Comparison of the diurnal precipitation cycle in convection-resolving and non-convection-resolving mesoscale models

The diurnal cycles of rainfall in 5-km grid-spacing convection-resolving and 22-km grid-spacing non-convection -resolving configurations of the Weather Research and Forecasting (WRF) model are compared to see if significant improvements can be obtained by using fine enough grid spacing to explicitly resolve convection. Diurnally averaged Hovmoller diagrams, spatial correlation coefficients computed in Hovmoller space, equitable threat scores (ETSs), and biases for forecasts conducted from 1 April to 25 July 2005 over a large portion of the central United States are used for the comparisons. A subjective comparison using Hovmoller diagrams of diurnally averaged rainfall show that the diurnal cycle representation in the 5-km configuration is clearly superior to that in the 22-km configuration during forecast hours 24-48. The superiority of the 5-km configuration is validated by much higher spatial correlation coefficients than in the 22-km configuration. During the first 24 forecast hours the 5-km model forecasts appear to be more adversely affected by model "spinup" processes than the 22-km model forecasts, and it is less clear, subjectively, which configuration has the better diurnal cycle representation, although spatial correlation coefficients are slightly higher in the 22-km configuration. ETSs in both configurations have diurnal oscillations with relative maxima occurring in both configurations at forecast hours corresponding to 0000-0300 LST, while biases also have diurnal oscillations with relative maxima (largest errors) in the 22-km (5-km) configuration occurring at forecast hours corresponding to 1200 (1800) LST. At all forecast hours, ETSs from the 22-km configuration are higher than those in the 5-km configuration. This inconsistency with some of the results obtained using the aforementioned spatial correlation coefficients reinforces discussion in past literature that cautions against using "traditional" verification statistics, such as ETS, to compare high- to low-resolution forecasts.