We utilized high temporal resolution, near-surface observations of sustained winds and gusts from two networks, the primarily airport-based Automated Surface Observing System (ASOS) and the New York State Mesonet (NYSM), to evaluate forecasts from the operational High-Resolution Rapid Refresh (HRRR) model, versions 3 and 4. Consistent with past studies, we showed the model has a high degree of skill in reproducing the diurnal variation of network-averaged wind speed of ASOS stations, but also revealed several areas where improvements could be made. Forecasts were found to be underdispersive, deficient in both temporal and spatial variability, with significant errors occurring during local nighttime hours in all regions and in forested environments for all hours of the day. This explained why the model overpredicted the network-averaged wind in the NYSM because much of that network’s stations are in forested areas. A simple gust parameterization was shown not only to have skill in predicting gusts in both networks but also to mitigate systemic biases found in the sustained wind forecasts.
Many users depend on forecasts from operational models and need to know their strengths, weaknesses, and limitations. We examined generally high-quality near-surface observations of sustained winds and gusts from the nationwide Automated Surface Observing System (ASOS) and the New York State Mesonet (NYSM) and used them to evaluate forecasts from the previous (version 3) and current (version 4) operational High-Resolution Rapid Refresh (HRRR) model for a selected month. Evidence indicated that the wind forecasts are excellent yet imperfect and areas for further improvement remain. In particular, we showed there is a high degree of skill in representing the diurnal variation of sustained wind at ASOS stations but insufficient spatial and temporal forecast variability and overprediction at night everywhere, in forested areas at all times of day, and at NYSM sites in particular, which are more likely to be sited in the forest. Gusts are subgrid even at the fine grid spacing of the HRRR (3 km) and thus must be parameterized. Our simple gust algorithm corrected for some of these systemic biases, resulting in very good predictions of the maximum hourly gust.
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