Analysis of Ensemble Mean Forecasts: The Blessings of High Dimensionality

Abstract

Abstract In weather and climate sciences ensemble forecasts have become an acknowledged community standard. It is often found that the ensemble mean not only has a low error relative to the typical error of the ensemble members but also that it outperforms all the individual ensemble members. We analyze ensemble simulations based on a simple statistical model that allows for bias and that has different variances for observations and the model ensemble. Using generic simplifying geometric properties of high-dimensional spaces we obtain analytical results for the error of the ensemble mean. These results include a closed form for the rank of the ensemble mean among the ensemble members and depend on two quantities: the ensemble variance and the bias both normalized with the variance of observations. The analytical results are used to analyze the GEFS reforecast where the variances and bias depend on lead time. For intermediate lead times between 20 and 100 h the two terms are both around 0.5 and the ensemble mean is only slightly better than individual ensemble members. For lead times larger than 240 h the variance term is close to 1 and the bias term is near 0.5. For these lead times the ensemble mean outperforms almost all individual ensemble members and its relative error comes close to −30%. These results are in excellent agreement with the theory. The simplifying properties of high-dimensional spaces can be applied not only to the ensemble mean but also to, for example, the ensemble spread.