“梅花”台风路径与强度的集合预报

doi:10.11978/j.issn.1009-5470.2014.06.007

Abstract

CLC Number:

P732.42

WANG Chen-xi. Ensemble prediction of typhoon Muifa’s track and intensity[J].Journal of Tropical Oceanography, 2014, 33(6): 48-60.

[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.

Ensemble prediction of typhoon Muifa’s track and intensity

PDF (PC)

Like

Knowledge

Cited

Abstract

Cite this article

share this article

References

Related Articles 6

Metrics

Comments

Recommended 0

[1]	LI Zhuo, LI Weibiao, ZHANG Aoqi. Analysis of diurnal variation characteristics of precipitation over South China before typhoon landfall [J]. Journal of Tropical Oceanography, 2022, 41(2): 26-37.
[2]	ZHU Wentao, XIA Jingquan, LIU Xiangbo, YIN Hongyang, ZHU Ming, REN Yuxiao, XIE Minrui, HUANG Jianzhong, LI Xiubao. Analysis of photosynthetic physiology and symbiotic fungi community in Galaxea fascicularis [J]. Journal of Tropical Oceanography, 2022, 41(2): 132-141.
[3]	Juan Li, Junliang Liu, Shuqun Cai. Numerical simulation of oceanic near-inertial energy induced by Typhoon Conson [J]. Journal of Tropical Oceanography, 2020, 39(2): 35-43.
[4]	Peini QI, Feifei SHEN, Leilei KOU, Zhigang CHU, Dongmei XU. Experiments of assimilating Doppler radar data in forecast of Typhoon Chanthu [J]. Journal of Tropical Oceanography, 2019, 38(2): 20-31.
[5]	ZHANG Guang-ping, XIE Zhong, LUO Xian-gang, ZHANG Chen-xiao. The Hainan typhoon disaster command management systems based on WebGIS [J]. Journal of Tropical Oceanography, 2014, 33(6): 80-87.
[6]	LIU Bei, LU Shao-zong, QIAN Yu-kun, PENG Shi-qiu. An application of ATOVS radiance in data assimilation of typhoon [J]. Journal of Tropical Oceanography, 2014, 33(1): 44-53.