一个黄渤海海雾大气水平能见度算法
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摘要
为了提高对黄渤海海雾天气海面大气水平能见度(以下简称"能见度")的数值预报能力,利用黄渤海23个沿岸和岛屿测站2013—2017年雾天的地面观测数据,构建了基于湿度信息的能见度算法(A-F算法),并将之应用于黄渤海海雾的能见度数值预报。结果表明,与常用的根据模式预报的云水含量诊断能见度的SW算法(Stoelinga and Warner,1999)相比,A-F算法表现更优,尤其可以诊断出被SW算法漏报的能见度为1～3 km的轻雾,说明A-F算法对黄渤海海雾天气能见度的数值预报具有一定应用价值。若将来加入浮标与船舶观测数据,可以进一步改进A-F算法能见度公式的具体形式;依据本文构建A-F算法的思路,可以发展适合其他海域的海雾天气能见度诊断公式。
In order to improve the numerical prediction skills for the atmospheric horizontal visibility( hereinafter referred to as " visibility") over sea surface associated with sea fog over the Yellow and Bohai Seas( the YBS),the A-F algorithm that calculates the visibility using air humidity information is constructed based on the surface observation data in foggy weather from 23 coastal and island stations of the YBS from 2013 to 2017,and then it is applied to numerical prediction of visibility associated with sea fog over the YBS. The results show that the A-F algorithm performs better than the SW algorithm( Stoelinga and Warner,1999) which is commonly used to diagnose visibility using the model-predicted cloud water content. In particular,the A-F algorithm has the capability to diagnose light sea fog areas with visibility between 1 and 3 km that the SW algorithm always fails to forecast,indicating that the A-F algorithm has application value in the numerical prediction of visibility associated with sea fog over the YBS. The specific form of the visibility equation using the A-F algorithm can be further improved if putting in buoy and ship observations. It is possible to develop appropriate visibility algorithms for other seas according to the idea of constructing the A-F algorithm.
In order to improve the numerical prediction skills for the atmospheric horizontal visibility( hereinafter referred to as " visibility") over sea surface associated with sea fog over the Yellow and Bohai Seas( the YBS),the A-F algorithm that calculates the visibility using air humidity information is constructed based on the surface observation data in foggy weather from 23 coastal and island stations of the YBS from 2013 to 2017,and then it is applied to numerical prediction of visibility associated with sea fog over the YBS. The results show that the A-F algorithm performs better than the SW algorithm( Stoelinga and Warner,1999) which is commonly used to diagnose visibility using the model-predicted cloud water content. In particular,the A-F algorithm has the capability to diagnose light sea fog areas with visibility between 1 and 3 km that the SW algorithm always fails to forecast,indicating that the A-F algorithm has application value in the numerical prediction of visibility associated with sea fog over the YBS. The specific form of the visibility equation using the A-F algorithm can be further improved if putting in buoy and ship observations. It is possible to develop appropriate visibility algorithms for other seas according to the idea of constructing the A-F algorithm.
引文
[1]王彬华.海雾[M].北京:海洋出版社,1983:352.
[2]张苏平,鲍献文.近十年中国海雾研究进展[J].中国海洋大学学报(自然科学版),2008,38(3):359-366.
[3]STOELINGA M T,WARNER T T.Nonhydrostatic,mesobeta-scale model simulations of cloud ceiling and visibility for an east coast winter precipitation event[J].JAppl Meteor,1999,38(4):385-404.
[4]GULTEPE I,MLLER M D,BOYBEYI Z.A new visibility parameterization for warm-fog applications in numerical weather prediction models[J].J Appl Meteor Climatol,2006,45(11):1469-1480.
[5]徐静琦,张正,魏皓.青岛海雾雾滴谱与含水量观测与分析[J].海洋湖沼通报,1994(2):174-178.
[6]王淑英,张小玲,徐晓峰.北京地区大气能见度变化规律及影响因子统计分析[J].气象科技,2003,31(2):109-114.
[7]宋明,韩素琴,张敏,等.天津大气能见度与相对湿度和PM10及PM2.5的关系[J].气象与环境学报,2013,29(2):34-41.
[8]侯灵,安俊琳,朱彬.南京大气能见度变化规律及影响因子分析[J].大气科学学报,2014,37(1):91-98.
[9]樊高峰,马浩,张小伟,等.相对湿度和PM2.5浓度对大气能见度的影响研究:基于小时资料的多站对比分析[J].气象学报,2016,74(6):959-973.
[10]CREIGHTON G,KUCHERA E,ADAMS-SELIN R,et al.AFWA diagnostics in WRF[EB/OL].(2014-09-10)[2019-03-28].http://www2.mmm.ucar.edu/wrf/users/docs/AFWA_Diagnostics_in_WRF.pdf.
[11]DORAN J A,ROOHR P J,BEBERWYK D J,et al.The MM5 at the Air Force Weather Agency-New products to support military operations[C]//AMS:The 8th Conference on Aviation,Range,and Aerospace Meteorology,Dallas,Texas,10-15 January,1999.Boston:AMS,1999.
[12]傅刚,李晓岚,魏娜.大气能见度研究[J].中国海洋大学学报(自然科学版),2009,39(5):855-862.
[13]高荣珍,李欣,时晓曚,等.基于WRF模式的青岛近海能见度算法比较研究[J].海洋气象学报,2018,38(2):28-35.
[14]BANG C H,LEE J W,HONG S Y.Predictability experiments of fog and visibility in local airports over Korea using the WRF model[J].J Korean Soc Atmos Environ,2008,24(E2):92-101.
[15]KUNKEL B A.Parameterization of droplet terminal velocity and extinction coefficient in fog models[J].JClimate Appl Meteor,1984,23(1):34-41.
[16]王晓丽,张苏平,张晓梅,等.青岛市水平能见度变化特征及气象影响因子分析[J].气象科学,2008,28(增刊):31-36.
[17]GAO S H,LIN H,SHEN B,et al.A heavy sea fog event over the Yellow Sea in March 2005:Analysis and numerical modeling[J].Adv Atmos Sci,2007,24(1):65-81.
[18]FU G,GUO J T,ANGELINE P,et al.An analysis and modeling study of a sea fog event over the Yellow and Bohai Seas[J].J Ocean Univ Chin,2008,7(1):27-34.
[19]高山红,齐伊玲,张守宝,等.利用循环3DVAR改进黄海海雾数值模拟初始场Ⅰ:WRF数值试验[J].中国海洋大学学报(自然科学版),2010,40(10):1-9.
[20]高山红,张守宝,齐伊玲,等.利用循环3DVAR改进黄海海雾数值模拟初始场Ⅱ:RAMS数值试验[J].中国海洋大学学报(自然科学版),2010,40(11):1-10.
[21]WANG Y M,GAO S H,FU G,et al.Assimilating MTSAT-derived humidity in nowcasting sea fog over the Yellow Sea[J].Wea Forecasting,2014,29(2):205-225.
[22]HONG S Y,NOH Y,DUDHIA J.A new vertical diffusion package with an explicit treatment of entrainment processes[J].Mon Wea Rev,2006,134(9):2318-2341.
[23]KAIN J S,FRITSCH J M.An one-dimensional entraining/detraining plume model and its application in convective parameterization[J].J Atmos Sci,1990,47(23):2784-2802.
[24]LIN Y L,FARLEY R D,ORIVILLE H D.Bulk parameterization of the snow field in a cloud model[J].JClimate Appl Meteor,1983,22(6):1065-1092.
[25]IACONO M J,DELAMERE J S,MLAWER E J,et al.Radiative forcing by long-lived greenhouse gases:Calculations with the AER radiative transfer models[J].JGeophys Res:Atmos,2008,113(D13):D13103.
[26]DUDHIA J.A nonhydrostatic version of the Penn StateNCAR mesoscale model:Validation tests and simulation of an Atlantic cyclone and cold front[J].Mon Wea Rev,1989,121(5):1493-1513.
[27]CHEN F,DUDHIA J.Coupling an advanced land surfacehydrology model with the Penn Stat-NCAR MM5modeling system.PartⅠ:Model implementation and sensitivity[J].Mon Wea Rev,2001,129(4):569-585.
(1)地面温度露点差的计算对观测气压不敏感,采用最高(1 040.9 h Pa)和最低(987.2 hPa)的观测气压值分别计算温度露点差,两者的差值大多不超过0.04 K。由这一差值导致的FSL算法能见度计算结果的差值大多在0.2 km以下。
(1)A-F算法已经应用于“区域大气与海洋短期实时预报系统”(http://222.195.136.24)。
(2)η=1.000 0,0.997 5,0.992 5,0.985 0,0.977 5,0.970 0,0.954 0,0.934 0,0.909 0,0.880 0,0.850 6,0.821 2,0.791 8,0.762 5,0.708 4,0.657 3,0.609 0,0.563 4,0.520 4,0.479 8,0.441 5,0.405 5,0.371 6,0.339 7,0.309 7,0.281 5,0.255 1,0.230 3,0.207 1,0.185 4,0.165 1,0.146 1,0.128 4,0.111 8,0.096 5,0.082 2,0.068 9,0.056 6,0.045 2,0.034 6,0.024 9,0.015 9,0.007 6,0.000 0。1 000 m以下各中间层对应的海拔高度分别大约为10、40、91、153、214、313、465、660、899 m。
[2]张苏平,鲍献文.近十年中国海雾研究进展[J].中国海洋大学学报(自然科学版),2008,38(3):359-366.
[3]STOELINGA M T,WARNER T T.Nonhydrostatic,mesobeta-scale model simulations of cloud ceiling and visibility for an east coast winter precipitation event[J].JAppl Meteor,1999,38(4):385-404.
[4]GULTEPE I,MLLER M D,BOYBEYI Z.A new visibility parameterization for warm-fog applications in numerical weather prediction models[J].J Appl Meteor Climatol,2006,45(11):1469-1480.
[5]徐静琦,张正,魏皓.青岛海雾雾滴谱与含水量观测与分析[J].海洋湖沼通报,1994(2):174-178.
[6]王淑英,张小玲,徐晓峰.北京地区大气能见度变化规律及影响因子统计分析[J].气象科技,2003,31(2):109-114.
[7]宋明,韩素琴,张敏,等.天津大气能见度与相对湿度和PM10及PM2.5的关系[J].气象与环境学报,2013,29(2):34-41.
[8]侯灵,安俊琳,朱彬.南京大气能见度变化规律及影响因子分析[J].大气科学学报,2014,37(1):91-98.
[9]樊高峰,马浩,张小伟,等.相对湿度和PM2.5浓度对大气能见度的影响研究:基于小时资料的多站对比分析[J].气象学报,2016,74(6):959-973.
[10]CREIGHTON G,KUCHERA E,ADAMS-SELIN R,et al.AFWA diagnostics in WRF[EB/OL].(2014-09-10)[2019-03-28].http://www2.mmm.ucar.edu/wrf/users/docs/AFWA_Diagnostics_in_WRF.pdf.
[11]DORAN J A,ROOHR P J,BEBERWYK D J,et al.The MM5 at the Air Force Weather Agency-New products to support military operations[C]//AMS:The 8th Conference on Aviation,Range,and Aerospace Meteorology,Dallas,Texas,10-15 January,1999.Boston:AMS,1999.
[12]傅刚,李晓岚,魏娜.大气能见度研究[J].中国海洋大学学报(自然科学版),2009,39(5):855-862.
[13]高荣珍,李欣,时晓曚,等.基于WRF模式的青岛近海能见度算法比较研究[J].海洋气象学报,2018,38(2):28-35.
[14]BANG C H,LEE J W,HONG S Y.Predictability experiments of fog and visibility in local airports over Korea using the WRF model[J].J Korean Soc Atmos Environ,2008,24(E2):92-101.
[15]KUNKEL B A.Parameterization of droplet terminal velocity and extinction coefficient in fog models[J].JClimate Appl Meteor,1984,23(1):34-41.
[16]王晓丽,张苏平,张晓梅,等.青岛市水平能见度变化特征及气象影响因子分析[J].气象科学,2008,28(增刊):31-36.
[17]GAO S H,LIN H,SHEN B,et al.A heavy sea fog event over the Yellow Sea in March 2005:Analysis and numerical modeling[J].Adv Atmos Sci,2007,24(1):65-81.
[18]FU G,GUO J T,ANGELINE P,et al.An analysis and modeling study of a sea fog event over the Yellow and Bohai Seas[J].J Ocean Univ Chin,2008,7(1):27-34.
[19]高山红,齐伊玲,张守宝,等.利用循环3DVAR改进黄海海雾数值模拟初始场Ⅰ:WRF数值试验[J].中国海洋大学学报(自然科学版),2010,40(10):1-9.
[20]高山红,张守宝,齐伊玲,等.利用循环3DVAR改进黄海海雾数值模拟初始场Ⅱ:RAMS数值试验[J].中国海洋大学学报(自然科学版),2010,40(11):1-10.
[21]WANG Y M,GAO S H,FU G,et al.Assimilating MTSAT-derived humidity in nowcasting sea fog over the Yellow Sea[J].Wea Forecasting,2014,29(2):205-225.
[22]HONG S Y,NOH Y,DUDHIA J.A new vertical diffusion package with an explicit treatment of entrainment processes[J].Mon Wea Rev,2006,134(9):2318-2341.
[23]KAIN J S,FRITSCH J M.An one-dimensional entraining/detraining plume model and its application in convective parameterization[J].J Atmos Sci,1990,47(23):2784-2802.
[24]LIN Y L,FARLEY R D,ORIVILLE H D.Bulk parameterization of the snow field in a cloud model[J].JClimate Appl Meteor,1983,22(6):1065-1092.
[25]IACONO M J,DELAMERE J S,MLAWER E J,et al.Radiative forcing by long-lived greenhouse gases:Calculations with the AER radiative transfer models[J].JGeophys Res:Atmos,2008,113(D13):D13103.
[26]DUDHIA J.A nonhydrostatic version of the Penn StateNCAR mesoscale model:Validation tests and simulation of an Atlantic cyclone and cold front[J].Mon Wea Rev,1989,121(5):1493-1513.
[27]CHEN F,DUDHIA J.Coupling an advanced land surfacehydrology model with the Penn Stat-NCAR MM5modeling system.PartⅠ:Model implementation and sensitivity[J].Mon Wea Rev,2001,129(4):569-585.
(1)地面温度露点差的计算对观测气压不敏感,采用最高(1 040.9 h Pa)和最低(987.2 hPa)的观测气压值分别计算温度露点差,两者的差值大多不超过0.04 K。由这一差值导致的FSL算法能见度计算结果的差值大多在0.2 km以下。
(1)A-F算法已经应用于“区域大气与海洋短期实时预报系统”(http://222.195.136.24)。
(2)η=1.000 0,0.997 5,0.992 5,0.985 0,0.977 5,0.970 0,0.954 0,0.934 0,0.909 0,0.880 0,0.850 6,0.821 2,0.791 8,0.762 5,0.708 4,0.657 3,0.609 0,0.563 4,0.520 4,0.479 8,0.441 5,0.405 5,0.371 6,0.339 7,0.309 7,0.281 5,0.255 1,0.230 3,0.207 1,0.185 4,0.165 1,0.146 1,0.128 4,0.111 8,0.096 5,0.082 2,0.068 9,0.056 6,0.045 2,0.034 6,0.024 9,0.015 9,0.007 6,0.000 0。1 000 m以下各中间层对应的海拔高度分别大约为10、40、91、153、214、313、465、660、899 m。