What is already known:
What this study adds:
3km grid spaced forecasts generated during the 2019 NOAA Hazardous Weather Testbed Spring Forecast Experiment (HWT SFE) by the Multiscale Data Assimilation and Predictability group at the University Ok- lahoma are verified using the Neighborhood Maximum Ensemble Probability (NMEP). The verification was first performed on variable HWT defined domains and later extended to a large fixed CONUS domain. 24- hour forecasts of hourly maximum composite radar reflectivity initialized at 0000 UTC on 26 days during the spring of 2019 were evaluated. Forecasts on the HWT domains were generally skilled albeit with a no- table over-forecasting bias, while forecasts on the fixed domain were generally poor. Cumulative Distribution Function (CDF) bias correction was performed in each domain and forecasts were reverified. Further, the fixed domain was segmented into sub-domains and a novel regional CDF (RCDF) bias correction approach was undertaken. CDF corrected forecasts on the fixed domain were still poorer than climatology, but signifi- cantly more skilled than without calibration. RCDF corrected forecasts on the fixed domain were significantly more skilled than CDF forecasts and were the only forecasts to exceed climatological skill. Synoptic pattern classification using Self Organizing Maps (SOMs) identified physically realistic synoptic patterns occurring over a ten-year climatology. na ̈ıvely using the SOM-derived synoptic classification to remove bias from me- teorologically similar synoptic flow regimes separately did not generally improve forecast skill compared to regime-blind bias correction, though an interesting exception is noted. Suggestions are made for improving the robustness of the regime-dependent calibration scheme.