Feature-Relative Error Analysis

Traditionally, spatial forecast verification has been performed in an Eulerian sense, as features move through a fixed geographical region, which helps to identify errors in the forecast for specific areas. However, forecasters and model developers alike may gain valuable insight by knowing the spatial error patterns relative to a particular weather feature, that is, in a Lagrangian sense. For example, in this work, the 24 and 48 hour forecasts from the North American Mesoscale Forecast System (NAM) are analyzed to determine the forecast errors relative to extratropical cyclones during the 2013-14 winter season for portions of the United States east of the Rocky Mountains. The location of an extratropical cyclone is determined and then centered on a pre-defined grid which then moves with the cyclone. Error fields are produced and tested for statistical significance after first adjusting for spatial correlation and serial dependence. Errors found to be statistically significant can then be accounted for in a forecast or an area of further development for modelers. Preliminary results for this ongoing work will be presented.