Comparing forecast systems with multiple correlation decomposition based on partial correlation

摘要

The multiple correlation and/or regression information that two competing forecast systems have on the same observations is decomposed into four components, adapting the method of multivariate information decomposition of Williams and Beer (2010), Wibral et?al. (2015), and Lizier et?al. (2018). Their concept is to divide source information about a target into total, (target) redundant or shared, and unique information from each source. It is applied here to the comparison of forecast systems using classic regression. Additionally, non-target redundant or shared information is newly defined that resumes the redundant information of the forecasts which is not observed. This provides views that go beyond classic correlation differences. These five terms share the same units and can be directly compared to put prediction results into perspective. The redundance terms in particular provide a new view. All components are given as maps of explained variance on the observations and for the non-target redundance on the models, respectively. Exerting this concept to lagged damped persistence is shown to be related to directed information entropy. To emphasize the benefit of the toolkit on all timescales, two analysis examples are provided. Firstly, two forecast systems of the German decadal prediction system of “Mittelfristige Klimaprognose”, namely the pre-operational version and a special version using ensemble Kalman filter for the ocean initialization, are compared. The analyses reveal the clear added value of the latter and provide an as yet unseen map of their non-target redundance. Secondly, 4 d lead forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) are compared to a simple autoregressive and/or damped persistence model. The analysis of the information partition on this timescale shows that interannual changes in damped persistence, seen as target redundance changes between forecasts and damped persistence models, are balanced by associated changes in the added value of the dynamic forecasts in the extratropics but not in the tropics.