Forecasts have to be verified against some product to evaluate their performance. However, when verifying against a product, there are many different ways one can compare the ensemble of forecasts. Here we cover the comparison options for both hindcast and perfect model ensembles. See terminology for clarification on the differences between these two experimental setups.
All high-level functions like
bootstrap() (for both
PerfectModelEnsemble objects) take a
comparison='' keyword to select the comparison style. See below for a detailed
description on the differences between these comparisons.
In hindcast ensembles, the ensemble mean forecast (
comparison='e2o') is expected to
perform better than individual ensemble members (
comparison='m2o') as the chaotic
component of forecasts is expected to be suppressed by this averaging, while the memory
of the system sustains. [Boer2016]
keyword: 'e2o', 'e2r'
Compare the ensemble mean forecast to the verification data for a
keyword: 'm2o', 'm2r'
Compares each ensemble member individually to the verification data for a
Perfect Model Ensembles¶
In perfect-model frameworks, there are many more ways of verifying forecasts.
[Seferian2018] uses a comparison of all ensemble members against the
control run (
comparison='m2c') and all ensemble members against all other ensemble
comparison='m2m'). Furthermore, the ensemble mean forecast can be verified
against one control member (
comparison='e2c') or all members (
as done in [Griffies1997].
Compare all members to ensemble mean while leaving out the reference in
Compare all other member forecasts to a single member verification, which is the first member.
Compare all members to all others in turn while leaving out the verification
Compare ensemble mean forecast to single member verification.
The goal of a normalized distance metric is to get a constant or comparable value of typically 1 (or 0 for metrics defined as 1 - metric) when the metric saturates and the predictability horizon is reached (see metrics).
A factor is added in the normalized metric formula (see [Seferian2018]) to accomodate
different comparison styles. For example,
nrmse gets smalled in comparison
m2m by design, since the ensembe mean is always closer to individual members
than the ensemble members to each other. In turn, the normalization factor is
m2o. It is 1 for
Interpretation of Results¶
HindcastEnsemble skill is computed over all initializations
dim='init' of the
hindcast, the resulting skill is a mean forecast skill over all initializations.
PerfectModelEnsemble skill is computed over a supervector comprised of all
initializations and members, which allows the computation of the ACC-based skill
[Bushuk2018], but also returns a mean forecast skill over all initializations.
Compute over dimension¶
dim defines over which dimension a metric is computed. We can
apply a metric over all dimensions from the initialized dataset expect
The resulting skill is then
reduced by this
dim. Therefore, applying a metric over
dim='member' creates a
skill for all initializations individually. This can show the initial conditions
dependence of skill. Likewise when computing skill over
'init', we get skill for
each member. This
dim argument is different from the
comparison argument which
just specifies how
observations are defined.
However, this above logic applies to deterministic metrics. Probabilistic metrics need
to be applied to the
member dimension and
comparison from [
'm2o' comparison in
dim should not contain
member when the comparison already computes ensemble means as in
You can also construct your own comparisons via the
Master class for all comparisons.
First, write your own comparison function, similar to the existing ones. If a
comparison should also be used for probabilistic metrics, make sure that
member dimension and
observations without. For deterministic metrics, return
observations with identical dimensions but without an identical comparison:
from climpred.comparisons import Comparison, _drop_members def _my_m2median_comparison(ds, metric=None): """Identical to m2e but median.""" observations_list =  forecast_list =  supervector_dim = 'member' for m in ds.member.values: forecast = _drop_members(ds, rmd_member=[m]).median('member') observations = ds.sel(member=m).squeeze() forecast_list.append(forecast) observations_list.append(observations) observations = xr.concat(observations_list, supervector_dim) forecast = xr.concat(forecast_list, supervector_dim) forecast[supervector_dim] = np.arange(forecast[supervector_dim].size) observations[supervector_dim] = np.arange(observations[supervector_dim].size) return forecast, observations
Then initialize this comparison function with
__my_m2median_comparison = Comparison( name='m2me', function=_my_m2median_comparison, probabilistic=False, hindcast=False)
Finally, compute skill based on your own comparison:
skill = compute_perfect_model(ds, control, metric='rmse', comparison=__my_m2median_comparison)
Once you come up with an useful comparison for your problem, consider contributing this
climpred, so all users can benefit from your comparison, see
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