基于平均风分量的阵风风速参数化方法研究OA

To further improve the forecast accuracy of strong gusts and mitigate the disaster risks they pose,this study utilizes 24 h forecast 10m wind field data from the European Centre for Medium-Range Weather Forecasts(ECMWF)deterministic model spanning January 2022 to December 2023,as well as concurrent hourly observa-tions of maximum gust speed and direction from ground stations operated by the China Meteorological Adminis-tration(CMA).Focused on 358 national observation stations in Sichuan and its surrounding regions,this study proposes a novel gust parameterization method based on mean wind components following a systematic analysis of the limitations of the traditional gust factor method.Widely adopted in operational forecasting due to its sim-plicity and strong practicality,the traditional gust factor method exhibits substantial underestimation of strong gusts in Sichuan and adjacent areas.This limitation stems primarily from inherent forecast errors in numerical models,unbalanced distribution of sample data,and failure to account for gust forecast discrepancies induced by variations in mean wind components.To address this underestimation issue,this study develops a gust parameter-ization method based on the numerical model's 10 m u and v wind forecasts for individual stations.Specifically,observed gust wind directions are first adjusted to align with model-predicted directions,eliminating the interfer-ence of directional mismatches on gust speed forecast accuracy.For different sign combinations of the model-pre-dicted 10 m u and v components,gust factors at designatedquantiles are calculated separately for the squared u and v components of direction-adjusted observed gusts and the squared u and v components of model-predicted winds.This approach fully incorporates the influence of differences in 10 m u and v wind components on gust forecasts while reducing biases in strong gust fitting caused by unbalanced sample distribution.Independent sam-ple testing is conducted using data from January to November 2024,with comparative analyses of multiple gust forecast methods.The results demonstrate that three key measures-calculating gust factors based on designated quantiles,constructing a parameterization model using mean wind components,and performing wind direction alignment-can all effectively reduce the underestimation of strong gusts.In the evaluation of metrics such as fore-cast error and accuracy,the proposed method outperforms other approaches in strong gust forecasting.Notably,the contributions of the first two measures to mitigating strong gust underestimation are greater than those of wind direction alignment.Case validation during a cold air-induced strong wind event in March 2024 further con-firms the superiority of the proposed method over the traditional gust factor method.Additionally,a comparative analysis is performed against the 3 hour maximum gust forecast product from the ECMWF deterministic model,a commonly used tool in operational forecasting.The results show that for gusts≥13.9 m·s⁻¹,the proposed meth-od achieves higher forecast accuracy than the ECMWF model product.However,the mean error and mean abso-lute error of the proposed method in this wind speed range are slightly higher than those of the ECMWF model.This is primarily because the 10 m wind field data used in this study are model forecasts at instantaneous time points,which fail to fully capture the hourly variability of wind fields driven by transient meteorological process-es.In future research,hourly maximum wind speed and corresponding direction forecast data from mesoscale nu-merical models will be integrated with the proposed method to further improve the forecast accuracy of strong gusts.