青藏高原地形复杂且气候恶劣,对高原空投伞降和航空安全是巨大的挑战;本文基于数值模拟方法,研究一套适用于高原复杂地形的风场精确模拟方法。本研究首先基于WRF(Weather Research and Forecasting)模式的大涡模拟LES(Large Eddy Simula...青藏高原地形复杂且气候恶劣,对高原空投伞降和航空安全是巨大的挑战;本文基于数值模拟方法,研究一套适用于高原复杂地形的风场精确模拟方法。本研究首先基于WRF(Weather Research and Forecasting)模式的大涡模拟LES(Large Eddy Simulation)方案,研究青藏高原大涡模拟方法,构建一套降尺度至40 m水平分辨率的WRF-LES系统。然后,基于青藏高原大风个例,通过敏感性试验研究,评估LES方案和地形高程数据对风场模拟影响。其外,对LES方案的标准亚格子湍流应力模型中参数进行分析,得到青藏高原风场模拟的最优方案组。最后,进行批量试验,检验该方案对高原风场模拟的适用性。试验结果表明:(1)40 m分辨率的WRF-LES系统可模拟得到更精细和准确的风场信息,模拟风速平均绝对误差MAE(Mean Absolute Error)较ACM2方案减小1.4 m·s^(-1)且均方根误差RMSE(Root Mean Square Error)减小1.81 m·s^(-1);(2)高精度地形资料ASTER的接入可以改善模式对风场模拟的效果,各项误差较模式默认地形模拟结果均存在约0.2 m·s^(-1)的改善;(3)LES方案采用基于1.5阶湍流动能方案且常数项系数为0.1时模拟效果最佳,MAE为1.56 m·s^(-1)且RMSE为2.06 m·s^(-1)。批量试验验证了大涡模拟方案对于青藏高原边界层风场模拟具有较强的适用性,40 m分辨率区域风场模拟效果明显优于中尺度模拟效果,可为高原空投伞降提供准确的风场信息。展开更多
阵风的预报误差检验对实际工作中的精细化预报订正具有一定的指导意义,同时对精细化预报中如何消除误差日变化的影响提供了借鉴。选取2017—2019年3~72 h逐日逐3 h欧洲中期天气预报中心(European Centre for Medium-Range Weather Forec...阵风的预报误差检验对实际工作中的精细化预报订正具有一定的指导意义,同时对精细化预报中如何消除误差日变化的影响提供了借鉴。选取2017—2019年3~72 h逐日逐3 h欧洲中期天气预报中心(European Centre for Medium-Range Weather Forecast,ECMWF)细网格10 m阵风和10 m平均风预报资料,基于大连地区9个国家气象观测站实况逐3 h极大风资料进行预报误差检验分析。结果表明:按预报风级和实况风级分类的预报误差对比检验均表明ECMWF细网格预报整体偏大,平均误差为0.96 m·s-1,但具体到各风级时两种分类的预报误差统计结论并不一致,按预报风级分类的检验更符合基于模式预报开展的实际预报工作。以预报为基准统计,各风向、各风级、各站的预报误差均差异明显,风级越大预报偏大的程度越高,风向也表现出随风级增大误差增大的趋势。阵风预报的平均误差具有明显日变化,08:00(北京时,下同)前后误差最大,20:00前后误差最小,主要由10 m平均风的平均误差日变化所致。全部预报个例与实况各时效预报相关系数均在0.7以上,具体到各风级、风向时,各风向相关性均较好,而各风级的相关系数则明显降低,8级及以上风力预报的可信度大幅下降。展开更多
基于欧洲中期天气预报中心(European Centre for Medium-Range Weather Forecasts,ECMWF)、中国国家气象中心业务运行的中尺度数值预报系统(Global/Regional Assimilation and Prediction Enhanced System Meso,GRAPES-Meso)、美国国家...基于欧洲中期天气预报中心(European Centre for Medium-Range Weather Forecasts,ECMWF)、中国国家气象中心业务运行的中尺度数值预报系统(Global/Regional Assimilation and Prediction Enhanced System Meso,GRAPES-Meso)、美国国家环境预报中心(National Centers for Environmental Prediction,NCEP)的全球预报系统(Global Forecast System,GFS)、GRAPES全球预报系统(GRAPES-GFS)4个模式风场预报资料,利用双线性、反距离加权、三次样条、克里格等插值方法对华东及周边地区(110°~130°E,20°~40°N)2020年1—4月逐日地面和高空风0~72 h集合预报资料进行降尺度处理,得到满足机场及终端区气象保障的精细化风场预报。此外,还对精细化风场预报做多模式集成。结果表明,对于风场的精细化格点预报,反距离加权插值方法误差最小,为最优水平插值方法。基于扩展复卡尔曼滤波的多模式集成(Augmented Complex Extended Kalman Filter,ACEKF)可进一步减小风场预报的误差。对华东地区上海、青岛和厦门3个机场地面和高空风的多模式集成风场精细化预报的分析表明,ACEKF多模式集成预报不但均方根误差较BREM、ECMWF和GRAPES-GFS的预报误差小,且随高度变化也不如单模式预报的大,其预报性能更为稳定。展开更多
Numerical weather prediction(NWP)models have always presented large forecasting errors of surface wind speeds over regions with complex terrain.In this study,surface wind forecasts from an operational NWP model,the SM...Numerical weather prediction(NWP)models have always presented large forecasting errors of surface wind speeds over regions with complex terrain.In this study,surface wind forecasts from an operational NWP model,the SMS-WARR(Shanghai Meteorological Service-WRF ADAS Rapid Refresh System),are analyzed to quantitatively reveal the relationships between the forecasted surface wind speed errors and terrain features,with the intent of providing clues to better apply the NWP model to complex terrain regions.The terrain features are described by three parameters:the standard deviation of the model grid-scale orography,terrain height error of the model,and slope angle.The results show that the forecast bias has a unimodal distribution with a change in the standard deviation of orography.The minimum ME(the mean value of bias)is 1.2 m s^(-1) when the standard deviation is between 60 and 70 m.A positive correlation exists between bias and terrain height error,with the ME increasing by 10%−30%for every 200 m increase in terrain height error.The ME decreases by 65.6%when slope angle increases from(0.5°−1.5°)to larger than 3.5°for uphill winds but increases by 35.4%when the absolute value of slope angle increases from(0.5°−1.5°)to(2.5°−3.5°)for downhill winds.Several sensitivity experiments are carried out with a model output statistical(MOS)calibration model for surface wind speeds and ME(RMSE)has been reduced by 90%(30%)by introducing terrain parameters,demonstrating the value of this study.展开更多
文摘基于欧洲中期天气预报中心(European Centre for Medium-Range Weather Forecasts,ECMWF)、中国国家气象中心业务运行的中尺度数值预报系统(Global/Regional Assimilation and Prediction Enhanced System Meso,GRAPES-Meso)、美国国家环境预报中心(National Centers for Environmental Prediction,NCEP)的全球预报系统(Global Forecast System,GFS)、GRAPES全球预报系统(GRAPES-GFS)4个模式风场预报资料,利用双线性、反距离加权、三次样条、克里格等插值方法对华东及周边地区(110°~130°E,20°~40°N)2020年1—4月逐日地面和高空风0~72 h集合预报资料进行降尺度处理,得到满足机场及终端区气象保障的精细化风场预报。此外,还对精细化风场预报做多模式集成。结果表明,对于风场的精细化格点预报,反距离加权插值方法误差最小,为最优水平插值方法。基于扩展复卡尔曼滤波的多模式集成(Augmented Complex Extended Kalman Filter,ACEKF)可进一步减小风场预报的误差。对华东地区上海、青岛和厦门3个机场地面和高空风的多模式集成风场精细化预报的分析表明,ACEKF多模式集成预报不但均方根误差较BREM、ECMWF和GRAPES-GFS的预报误差小,且随高度变化也不如单模式预报的大,其预报性能更为稳定。
基金supported by the National Natural Science Foundation of China(No.U2142206).
文摘Numerical weather prediction(NWP)models have always presented large forecasting errors of surface wind speeds over regions with complex terrain.In this study,surface wind forecasts from an operational NWP model,the SMS-WARR(Shanghai Meteorological Service-WRF ADAS Rapid Refresh System),are analyzed to quantitatively reveal the relationships between the forecasted surface wind speed errors and terrain features,with the intent of providing clues to better apply the NWP model to complex terrain regions.The terrain features are described by three parameters:the standard deviation of the model grid-scale orography,terrain height error of the model,and slope angle.The results show that the forecast bias has a unimodal distribution with a change in the standard deviation of orography.The minimum ME(the mean value of bias)is 1.2 m s^(-1) when the standard deviation is between 60 and 70 m.A positive correlation exists between bias and terrain height error,with the ME increasing by 10%−30%for every 200 m increase in terrain height error.The ME decreases by 65.6%when slope angle increases from(0.5°−1.5°)to larger than 3.5°for uphill winds but increases by 35.4%when the absolute value of slope angle increases from(0.5°−1.5°)to(2.5°−3.5°)for downhill winds.Several sensitivity experiments are carried out with a model output statistical(MOS)calibration model for surface wind speeds and ME(RMSE)has been reduced by 90%(30%)by introducing terrain parameters,demonstrating the value of this study.
