| Abstract |
Strong wind events can cause severe economic loss and societal impacts. Peak winds over Wyoming and Colorado occur during the wintertime months, and in Part I of this two-part series, it was shown that the High Resolution Rapid Refresh (HRRR) model displays large negative biases with respect to strong wind events. In Part II of this study, we address two questions: 1) does increasing the horizontal resolution improve the representation of strong wind events over this region, and 2) are the biases in HRRR-forecasted winds related to the selected planetary boundary layer (PBL), surface layer (SL), and/or land surface model (LSM) parameterizations? We conduct Weather Research and Forecasting (WRF) simulations to address these two main questions. Increasing the horizontal resolution leads to a small improvement in the simulation of strong wind speeds over the complex terrain of Wyoming and Colorado. In general, changes in the PBL, SL, and LSM parameterizations show much larger changes in simulated wind speeds compared to increasing the model resolution alone. Specifically, changing from the Mellor-Yamada-Nakanishi-Niino scheme to the Mellor-Yamada-Janjić PBL and SL schemes results in nearly no change in the r 2 values, but there is a decrease in the magnitude of the strong wind speed bias (−12.52 m s −1 to −10.16 m s −1 ). We attribute these differences to differences in the diagnosis of surface wind speeds and mixing in the boundary layer. Further analysis is conducted to determine the value of 1-km forecasts of strong winds compared with wind speed diagnostics commonly used by the National Weather Service. |
| Authors |
Ethan Collins  , Zachary J. Lebo  , Robert M. Cox , Christopher Hammer , Matthew Brothers , Bart Geerts , Robert Capella , Sarah McCorkle
|
| Journal Info |
American Meteorological Society | Weather and Forecasting
|
| Publication Date |
3/8/2024 |
| ISSN |
0882-8156 |
Type |
article |
| Open Access |
closed
|
| DOI |
https://doi.org/10.1175/waf-d-23-0037.1 |
| Keywords |
Climate Modeling (Score: 0.515081) , Probabilistic Forecasting (Score: 0.501042)
|
