基于不同分辨率和参数化方案的新疆区域数值模式性能初步评估
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摘要
基于中尺度数值模式WRF,选取新疆两次强降水过程,设计3个试验方案,其中试验1为控制试验,试验2提高分辨率,试验3提高分辨率并调整物理参数化方案,初步评估不同分辨率和参数化方案对新疆区域2 m温度、10 m风速、降水预报的影响。结果表明:(1)提高分辨率对2 m温度、10 m风速模拟精度均有提高,2 m温度预报精度提高约0.5℃,降低了日间温度模拟冷偏差;10 m风速预报精度提高约0.5 m/s,降低了风速模拟正偏差;但提高分辨率后,模式出现虚假降水预报的情况。(2)提高分辨率并调整物理参数化方案后,2 m温度模拟误差略有减小,模拟偏差减小约0.2℃;10 m风速模拟误差增大约0.5 m/s,模拟偏差增大超过0.5 m/s;对降水落区、量级的模拟精度显著提高,减小了降水中心的模拟强度,对虚假降水预报有一定修正。
The skill of the Weather Research and Forecasting(WRF) model was assessed in predicting temperature, wind and rainfall in Xinjiang for two heavy rainfall processes by using different spatial resolutions and parameterization schemes. Three experiments were conducted, including(1)27 km/9 km nested grid(Exp1),(2)9 km/3 km nested grid(Exp2)and(3)9 km/3 km nested grid with another parameterization schemes. After increasing the spatial resolution, the simulation precision of air temperature was improved by 0.5 degrees centigrade and the cold bias during daytime was reduced.In addition, the RMSE of 10 meters wind was decreased by 0.5 m/s and the wind positive deviation was reduced. However, the model produced too much spurious rainfall. Increasing the spatial resolution and changing the parameterization scheme simultaneously, the accuracy of air temperature was further improved with a reduction of the temperature deviation by 0.2 ℃. Nevertheless, the simulation error of10 meters wind was increased about 0.5 m/s, and the bias was enlarged up to 0.5 m/s. Most importantly, the spatial distribution of rainfall and rainfall intensity were greatly improved. More specifically, the location and intensity of precipitation cell were well captured, and spurious rainfall was reduced due to increasing spatial resolution.
The skill of the Weather Research and Forecasting(WRF) model was assessed in predicting temperature, wind and rainfall in Xinjiang for two heavy rainfall processes by using different spatial resolutions and parameterization schemes. Three experiments were conducted, including(1)27 km/9 km nested grid(Exp1),(2)9 km/3 km nested grid(Exp2)and(3)9 km/3 km nested grid with another parameterization schemes. After increasing the spatial resolution, the simulation precision of air temperature was improved by 0.5 degrees centigrade and the cold bias during daytime was reduced.In addition, the RMSE of 10 meters wind was decreased by 0.5 m/s and the wind positive deviation was reduced. However, the model produced too much spurious rainfall. Increasing the spatial resolution and changing the parameterization scheme simultaneously, the accuracy of air temperature was further improved with a reduction of the temperature deviation by 0.2 ℃. Nevertheless, the simulation error of10 meters wind was increased about 0.5 m/s, and the bias was enlarged up to 0.5 m/s. Most importantly, the spatial distribution of rainfall and rainfall intensity were greatly improved. More specifically, the location and intensity of precipitation cell were well captured, and spurious rainfall was reduced due to increasing spatial resolution.
引文
[1]马雷鸣,鲍旭炜.数值天气预报模式物理过程参数化方案的研究进展[J].地球科学进展,2017,32(7):679-687.
[2]Skamarock W C.A description of the advanced research WRF version 3[J].Ncar Technical,2006,113:7-25.
[3]Yih A C,Walsh J E.Sensitivities of Numerical Model Forecasts of Extreme Cyclone Events[J].Adv Atmos Sci,1991,8(1):51-66.
[4]崔波,王建捷.MM5在国家气象中心CRAY-C92的实时预报试验尝试[J].应用气象学报,1999,10(2):129-140.
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[6]盛春岩,薛德强,雷霆,等.雷达资料同化与提高模式水平分辨率对短时预报影响的数值对比试验[J].气象学报,2006,64(3):293-307.
[7]Langhans W,Schmidli J,Fuhrer O,et al.Long-term simulations of thermally driven flows and orographic convection at convection-parameterizing and cloudresolving resolutions[J].Journal of Applied Meteorology&Climatology,2013,52(6):1490-1510.
[8]Gentry M S,Lackmann G M.Sensitivity of simulated tropical cyclone structure and intensity to horizontal resolution[J].Monthly Weather Review,2010,138(3):688-704.
[9]马雷鸣.热带气旋边界层关键结构研究进展[J].地球物理学进展,2013,28(3):1259-1268.
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[11]Seity Y,Brousseau P,Malardel S,et al.The AROME-France convective-scale operational model[J].Monthly Weather Review,2010,139(3):976-991.
[12]Rajeevan M,Kesarkar A,Thampi S B,et al.Sensitivity of WRF cloud microphysics to simulations of a severe thunderstorm event over Southeast India[J].Annales Geophysicae,2010,28(2):603-619.
[13]Jankov I,Gallus J W A,Segal M,et al.An investigation of IHOP convective system predictability using a matrix of 19WRF members[C]//Proceedings of the 84th AMS Annual Meeting,Seattle,U.S.A.Jan.10-15,2004.
[14]赵鸣.边界层和陆面过程对中国暴雨影响研究的进展[J].暴雨灾害,2008,27(2):186-190.
[15]于晓晶,于志翔,唐永兰,等.不同云微物理方案对新疆冷锋暴雪的预报影响分析[J].暴雨灾害,2017,36(1):33-41.
[16]朱格利,林万涛,曹艳华.用WRF模式中不同云微物理参数化方案对华南一次暴雨过程的数值模拟和性能分析[J].大气科学,2014,38(3):513-523.
[17]肖玉华,何光碧,顾清源,等.边界层参数化方案对不同性质降水模拟的影响[J].高原气象,2010,29(2):331-339.
[18]马艳,陈尚,董海鹰.两种边界层参数化方案和下垫面信息对一次暴雨过程模拟的影响[J].暴雨灾害,2017,36(6):550-556.
[19]徐慧燕,朱业,刘瑞,等.长江下游地区不同边界层参数化方案的试验研究[J].大气科学,2013,37(1):149-159.
[20]沈新勇,黄文彦,王卫国,等.利用TWP-IBE试验资料对比两种边界层参数化方案[J].应用气象学报,2014,25(4):385-396.
[21]杨薇,冯文,李勋.微物理过程和积云参数化方案对海南岛秋季暴雨模拟的影响[J].暴雨灾害,2017,36(1):8-17.
[22]于晓晶,李淑娟,辛渝,等.DOGRAFS边界层方案对新疆气象要素预报影响初探[J].沙漠与绿洲气象,2014,8(5):16-22.
[23]章国材.美国WRF模式的进展和应用前景[J].气象,2004,30(12):27-31.
[24]Watson D F.A refinement of inverse distance weighted interpolation[J].Geo-Processing,1985,2(2):315-327.
[25]彭彬,周艳莲,高苹,等.气温插值中不同空间插值方法的适用性分析---以江苏省为例[J].地球信息科学学报,2011,13(4):539-548.
[26]裘国庆.数值天气预报标准化检验方法[J].气象,1989,15(9):48-50.
[27]王雨,公颖,陈法敬,等.区域业务模式6h降水预报检验方案比较[J].应用气象学报,2013,24(2):171-178.
[2]Skamarock W C.A description of the advanced research WRF version 3[J].Ncar Technical,2006,113:7-25.
[3]Yih A C,Walsh J E.Sensitivities of Numerical Model Forecasts of Extreme Cyclone Events[J].Adv Atmos Sci,1991,8(1):51-66.
[4]崔波,王建捷.MM5在国家气象中心CRAY-C92的实时预报试验尝试[J].应用气象学报,1999,10(2):129-140.
[5]Colle B A,Garvert M F,Wolfe J B,et al.The 13-14December 2001 IMPROVE-2 Event.Part III:Simulated Microphysical Budgets and Sensitivity Studies.[J].Journal of the Atmospheric Sciences,2005,62(10):3535-3558.
[6]盛春岩,薛德强,雷霆,等.雷达资料同化与提高模式水平分辨率对短时预报影响的数值对比试验[J].气象学报,2006,64(3):293-307.
[7]Langhans W,Schmidli J,Fuhrer O,et al.Long-term simulations of thermally driven flows and orographic convection at convection-parameterizing and cloudresolving resolutions[J].Journal of Applied Meteorology&Climatology,2013,52(6):1490-1510.
[8]Gentry M S,Lackmann G M.Sensitivity of simulated tropical cyclone structure and intensity to horizontal resolution[J].Monthly Weather Review,2010,138(3):688-704.
[9]马雷鸣.热带气旋边界层关键结构研究进展[J].地球物理学进展,2013,28(3):1259-1268.
[10]Davies T,Cullen M J P,Malcolm A J,et al.A new dynamical core for the Met Office's global and regional modelling of the atmosphere[J].Quarterly Journal of the Royal Meteorological Society,2005,131(608):1759-1782.
[11]Seity Y,Brousseau P,Malardel S,et al.The AROME-France convective-scale operational model[J].Monthly Weather Review,2010,139(3):976-991.
[12]Rajeevan M,Kesarkar A,Thampi S B,et al.Sensitivity of WRF cloud microphysics to simulations of a severe thunderstorm event over Southeast India[J].Annales Geophysicae,2010,28(2):603-619.
[13]Jankov I,Gallus J W A,Segal M,et al.An investigation of IHOP convective system predictability using a matrix of 19WRF members[C]//Proceedings of the 84th AMS Annual Meeting,Seattle,U.S.A.Jan.10-15,2004.
[14]赵鸣.边界层和陆面过程对中国暴雨影响研究的进展[J].暴雨灾害,2008,27(2):186-190.
[15]于晓晶,于志翔,唐永兰,等.不同云微物理方案对新疆冷锋暴雪的预报影响分析[J].暴雨灾害,2017,36(1):33-41.
[16]朱格利,林万涛,曹艳华.用WRF模式中不同云微物理参数化方案对华南一次暴雨过程的数值模拟和性能分析[J].大气科学,2014,38(3):513-523.
[17]肖玉华,何光碧,顾清源,等.边界层参数化方案对不同性质降水模拟的影响[J].高原气象,2010,29(2):331-339.
[18]马艳,陈尚,董海鹰.两种边界层参数化方案和下垫面信息对一次暴雨过程模拟的影响[J].暴雨灾害,2017,36(6):550-556.
[19]徐慧燕,朱业,刘瑞,等.长江下游地区不同边界层参数化方案的试验研究[J].大气科学,2013,37(1):149-159.
[20]沈新勇,黄文彦,王卫国,等.利用TWP-IBE试验资料对比两种边界层参数化方案[J].应用气象学报,2014,25(4):385-396.
[21]杨薇,冯文,李勋.微物理过程和积云参数化方案对海南岛秋季暴雨模拟的影响[J].暴雨灾害,2017,36(1):8-17.
[22]于晓晶,李淑娟,辛渝,等.DOGRAFS边界层方案对新疆气象要素预报影响初探[J].沙漠与绿洲气象,2014,8(5):16-22.
[23]章国材.美国WRF模式的进展和应用前景[J].气象,2004,30(12):27-31.
[24]Watson D F.A refinement of inverse distance weighted interpolation[J].Geo-Processing,1985,2(2):315-327.
[25]彭彬,周艳莲,高苹,等.气温插值中不同空间插值方法的适用性分析---以江苏省为例[J].地球信息科学学报,2011,13(4):539-548.
[26]裘国庆.数值天气预报标准化检验方法[J].气象,1989,15(9):48-50.
[27]王雨,公颖,陈法敬,等.区域业务模式6h降水预报检验方案比较[J].应用气象学报,2013,24(2):171-178.