Application and research progress of the neighborhood method in weather forecasting
The traditional dichotomous contingency table test,which evaluates the objective performance of nu-merical weather prediction(NWP)based on the point-to-point matching between forecasted and observed events,has notable limitation when applied to high-resolution NWP or convection-allowing models(CAM).The neighborhood method addresses these limitations by relaxing the grid scale matching constraints between forecasted and observed events,making it particularly valuable for evaluating high-resolution numerical weather forecasts and the post-processing of objective probability forecasts.This paper systematically reviews the key ap-plications of the neighborhood method in weather forecasting,focusing on two key aspects:one is the verification of high-resolution numerical models using neighborhood method;and other is the neighborhood prob-ability or neighborhood probability of ensemble forecasts.First,the study outlines the verification frameworks of two neighborhood methods,"one-to-many"and"many-to-many",and discusses the data processing techniques associated with the neighborhood method,alongside the physical interpretation of common scoring matrices such as FBS(fractions brier score)and FSS(fractions skill score).It is concluded that,in addition to traditional di-chotomous contingency table-based verification metrics,the neighborhood method facilitates comparisons of fore-cast performance across multiple spatial and temporal scales.This enables the derivation of diagnostic metrics for NWP forecast performance based on scale changes,providing unique advantages.Second,it summarizes the fun-damental concepts and statistical meaning of the grid scale neighborhood probability and the neighborhood proba-bility at scales larger than the grid.Discussion focuses on expounding the algorithm workflow and internal mean-ing of neighborhood ensemble probability(NEP)forecast and neighborhood maximum ensemble probability(NMEP)forecast derived from ensemble forecasts.Third,by examining typical application cases,it analyzes the advantages,disadvantages and applicability of the neighborhood method and neighborhood ensemble probability.Results show that both NEP and NMEP enhance precipitation forecast scores.NEP performs better for large-scale and systematic precipitation forecasts,whereas NMEP is more effective for convective and extreme precipitation events.However,the selection of an appropriate neighborhood radius remains a critical technical challenge,as it is influenced by variations in underlying surface conditions and the optimal neighborhood scales of different NWP products.Finally,the paper discusses future directions for the application of the neighborhood method in weather forecasting.Promising areas of research and application include integrating neighborhood ensemble probability with the temporal dimension,developing metrics for the rare-event ensemble neighborhood probability,and ex-ploring synergies between the neighborhood method and artificial intelligence.These directions hold significant potential for advancing the utility and impact of the neighborhood method in weather forecasting.
neighborhood verificationfractions skill scoreneighborhood probabilityneighborhood ensemble probability
万方数据
维普
