大小兴安岭林区风场动力降尺度对比研究

Comparisons of dynamic downscaling of the wind field in forest areas of Da-Xiao-Xing’anling Mountains

对2017年春季黑龙江省大、小兴安岭林区的6个代表站点10m风场进行降尺度分析,并结合观测数据对比分析了WRF模式和CALMET降尺度模式的10m风速、风向预报结果。结果表明:两模式逐小时风速预报与观测的相关系数为0.5—0.7,且随着风速的增加,模式的预报准确率逐渐提高,夜间的风速预报偏差较大,进入白天后,偏差明显减小。WRF模式对风速变化趋势的预报效果优于CALMET模式,与观测的风速相关性更高,而CALMET模式对较大风速的预报效果优于WRF模式。在风向预报方面,WRF和CALMET的风向模拟与观测风向均有较好的一致性,模式预报准确率较高的两个风向也刚好对应各站的盛行风向。同时,本文用回归方法对日平均风速进行订正发现,订正后各站的日平均风速预报准确率平均提高了50%,具有较好的业务应用价值。

In this study,we conducted the downscaling analysis of10-m wind fields at 6 stations in Da-Xiao-Xing′anling Mountains during spring in2017.We also used the observations to evaluate the10-m wind speed and direction simulated with the Weather Research and Forecasting (WRF) model and with the CALMET downscaling model.The correlation coefficients between observed hourly wind speeds and that simulated with the two models reach 0.5- 0.7.The prediction accuracy gradually increases with the increase of wind speed.The forecasting deviation of wind speed is relatively large at night and decreases during the daytime.The WRF model predicts the variability of wind speeds better than the CALMET model,and that has a higher correlation with the observations;but for the case of strong winds,the CALMET model performs better than the WRF model.The wind direction simulations of WRF and CALMET models are both in a good agreement with the observations.Wind directions with high prediction accuracy correspond to the prevailing wind directions of each station.Meanwhile,a simple-regression method is used to correct daily mean wind speed.Results indicate that the forecasting accuracy of daily mean wind speed increases by 50% on average,with good prospects in the operational application.