To quantify the quality of an initialized forecast, it is useful to judge it against some simple baseline forecast.
climpred currently supports a persistence forecast, but future releases will allow computation of other baseline 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 directly via
compute_persistence() or as a method of
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
|[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.|