Reference Forecasts¶

To quantify the quality of an initialized forecast, it is useful to judge it against some simple reference forecast. 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 HindcastEnsemble and PerfectModelEnsemble objects.

Damped Persistence Forecast: (Not Implemented) The amplitudes of the anomalies reduce in time exponentially at a time scale of the local autocorrelation [Yuan2016].

$v_{dp}(t) = v(0)e^{-\alpha t}$

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 reference='uninitialized' in HindcastEnsemble and 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 equivalent.

References¶

Jolliffe2012(1,2,3): Jolliffe, Ian T., and David B. Stephenson, eds. Forecast verification: a practitioner’s guide in atmospheric science. John Wiley & Sons, 2012.
Yuan2016: Yuan, Xiaojun, et al. “Arctic sea ice seasonal prediction by a linear Markov model.” Journal of Climate 29.22 (2016): 8151-8173.