Abstract

Spatial verification methods for feature- or object-based approaches are becoming more prevalent owing to increases in both model resolution and the demand for identification of specific features in storms. Yet further investigation is needed to determine which spatial verification methods and sp... Show moreSpatial verification methods for feature- or object-based approaches are becoming more prevalent owing to increases in both model resolution and the demand for identification of specific features in storms. Yet further investigation is needed to determine which spatial verification methods and specific configurations work best for certain forecast parameters, storm types, or regions of interest. This study investigates the use of Method for Object-based Diagnostic Evaluation -- Time Domain (MODE-TD) configurations to identify objects for the purpose of evaluating both mesoscale convective systems (MCSs) and localized storms through verification of precipitation accumulation. Unlike other statistical evaluation tools, MODE-TD has two horizontal spatial dimensions and time extending into the vertical spatial dimension which creates a 3D image, producing a more complete spatial and temporal view of the data. Weather Research and Forecasting (WRF) model forecasts for two cases in 2015 were selected to run with a variety of configurations of MODE-TD for analysis. These were then compared against Multi-Radar Multi-Sensor observations of precipitation. With an increase in convolution radius (hereafter R) and convolution threshold (hereafter T), fewer objects were identified -- especially for coarser and smaller objects -- and they tended to initiate later in time. For MCSs, a medium R-medium T combination is recommended whereas a medium R-small T combination is suggested for local storms. The results of this study will be applied in a verification study of the 2015 and 2016 Short Term Explicit Prediction program forecast experiment to evaluate model performance related to predicting heavy rainfall as well as storm propagation and evolution. Show less