A New Measure of Ensemble Central Tendency

Jie Feng
University of Oklahoma

  2 July, Noon, in 2155


The mean of an ensemble of forecasts is traditionally defined as the Arithmetic Mean (AM) of all ensemble members. In the past two decades, this product gained significant use in weather forecasting. As a statistical construct, AM offers a nonlinear filtering of unpredictable forecast features across ensemble members that is reflected in a Root-Mean-Square (RMS) forecast error below that of individual forecasts. This is achieved at the cost of smearing out features that in different members appear at different positions and with different shapes and amplitudes.

In the proposed Developmental Testbed Center (DTC) Visitor Project, we will develop an algorithm and software for an alternative ensemble mean that we call Feature-based Mean (FM). In FM, all forecast features appear at the mean of their position in the individual members, represented with an amplitude that is the mean amplitude of features aligned in all members. Preliminary results show that the FM retains more small-scale features and larger amplitudes than the AM. In addition, the FM can reduce about 10% RMS error for short to mid-term forecasts of extreme events relative to the AM. After the development of the algorithm, real-time FM graphics will be demonstrated to Weather Prediction Center (WPC) forecasters and the FM software will be contributed to the DTC software repository.