To represent model uncertainties more comprehensively,a stochastically perturbed parameterization(SPP)scheme consisting of temporally and spatially varying perturbations of 18 parameters in the microphysics,convection...To represent model uncertainties more comprehensively,a stochastically perturbed parameterization(SPP)scheme consisting of temporally and spatially varying perturbations of 18 parameters in the microphysics,convection,boundary layer,and surface layer parameterization schemes,as well as the stochastically perturbed parameterization tendencies(SPPT)scheme,and the stochastic kinetic energy backscatter(SKEB)scheme,is applied in the Global and Regional Assimilation and Prediction Enhanced System-Regional Ensemble Prediction System(GRAPES-REPS)to evaluate and compare the general performance of various combinations of multiple stochastic physics schemes.Six experiments are performed for a summer month(1-30 June 2015)over China and multiple verification metrics are used.The results show that:(1)All stochastic experiments outperform the control(CTL)experiment,and all combinations of stochastic parameterization schemes perform better than the single SPP scheme,indicating that stochastic methods can effectively improve the forecast skill,and combinations of multiple stochastic parameterization schemes can better represent model uncertainties;(2)The combination of all three stochastic physics schemes(SPP,SPPT,and SKEB)outperforms any other combination of two schemes in precipitation forecasting and surface and upper-air verification to better represent the model uncertainties and improve the forecast skill;(3)Combining SKEB with SPP and/or SPPT results in a notable increase in the spread and reduction in outliers for the upper-air wind speed.SKEB directly perturbs the wind field and therefore its addition will greatly impact the upper-air wind-speed fields,and it contributes most to the improvement in spread and outliers for wind;(4)The introduction of SPP has a positive added value,and does not lead to large changes in the evolution of the kinetic energy(KE)spectrum at any wavelength;(5)The introduction of SPPT and SKEB would cause a 5%-10%and 30%-80%change in the KE of mesoscale systems,and all three stochastic schemes(SPP,SPPT,and SKEB)mainly affect the KE of mesoscale systems.This study indicates the potential of combining multiple stochastic physics schemes and lays a foundation for the future development and design of regional and global ensembles.展开更多
使用1980~2017年共38年崇明站逐日降水资料对崇明站年降水量及暴雨日数的特征进行分析,并使用中尺度数值预报模式WRF3.9.1.1(Weather Research and Forecasting model)针对崇明年降水量及暴雨日数异常年份2015年的最强降水过程进行数值...使用1980~2017年共38年崇明站逐日降水资料对崇明站年降水量及暴雨日数的特征进行分析,并使用中尺度数值预报模式WRF3.9.1.1(Weather Research and Forecasting model)针对崇明年降水量及暴雨日数异常年份2015年的最强降水过程进行数值模拟,结合站点降水观测资料使用统计方法来系统验证模拟结果。通过敏感性试验着重研究尺度自适应的GF(Grell–Freitas)与传统的KF(Kain–Fritsch)、BMJ(Betts–Miller–Janji?)积云对流参数化方案在不同比率的网格嵌套方式下对于本次过程极端降水总量及逐时变化预报的影响。研究结果表明:使用大比率(9:1或15:1)的双层嵌套可以更真实地模拟强降水区累积降水量分布和逐时变化情况,而使用传统的小比率(3:1或5:1)三层嵌套网格会导致大暴雨和特大暴雨的TS(Threat Score)评分降低,小时降水峰值模拟偏弱等问题;模式外圈使用传统的KF、BMJ积云对流方案比尺度自适应的GF方案对于内圈高分辨率的极端降水总量、逐时变化模拟更有优势,特别是使用KF方案,可以更真实地模拟出极端降水中心的日变化强度;而使用GF方案对于入海口降水模拟偏弱,大暴雨和特大暴雨的TS评分普遍偏低,小时降水峰值也被严重低估。展开更多
基金National Key Research and Development(R&D)Program of China,(Grant No.2018YFC1507405).
文摘To represent model uncertainties more comprehensively,a stochastically perturbed parameterization(SPP)scheme consisting of temporally and spatially varying perturbations of 18 parameters in the microphysics,convection,boundary layer,and surface layer parameterization schemes,as well as the stochastically perturbed parameterization tendencies(SPPT)scheme,and the stochastic kinetic energy backscatter(SKEB)scheme,is applied in the Global and Regional Assimilation and Prediction Enhanced System-Regional Ensemble Prediction System(GRAPES-REPS)to evaluate and compare the general performance of various combinations of multiple stochastic physics schemes.Six experiments are performed for a summer month(1-30 June 2015)over China and multiple verification metrics are used.The results show that:(1)All stochastic experiments outperform the control(CTL)experiment,and all combinations of stochastic parameterization schemes perform better than the single SPP scheme,indicating that stochastic methods can effectively improve the forecast skill,and combinations of multiple stochastic parameterization schemes can better represent model uncertainties;(2)The combination of all three stochastic physics schemes(SPP,SPPT,and SKEB)outperforms any other combination of two schemes in precipitation forecasting and surface and upper-air verification to better represent the model uncertainties and improve the forecast skill;(3)Combining SKEB with SPP and/or SPPT results in a notable increase in the spread and reduction in outliers for the upper-air wind speed.SKEB directly perturbs the wind field and therefore its addition will greatly impact the upper-air wind-speed fields,and it contributes most to the improvement in spread and outliers for wind;(4)The introduction of SPP has a positive added value,and does not lead to large changes in the evolution of the kinetic energy(KE)spectrum at any wavelength;(5)The introduction of SPPT and SKEB would cause a 5%-10%and 30%-80%change in the KE of mesoscale systems,and all three stochastic schemes(SPP,SPPT,and SKEB)mainly affect the KE of mesoscale systems.This study indicates the potential of combining multiple stochastic physics schemes and lays a foundation for the future development and design of regional and global ensembles.
文摘使用1980~2017年共38年崇明站逐日降水资料对崇明站年降水量及暴雨日数的特征进行分析,并使用中尺度数值预报模式WRF3.9.1.1(Weather Research and Forecasting model)针对崇明年降水量及暴雨日数异常年份2015年的最强降水过程进行数值模拟,结合站点降水观测资料使用统计方法来系统验证模拟结果。通过敏感性试验着重研究尺度自适应的GF(Grell–Freitas)与传统的KF(Kain–Fritsch)、BMJ(Betts–Miller–Janji?)积云对流参数化方案在不同比率的网格嵌套方式下对于本次过程极端降水总量及逐时变化预报的影响。研究结果表明:使用大比率(9:1或15:1)的双层嵌套可以更真实地模拟强降水区累积降水量分布和逐时变化情况,而使用传统的小比率(3:1或5:1)三层嵌套网格会导致大暴雨和特大暴雨的TS(Threat Score)评分降低,小时降水峰值模拟偏弱等问题;模式外圈使用传统的KF、BMJ积云对流方案比尺度自适应的GF方案对于内圈高分辨率的极端降水总量、逐时变化模拟更有优势,特别是使用KF方案,可以更真实地模拟出极端降水中心的日变化强度;而使用GF方案对于入海口降水模拟偏弱,大暴雨和特大暴雨的TS评分普遍偏低,小时降水峰值也被严重低估。
