以新一代全球/区域多尺度通用同化与数值预报系统-热带气旋路径数值预报系统(global/regional assimilation and prediction system-tropical cyclone model, GRAPES-TCM)为试验模式, 采用组合不同的物理参数化方案(MP)方法和随机全倾向扰动(STTP)方法, 生成反映模式不确定性的集合成员, 在此基础上设计包含6个成员的3种集合方案, 方案1和方案3的成员分别用MP方法和STTP方法生成, 方案2的成员同时采用MP和STTP方法生成, 用3种集合方案对1109台风“梅花”进行了36次72h的集合预报试验。结果显示: 对于路径预报, 3种集合方案中预报效果最好的是方案3, 其次为方案2, 最差的是方案1; 对于强度预报, 方案1和方案2的预报效果差异不大, 都远好于方案3。方案2和方案3的路径预报与强度预报都好于控制试验的预报, 方案1的路径预报好于大部分成员的预报, 强度预报好于所有成员的预报。3种方案的路径离散度都偏小, 方案3偏小最多, 其次为方案2; 方案3的强度离散度也过于偏小, 是3种方案中最小的, 方案1和方案2的强度离散度在积分前期明显偏小, 积分后期则有偏大的趋势, 其中方案2的强度离散度大于方案1。与国内外8个业务数值模式的预报结果比较, 对于路径预报, 方案1优于5个业务模式的预报, 方案2和方案3则优于除欧洲数值以外的7个业务模式的预报; 对于强度预报, 方案1和方案2优于所有8个业务模式的预报, 方案3优于6个业务模式的预报。总体而言, 3种集合方案的路径和强度预报都表现出优于确定性预报的预报能力, 相对于各业务数值模式都表现出一定的预报优势, 具有业务应用的价值, 其中同时应用STTP和MP方法的方案2对台风的综合预报效果是最优的。
The GRAPES-TCM (global/regional assimilation and prediction system-tropical cyclone model) is used to make ensemble prediction experiments for typhoon Muifa (1109) in 2011. Three kinds of ensemble schemes are designed for the experiments. Every scheme has six ensemble members, which reflect the uncertainty of the model. The method of multiple physics (MP) is used to form the members of scheme 1. The method of stochastic total tendency perturbation (STTP) is used to form the members of scheme 3. Both the MP method and the STTP method are used to form the members of scheme 2. Thirty-six experiments are made and the integration time is 72 h.
[1] 陈国民, 汤杰, 曾智华. 2012. 2011年西北太平洋热带气旋预报精度评定[J]. 气象, 38(10): 1238-1246.
[2] 端义宏, 陈联寿, 许映龙, 等. 2012. 我国台风监测预报预警体系的现状及建议[J]. 中国工程科学, 14(9): 4-9.
[3] 郝世峰, 崔晓鹏, 潘劲松. 2007. 多积云参数化方案热带气旋路径集合预报试验[J]. 热带气象学报, 23(6): 569-574.
[4] 黄伟, 端义宏, 薛纪善, 等. 2007. 热带气旋路径数值模式业务试验性能分析[J]. 气象学报, 65(4): 578-587.
[5] 黄小刚, 费建芳, 陆汉城. 2007. 基于集合Kalman 滤波数据 同化的热带气旋路径集合预报研究[J]. 大气科学, 31(3): 468-478.
[6] 黄小刚, 费建芳, 陆汉城, 等. 2010. 基于集合Kalman滤波数据同化与偏差修正的热带气旋强度集合预报研究[J]. 气象学报, 68(1): 79-87.
[7] 黄燕燕, 袁金南, 万齐林, 等. 2006. 基于BDA扰动法的台风路径集合预报试验研究[J]. 热带气象学报, 22(1): 49-54.
[8] 钱传海, 端义宏, 麻素红, 等. 2012. 我国台风业务现状及其关键技术[J]. 气象科技进展, 2(5): 36-43.
[9] 汤杰, 陈国民, 余晖. 2011. 2010年西北太平洋台风预报精度评定及分析[J]. 气象, 37(10): 1320-1328.
[10] 谭燕, 梁旭东. 2010. 一次登陆台风的集合预报试验[J]. 热带气象学报, 26(4): 401-408.
[11] 王晨稀, 梁旭东. 2007. 热带气旋路径集合预报试验[J]. 应用气象学报, 18(5): 586-593.
[12] 王秋良, 刘家峻, 张立凤. 2012. 台风路径集合预报试验[J]. 气象科学, 32(2): 137-144.
[13] 许映龙, 张玲, 高拴柱. 2010. 我国台风预报业务的现状及思考[J]. 气象, 36(7): 43-49.
[14] 袁金南, 万齐林, 黄燕燕, 等. 2006. 南海热带气旋路径集合预报试验[J]. 热带气象学报, 22(2): 105-112.
[15] 张庆红, 张春喜, 张中锋, 等. 2007. 热带气旋集合预报中的不确定性研究[J]. 地球物理学报, 50(3): 701-706.
[16] 周霞琼, 端义宏, 朱永禔. 2003a. 热带气旋路径集合预报方法研究Ⅰ——正压模式结果的初步分析[J]. 热带气象学报, 19(1): 1-8.
[17] 周霞琼, 张秀珍, 端义宏, 等. 2003b. 滞后平均法(LAF)在热带气旋路径集合预报中的应用[J]. 气象科学, 23(4): 410-417.
[18] BUCKINGHAM C, MARCHOK T, GINIS I, et al. 2010. Short- and medium-range prediction of tropical and transitioning cyclone tracks within the NCEP global ensemble forecasting system[J]. Weather and Forecasting, 25: 1736-1754.
[19] CHAN J C L, LI K K. 2005. Ensemble forecasting of tropical cyclone motion using a barotropic model. Part Ⅲ: combining perturbations of the environment and the vortex[J]. Meteorology Atmospheric Physics, 90: 109-126.
[20] CHEN CAIZHU, YU JINHUA, LI QINGQING. 2011. Western north Pacific tropical cyclone intensity guidance evaluations using an alternative verification technique[J]. Atmospheric and Oceanic Science Letters, 4(3): 151-156.
[21] CHEUNG K K W, CHAN J C L. 1999a. Ensemble forecasting of tropical cyclone motion using a barotropic model. Part Ⅰ: perturbations of the environment[J]. Monthly Weather Review, 127: 1229-1243.
[22] CHEUNG K K W, CHAN J C L. 1999b. Ensemble forecasting of tropical cyclone motion using a barotropic model. Part Ⅱ: perturbations of the vortex[J]. Monthly Weather Review, 127: 2617-2640.
[23] CHEUNG K K W. 2001. Ensemble forecasting of tropical cyclone motion: comparison between regional bred modes and random perturbations[J]. Meteorology Atmospheric Physics, 78: 23-34.
[24] DAVIS C A, BOSART L F. 2002. Numerical simulations of the genesis of Hurricane Diana. Part Ⅱ: sensitivity of track and intensity prediction[J]. Monthly Weather Review, 130: 1100-1124.
[25] ECKEL F A, MASS C. 2005. Aspects of effective mesoscale, short-range ensemble forecasting[J]. Weather and Forecasting, 20: 328-350.
[26] GOERSS J S. 2000. Tropical cyclone track forecasts using an ensemble of dynamical models[J]. Monthly Weather Review, 128: 1187-1193.
[27] GRELL G A. 1993. Prognostic evaluation of assumption used by cumulus parameterizations[J]. Monthly Weather Review, 121: 764-787.
[28] HOU DINGCHEN, TOTH Z, ZHU YUEJIAN. 2006. A stochastic parameterization scheme within NCEP global ensemble forecast system[C]. Atlanta, Georgia: The 18th AMS conference on probability and statistics in the atmospheric sciences: 4.5.
[29] HOU DINGCHEN, TOTH Z, ZHU YUEJIAN, et al. 2008. Impact of a stochastic perturbation scheme on global ensemble forecast[C]. New Orleans, Louisiana: The 19th AMS conference on probability and statistics: 1.1.
[30] HOUZE R A, CHEN S S, LEE W C, et al. 2006. The hurricane rainband and intensity change experiment: observations and modeling of Hurricanes Katrina, Ophelia, and Rita[J]. Bulletin of the American Meteorological Society, 87: 1503-1521.
[31] JANJIC Z I. 1994. The step-mountain eta coordinate model: further developments of the convection, viscous sublayer and turbulence closure schemes[J]. Monthly Weather Review, 122: 927-945.
[32] KAIN J S, FRITSCH J M. 1993. Convective parameterization for mesoscale models: the Kain-Fritsch scheme. The Representation of Cumulus Convection in Numerical Models[M]. Washington D C: American Meteorological Society: 165-170.
[33] KAIN J S, BALDWIN M E, WEISS S J. 2003. Parameterized updraft mass flux as a predictor of convective intensity[J]. Weather and Forecasting, 18: 106-116.
[34] MACKEY B P, KRISHNAMURTI T N. 2001. Ensemble forecast of a typhoon flood event[J]. Weather and Forecasting, 16: 399-415.
[35] PAN HUALU, WU WANSHU. 1995. Implementing a mass flux convection parameterization package for the NMC medium- range forecast model[R]. Silver Spring, Maryland: NMC Office Note 409: 39.
[36] PURI K, BARKMEIJER J, PALMER T N. 2001. Ensemble prediction of tropical cyclones using targeted diabatic singular vectors[J]. Quarterly Journal of the Royal Meteorological Society, 127: 709-731.
[37] SIPPEL J A, ZHANG FUQING. 2008. A probabilistic analysis of the dynamics and predictability of tropical cyclogenesis[J]. Journal of the Atmospheric Sciences, 65: 3440-3459.
[38] VIJAYA KUMAR T S V, KRISHNAMURTI T N, FIORINO M, et al. 2003. Multimodel superensemble forecasting of tropical cyclones in the Pacific[J]. Monthly Weather Review, 131: 574-583.
[39] YAMAGUCHI M, SAKAI R, KYODA M, et al. 2009. Typhoon ensemble prediction system developed at the Japan meteorological agency[J]. Monthly Weather Review, 137: 2592-2604.
[40] ZHANG FUQING, SIPPEL J A. 2009. Effects of moist convection on hurricane predictability[J]. Journal of the Atmospheric Sciences, 66: 1944-1961.
[41] ZHANG ZHAN, KRISHNAMURTI T N. 1997. Ensemble forecasting of hurricane tracks[J]. Bulletin of the American Meteorological Society, 78: 2785-2795.
[42] ZHANG ZHAN, KRISHNAMURTI T N. 1999. A perturbation method for hurricane ensemble predictions[J]. Monthly Weather Review, 127: 447-469.
[43] ZIEHMANN C. 2000. Comparison of a single-model EPS with a multi-model ensemble consisting of a few operational models[J]. Tellus, 52A: 280-299.
