Generation of Ensemble Mean Precipitation Forecasts from Convection-allowing Models

Methods for generating ensemble mean precipitation forecasts from convection-allowing model (CAM) ensembles based on a simple average of all members at each grid-point can have limited utility because of amplitude reduction and over-prediction of light precipitation areas caused by averaging complex spatial fields with strong gradients and high amplitude features.  To combat these issues with the simple ensemble mean, a method known as probability matching is commonly used to replace the ensemble mean amounts with amounts sampled from the distribution of ensemble member forecasts, which results in a field that has a bias equal to the average bias of the ensemble members.  Thus, the probability matched mean (PM-mean, hereafter) is viewed as a better representation of the ensemble members, and previous studies find that it is more skillful than any of the individual members.  

Herein, using nearly a year of data from a CAM-based ensemble running in real-time at the National Severe Storms Laboratory, we show that the superior performance of the PM-mean is actually an artifact of the spatial redistribution of precipitation amounts that occur when the PM-mean is computed over a relatively large domain.  Specifically, the PM-mean enlarges big areas of heavy precipitation and shrinks or even eliminates smaller ones.  An alternative approach for the PM-mean is developed that restricts the grid-points used to those within a specified radius-of-influence.  The new approach has an improved spatial representation of precipitation and is found to perform more skillfully than the PM-mean when using neighborhood-based verification metrics.