Verification of High-Resolution Banded Snowfall Forecasts

Location: National Center for Atmospheric Research, Boulder, Foothills Lab Building 2, Room 1001
Jacob Radford, North Carolina State University

Narrow regions of intense snowfall, also known as snowbands, present hazardous travel conditions due to rapid onset, high precipitation rates, and limited visibility. A promising approach to operational prediction of snowbands is the application of convection-allowing models such as the High-Resolution Rapid Refresh (HRRR). However, to the best of our knowledge, no attempt has been made to verify the skill of such high-resolution forecasts. We have adapted an objective snowband definition to allow for algorithmic detection of bands in both model-simulated and observed base radar reflectivity fields. Comparison between these fields will then enable us to assess band predictability by the HRRR.

Our automated identification method is based upon a thresholding procedure combined with Real-Time Mesoscale Analysis (RTMA) temperature data. Similar studies have utilized a set reflectivity threshold, such as 20, 25, or 30 dBZ, to identify precipitation features. In contrast, we define a snowband as a reflectivity region some level above the background reflectivity. Calibration with a manual classification yielded a “best” definition of 1.25 standard deviations above the background reflectivity. This distribution-based threshold allows for improved detection of bands of varying intensities and reduces biases associated with differences between model-simulated and observed reflectivity fields.

419 hourly reflectivity images containing bands were identified in the 2015-16, 16-17, and 17-18 winter seasons. The POD and FAR by the HRRR are approximately 35% and 65%, respectively. However, allowing for small timing discrepancies drastically improves performance, with a POD and FAR of approximately 65% and 40%, respectively. In order to perform an object-oriented verification, we plan to apply the Method for Object-based Diagnostic Evaluation (MODE) to directly compare the salient features of forecasted and observed bands such as location, shape, and orientation.