Generating probabilistic forecasts from convection-allowing ensembles using neighborhood approaches: A review and recommendations

Abstract

Neighborhood approaches? have been used in two primary ways to post-process and verify high-resolution ensemble output. While the two methods appear deceptively similar, they in fact define events over different spatial scales and yield fields with different interpretations: the first produces probabilities interpreted as likelihood of event occurrence at the grid-scale, while the second produces probabilities of event occurrence over spatial scales larger than the grid-scale. Unfortunately, some studies have confused the two methods, while others did not acknowledge multiple possibilities of neighborhood approach application and simply stated, ?a neighborhood approach was applied? without supporting details. Thus, this paper reviews applications of neighborhood approaches to convection-allowing ensembles in hopes of clarifying the two methods and their different event definitions. Then, using real data, we demonstrate how the two approaches can yield statistically significantly different objective conclusions about model performance, underscoring the critical need for thorough descriptions of how neighborhood approaches are implemented and events are defined. We conclude by providing some recommendations for application of neighborhood approaches to convection-allowing ensembles.