To quantify the quality of an initialized forecast, it is useful to judge it against some simple
climpred currently supports a persistence forecast, but future releases
will allow computation of other reference forecasts. Consider opening a
Pull Request to get it implemented more quickly.
Persistence Forecast: Whatever is observed at the time of initialization is forecasted to
persist into the forecast period [Jolliffe2012]. You can compute this by passing
reference='persistence' into the
.verify() method for
Damped Persistence Forecast: (Not Implemented) The amplitudes of the anomalies reduce in time exponentially at a time scale of the local autocorrelation [Yuan2016].
Climatology: (Not Implemented) The average values at the temporal forecast resolution (e.g., annual, monthly) over some long period, which is usually 30 years [Jolliffe2012].
Random Mechanism: (Not Implemented) A probability distribution is assigned to the possible
range of the variable being forecasted, and a sequence of forecasts is produced by taking a sequence
of independent values from that distribution [Jolliffe2012]. This would be similar to computing an
uninitialized forecast, using
PerfectModelEnsemble objects. For
HindcastEnsemble objects, an
uninitialized ensemble has to be added through
.add_uninitialized(...). This could be, for
example, output from a Large Ensemble. For
PerfectModelEnsemble objects, one can run
.generate_uninitialized() which uses a bootstrapping approach to create an uninitialized
Jolliffe, Ian T., and David B. Stephenson, eds. Forecast verification: a practitioner’s guide in atmospheric science. John Wiley & Sons, 2012.
Yuan, Xiaojun, et al. “Arctic sea ice seasonal prediction by a linear Markov model.” Journal of Climate 29.22 (2016): 8151-8173.