Limitations of the High-Resolution Rapid Refresh Gust Algorithm

Abstract

ABSTRACT: Accurate wind and gust forecasts are crucial in operational meteorology, yet there is a distinct gap in the literature regarding the verification of operational gust forecasts. In the present work, we assess the performance of the High-Resolution Rapid Refresh\u27s (HRRR) gust potential, verifying it against the Automated Surface Observing System\u27s (ASOS) observed gusts across the Continental United States (CONUS). The HRRR gust\u27s diurnal cycle reveals a significant underprediction of the gust during daylight hours and a slight overprediction of the gust during hours of darkness. The daytime underprediction undermines the assumption that forecasts represent a potential that tends to overpredict the gust. These underpredictions result from the gust algorithm\u27s failure to appropriately respond to surface roughness and dominant land-use type. Each vegetation type, including its associated surface roughness, is found to have a distinct relationship with the observed gust that is not reflected in the forecasts. We also question the gust algorithm\u27s reliance on planetary boundary layer (PBL) diagnostics. It is found that no relationship exists between the predicted PBL height and that of the forecast gust while one does exist with the observed gust. We also consider possible replacements or additions to the PBL diagnostics, such as sensible heat flux and surface lapse rate. Both predicted sensible heat fluxes and unstable lapse rates have a moderate negative correlation with gust forecast bias, suggesting they have information that can be used to improve the algorithm. The result of this work is an understanding of the limitations of the current algorithm so that a new and better one can be implemented