模式地形与观测站地形高度差异一直是地面资料同化面临的棘手问题,合理的同化方案能够将地面自动站资料有效的同化到中尺度数值模式中。本文首先采用Guo et al.(2002)的方案实现了在WRF模式中应用集合Kalman滤波方法同化地面自动站资料;然后对方案进行调整,对10 m高度风场、2 m高度位温、2 m高度露点和地表气压进行同化。通过均方根误差分析,模拟结果和同化增量分析来确定集合平方根滤波(EnSRF)同化地面自动站资料的有效性,并进行敏感性试验分析检验模式对各要素物理量的响应状况。结果表明:在EnSRF同化系统中应用Guo et al.(2002)的方案将地面自动站资料进行同化到数值模式中,能够部分改善模拟结果;地面观测资料(温度、湿度、风场、地表气压)中各物理量分别同化到数值模式都能影响18小时降水预报,但各物理量所起作用大小不同,其中对结果影响最大的是露点;使用位温、露点分别代替温度、比湿进行同化模拟效果更好,对自动站资料的同化也更加有效
References
[1]
Fujita T, Stensrud D J, Dowell D C. 2006. Surface data assimilation using an ensemble Kalman filter approach with initial condition and model physics uncertainties [J]. Mon. Wea. Rev., 135 (5): 1846-1868.
[2]
Gao J, Xue M, Brewster K, et al. 2001. A 3DVAR data assimilation scheme for storm-scale model [C]. 14th Conference on NWP, AMS, J: 72-74.
[3]
Guo Y R, Shin D H, Lee J H, et al. 2002. Application of the MM5 3DVAR system for a heavy rain case over the Korean Peninsula [C]//Papers Presented at the Twelfth PSU/NCAR Mesoscale Model Users\' Workshop NCAR, June 24-25, 2002.
[4]
Hacker J P, Snyder C. 2005. Ensemble Kalman filter assimilation of fixed screen-height observations in a parameterized PBL [J]. Mon. Wea. Rev., 133 (11): 3260-3275.
[5]
Huang X Y, Xiao Q, Barker D M, et al. 2009. Four-dimensional variational data assimilation for WRF: Formulation and preliminary results [J]. Mon. Wea. Rev., 137 (1): 299-314.
[6]
J？rvinen H, Andersson E, Bouttier F. 1999. Variational assimilation of time sequences of surface observations with serially correlated errors [J]. Tellus, 51 (4): 469-488.
[7]
Martinelli J T, Pasken R W, Lin Y J, et al. 2003. A high resolution numerical simulation of a linear mesoscale convective system utilizing the MM5 4DVar system and single WSR-88D data [C]. 31st International Conference on Radar Meteorology American Meteorology Society, P1C. 3, Abstract and paper.
[8]
Miller P A, Benjamin S G. 1992. A system for the hourly assimilation of surface observations in mountainous and flat terrain [J]. Mon. Wea. Rev., 120 (10): 2342-2359.
[9]
Barker D M, Huang W, Guo Y R, et al. 2004. A Three Dimensional Variational (3DVAR) data assimilation system for use with MM5: Implementation and initial results [J]. Mon. Wea. Rev., 132 (4): 897-914.
[10]
De Pondeca M S F V, Zou X L. 2001. A case study of the variational assimilation of GPS zenith delay observations into a mesoscale model [J]. J. Appl. Meteor., 40 (9): 1559-1576.
[11]
Evensen G. 1994. Sequential data assimilation with a non-linear quasi-geostrophic model using Monte Carlo methods to forecast error statistics [J]. J. Geophys. Res., 99 (C5): 10143-10162.
[12]
Qiu C J, Chou J F. 2006. Four-dimensional data assimilation method based on SVD: Theoretical aspect [J]. Theor. Appl. Climatol., 83 (1-4): 51-57.
[13]
Ruggiero F H, Sashegyi K D, Madala R V, et al. 1996. The use of surface observations in four dimensional data assimilation using a mesoscale model [J]. Mon. Wea. Rev., 124 (5): 1018-1033.
[14]
Shafer M A, Fiebrich C A, Arent D S, et al. 2000. Quality assurance procedures in the Oklahoma Mseonet-work [J]. J. Atmos. Oceanic Techenol., 17 (4): 474-494.
[15]
Stensrud D J, Yussouf N, Dowell D C, et al. 2009. Assimilating surface data into a mesoscale model ensemble: Cold pool analyses from spring 2007 [J]. Atmospheric Research, 93 (1-3): 207-220.
[16]
陶士伟, 仲跻芹, 徐枝芳, 等. 2009. 地面自动站资料控制方案及应用 [J]. 高原气象, 28 (5): 1202-1210. Tao Shiwei, Zhong Qiqin, Xu Zhifang, et al. 2009. Quality control schemes and its application to automatic surface weather observation system [J]. Plateau Meteorology (in Chinese), 28 (5): 1202-1210.
[17]
Thompson R L, Edwards R, Hart J A, et al. 2003. Close proximity soundings within supercell environments obtained from the rapid update cycle [J]. Wea. Forecasting, 18: 1243-1261.
[18]
Tippett M K, Anderson J L, Bishop C H, et al. 2003. Ensemble square-root filters [J]. Mon. Wea. Rev., 131: 1485-1490.
[19]
Urban B. 1996. Coherent observation operators for surface data assimilation with application to snow depth [J]. J. Appl. Meteor., 35 (2): 258-270.
[20]
Vejen F, Jacobsson C, Fredriksson U, et al. 2002. Quality control of meteorological observations automatic methods used in the Nordic countries [R]. Climate Report, No. 8, KLIMA.
[21]
Whitaker J S, Hamill T M. 2002. Ensemble data assimilation without perturbed observations [J]. Mon. Wea. Rev., 130 (7): 1913-1924.
[22]
徐枝芳, 龚建东, 王建捷, 等. 2007a. 复杂地形下地面观测资料同化Ⅰ. 模式地形与观测站地形高度差异对地面资料同化的影响研究 [J]. 大气科学, 31 (2): 222-232. Xu Zhifang, Gong Jiandong, Wang Jianjie, et al. 2007a. A study of assimilation of surface observational data in complex terrain. Part Ⅰ: Influence of the elevation difference between model surface and observation site [J]. Chinese J. Atmos. Sci. (in Chinese), 31 (2): 222-232.
[23]
徐枝芳, 龚建东, 王建捷, 等. 2007b. 复杂地形下地面观测资料同化Ⅱ. 模式地形与观测站地形高度差异代表性误差 [J]. 大气科学, 31 (3): 449-458. Xu Zhifang, Gong Jiandong, Wang Jianjie, et al. 2007b. A study of assimilation of surface observational data in complex terrain. Part Ⅱ: Representative error of the elevation difference between model surface and observation site [J]. Chinese J. Atmos. Sci. (in Chinese), 31 (3): 449-458.
[24]
薛纪善, 庄世宇, 朱国富, 等. 2008. GRAPES新一代全球/区域变分同化系统研究 [J]. 科学通报, 53 (20): 2408-2417. Xue Jshan, Zhuang Shiyu, Zhu Guofu, et al. 2008. Research of new generation global/region variational assimilation system GRAPES [J]. Chin. Sci. Bull. (in Chinese), 53 (20): 2408-2417.
[25]
Zhang D L, Fritcsh J M. 1986. A case study of the sensitivity of numerical model simulation of mesoscale convective systems to varying initial condition [J]. Mon. Wea. Rev., 114 (12): 2481-2431.
