.0 orientation positive
See also
 climpred.pearson_r
 climpred.conditional_bias
 climpred.unconditional_bias
 Reference:
 https://wwwmiklip.dkrz.de/about/murcss/
 Murphy, Allan H. “Skill Scores Based on the Mean Square Error and Their Relationships to the Correlation Coefficient.” Monthly Weather Review 116, no. 12 (December 1, 1988): 2417–24. https://doi.org/10/fc7mxd.
Probabilistic¶
Probabilistic metrics include the spread of the ensemble simulations in their calculations and assign a probability value between 0 and 1 to their forecasts [Jolliffe2011].
Continuous Ranked Probability Score (CRPS)¶
# Enter any of the below keywords in ``metric=...`` for the compute functions.
In [24]: print(f"\n\nKeywords: {metric_aliases['crps']}")
Keywords: ['crps']

climpred.metrics.
_crps
(forecast, verif, **metric_kwargs)[source]¶ Continuous Ranked Probability Score (CRPS).
The CRPS can also be considered as the probabilistic Mean Absolute Error (
mae
). It compares the empirical distribution of an ensemble forecast to a scalar observation. Smaller scores indicate better skill.where is the cumulative distribution function (CDF) of the forecast (since the verification data are not assigned a probability), and H() is the Heaviside step function where the value is 1 if the argument is positive (i.e., the forecast overestimates verification data) or zero (i.e., the forecast equals verification data) and is 0 otherwise (i.e., the forecast is less than verification data).
Note
The CRPS is expressed in the same unit as the observed variable. It generalizes the Mean Absolute Error (MAE), and reduces to the MAE if the forecast is determinstic.
Parameters:  forecast (xr.object) – Forecast with member dim.
 verif (xr.object) – Verification data without member dim.
 metric_kwargs (xr.object) – If provided, the CRPS is calculated exactly with the assigned probability weights to each forecast. Weights should be positive, but do not need to be normalized. By default, each forecast is weighted equally.
 Details:
minimum 0.0 maximum ∞ perfect 0.0 orientation negative  Reference:
 Matheson, James E., and Robert L. Winkler. “Scoring Rules for Continuous Probability Distributions.” Management Science 22, no. 10 (June 1, 1976): 1087–96. https://doi.org/10/cwwt4g.
 https://www.lokad.com/continuousrankedprobabilityscore
See also
 properscoring.crps_ensemble
 xskillscore.crps_ensemble
Continuous Ranked Probability Skill Score (CRPSS)¶
# Enter any of the below keywords in ``metric=...`` for the compute functions.
In [25]: print(f"\n\nKeywords: {metric_aliases['crpss']}")
Keywords: ['crpss']

climpred.metrics.
_crpss
(forecast, verif, **metric_kwargs)[source]¶ Continuous Ranked Probability Skill Score.
This can be used to assess whether the ensemble spread is a useful measure for the forecast uncertainty by comparing the CRPS of the ensemble forecast to that of a reference forecast with the desired spread.
Note
When assuming a Gaussian distribution of forecasts, use default
gaussian=True
. If not gaussian, you may specify the distribution type, xmin/xmax/tolerance for integration (see xskillscore.crps_quadrature).Parameters:  forecast (xr.object) – Forecast with
member
dim.  verif (xr.object) – Verification data without
member
dim.  gaussian (bool, optional) – If
True
, assum Gaussian distribution for baseline skill. Defaults toTrue
.  cdf_or_dist (scipy.stats) – Function which returns the cumulative density of the
forecast at value x. This can also be an object with
a callable
cdf()
method such as ascipy.stats.distribution
object. Defaults toscipy.stats.norm
.  xmin (float) – Lower bounds for integration. Only use if not assuming Gaussian.
 xmax (float) –
 tol (float, optional) – The desired accuracy of the CRPS. Larger values will
speed up integration. If
tol
is set toNone
, bounds errors or integration tolerance errors will be ignored. Only use if not assuming Gaussian.
 Details:
minimum ∞ maximum 1.0 perfect 1.0 orientation positive better than climatology > 0.0 worse than climatology < 0.0  Reference:
 Matheson, James E., and Robert L. Winkler. “Scoring Rules for Continuous Probability Distributions.” Management Science 22, no. 10 (June 1, 1976): 1087–96. https://doi.org/10/cwwt4g.
 Gneiting, Tilmann, and Adrian E Raftery. “Strictly Proper Scoring Rules, Prediction, and Estimation.” Journal of the American Statistical Association 102, no. 477 (March 1, 2007): 359–78. https://doi.org/10/c6758w.
Example
>>> compute_perfect_model(ds, control, metric='crpss') >>> compute_perfect_model(ds, control, metric='crpss', gaussian=False, cdf_or_dist=scipy.stats.norm, xminimum=10, xmaximum=10, tol=1e6)
See also
 properscoring.crps_ensemble
 xskillscore.crps_ensemble
 forecast (xr.object) – Forecast with
Continuous Ranked Probability Skill Score Ensemble Spread¶
# Enter any of the below keywords in ``metric=...`` for the compute functions.
In [26]: print(f"\n\nKeywords: {metric_aliases['crpss_es']}")
Keywords: ['crpss_es']

climpred.metrics.
_crpss_es
(forecast, verif, **metric_kwargs)[source]¶ Continuous Ranked Probability Skill Score Ensemble Spread.
If the ensemble variance is smaller than the observed
mse
, the ensemble is said to be underdispersive (or overconfident). An ensemble with variance larger than the verification data indicates one that is overdispersive (underconfident).Parameters:  forecast (xr.object) – Forecast with
member
dim.  verif (xr.object) – Verification data without
member
dim.  weights (xarray object, optional) – Weights to apply over dimension. Defaults to
None
.  skipna (bool, optional) – If True, skip NaNs over dimension being applied to.
Defaults to
False
.
 Details:
minimum ∞ maximum 0.0 perfect 0.0 orientation positive underdispersive > 0.0 overdispersive < 0.0  Reference:
 Kadow, Christopher, Sebastian Illing, Oliver Kunst, Henning W. Rust, Holger Pohlmann, Wolfgang A. Müller, and Ulrich Cubasch. “Evaluation of Forecasts by Accuracy and Spread in the MiKlip Decadal Climate Prediction System.” Meteorologische Zeitschrift, December 21, 2016, 631–43. https://doi.org/10/f9jrhw.
 Range:
 perfect: 0
 else: negative
 forecast (xr.object) – Forecast with
Brier Score¶
# Enter any of the below keywords in ``metric=...`` for the compute functions.
In [27]: print(f"\n\nKeywords: {metric_aliases['brier_score']}")
Keywords: ['brier_score', 'brier', 'bs']

climpred.metrics.
_brier_score
(forecast, verif, **metric_kwargs)[source]¶ Brier Score.
The Mean Square Error (
mse
) of probabilistic twocategory forecasts where the verification data are either 0 (no occurrence) or 1 (occurrence) and forecast probability may be arbitrarily distributed between occurrence and nonoccurrence. The Brier Score equals zero for perfect (singlevalued) forecasts and one for forecasts that are always incorrect.where is the forecast probability of .
Note
The Brier Score requires that the observation is binary, i.e., can be described as one (a “hit”) or zero (a “miss”).
Parameters:  forecast (xr.object) – Forecast with
member
dim.  verif (xr.object) – Verification data without
member
dim.  func (function) – Function to be applied to verification data and forecasts
and then
mean('member')
to get forecasts and verification data in interval [0,1].
 Details:
minimum 0.0 maximum 1.0 perfect 0.0 orientation negative  Reference:
 Brier, Glenn W. Verification of forecasts expressed in terms of probability.” Monthly Weather Review 78, no. 1 (1950). https://doi.org/10.1175/15200493(1950)078<0001:VOFEIT>2.0.CO;2.
 https://www.nws.noaa.gov/oh/rfcdev/docs/ Glossary_Forecast_Verification_Metrics.pdf
See also
 properscoring.brier_score
 xskillscore.brier_score
Example
>>> def pos(x): return x > 0 >>> compute_perfect_model(ds, control, metric='brier_score', func=pos)
 forecast (xr.object) – Forecast with
Threshold Brier Score¶
# Enter any of the below keywords in ``metric=...`` for the compute functions.
In [28]: print(f"\n\nKeywords: {metric_aliases['threshold_brier_score']}")
Keywords: ['threshold_brier_score', 'tbs']

climpred.metrics.
_threshold_brier_score
(forecast, verif, **metric_kwargs)[source]¶ Brier score of an ensemble for exceeding given thresholds.
where is the cumulative distribution function (CDF) of the forecast distribution , is a point estimate of the true observation (observational error is neglected), denotes the Brier score and denotes the Heaviside step function, which we define here as equal to 1 for and 0 otherwise.
Parameters:  forecast (xr.object) – Forecast with
member
dim.  verif (xr.object) – Verification data without
member
dim.  threshold (int, float, xr.object) – Threshold to check exceedance, see properscoring.threshold_brier_score.
 Details:
minimum 0.0 maximum 1.0 perfect 0.0 orientation negative  Reference:
 Brier, Glenn W. Verification of forecasts expressed in terms of probability.” Monthly Weather Review 78, no. 1 (1950). https://doi.org/10.1175/15200493(1950)078<0001:VOFEIT>2.0.CO;2.
See also
 properscoring.threshold_brier_score
 xskillscore.threshold_brier_score
Example
>>> compute_perfect_model(ds, control, metric='threshold_brier_score', threshold=.5)
 forecast (xr.object) – Forecast with
Userdefined metrics¶
You can also construct your own metrics via the climpred.metrics.Metric
class.
Metric (name, function, positive, …[, …]) 
Master class for all metrics. 
First, write your own metric function, similar to the existing ones with required
arguments forecast
, observations
, dim=None
, and **metric_kwargs
:
from climpred.metrics import Metric
def _my_msle(forecast, observations, dim=None, **metric_kwargs):
"""Mean squared logarithmic error (MSLE).
https://peltarion.com/knowledgecenter/documentation/modelingview/buildanaimodel/lossfunctions/meansquaredlogarithmicerror."""
return ( (np.log(forecast + 1) + np.log(observations + 1) ) ** 2).mean(dim)
Then initialize this metric function with climpred.metrics.Metric
:
_my_msle = Metric(
name='my_msle',
function=_my_msle,
probabilistic=False,
positive=False,
unit_power=0,
)
Finally, compute skill based on your own metric:
skill = compute_perfect_model(ds, control, metric=_my_msle)
Once you come up with an useful metric for your problem, consider contributing this metric to climpred, so all users can benefit from your metric, see contributing.
References¶
[Jolliffe2011]  (1, 2) Ian T. Jolliffe and David B. Stephenson. Forecast Verification: A Practitioner’s Guide in Atmospheric Science. John Wiley & Sons, Ltd, Chichester, UK, December 2011. ISBN 9781119960003 9780470660713. URL: http://doi.wiley.com/10.1002/9781119960003. 
[Murphy1988]  Allan H. Murphy. Skill Scores Based on the Mean Square Error and Their Relationships to the Correlation Coefficient. Monthly Weather Review, 116(12):2417–2424, December 1988. https://doi.org/10/fc7mxd. 