基于TIGGE数据的西太平洋副热带高压多模式集成预报及检验

doi:10.3969/j.issn.0253-2778.2017.05.004

中国科学技术大学学报 ›› 2017, Vol. 47 ›› Issue (5): 392-402.DOI: 10.3969/j.issn.0253-2778.2017.05.004

基于TIGGE数据的西太平洋副热带高压多模式集成预报及检验

颜妍，周任君，柯宗建，刘长征，杜良敏，苏琪骅

1.中国科学技术大学地球与空间科学学院，安徽合肥 230026；
2.中国气象局国家气候中心，北京 100081；3.武汉区域气候中心，湖北武汉 430074

收稿日期:2017-02-21 修回日期:2017-04-12 出版日期:2017-05-31 发布日期:2017-05-31
通讯作者: 周任君
作者简介:颜妍，女，1992年生，硕士生. 研究方向：多模式集成预报. E-mail：yany519@outlook.com
基金资助:
国家自然科学基金（91437105）,公益性行业（气象）科研专项（GYHY201306024）资助.

Ensemble forecast and verification of the Western Pacific Subtropical High based on multi-model data from TIGGE

YAN Yan, ZHOU Renjun, KE Zongjian, LIU Changzheng, DU Liangmin, SU Qihua

1. School of Earth and Space Sciences, University of Science and Technology of China, Hefei 230016,China;
2. National Climate Center, China Meteorological Administration, Beijing 100081,China;
3. Wuhan Regional Climate Center, Wuhan 430074,China

Received:2017-02-21 Revised:2017-04-12 Online:2017-05-31 Published:2017-05-31

摘要/Abstract

摘要： 基于TIGGE(THORPEX Interactive Grand Global Ensemble)资料中的中国气象局（CMA）、日本气象厅（JMA）、欧洲中期天气预报中心（ECMWF）、美国国家环境预报中心（NCEP）和英国气象局（UKMO）等5个中心的500 hPa位势高度场数据，评估了各中心对西太副高控制预报和集合预报的效果，并采用了多模式集成平均（EMN）、消除偏差集成平均（BREM）和滑动训练期超级集合（R_SUP）3种方法对各中心数据进行集成.评估方法包括Talagrand分布、相关系数、均方根误差、Brier技巧评分等.结果表明：各中心预报效果有明显差异，各模式对500 hPa位势高度场控制预报中，UKMO预报效果最好，而各模式对500 hPa位势高度场集合预报中，则是ECMWF预报效果最好.从均方根误差改进率来看，基于控制预报的BREM和R_SUP集成方法明

关键词: TIGGE, 西太副高, 多模式集成, 预报

Abstract: The skill of a set of control and ensemble forecasts of Western Pacific Subtropical High was evaluated based on the 500 hPa geopotential height information from the THORPEX Interactive Grand Global Ensemble (TIGGE) datasets, which consist of model outputs from CMA, JMA, ECMWF, UKMO and NCEP. Three methods were adopted, i.e., Ensemble Mean (EMN), Bias-Removed Ensemble Mean (BREM) and running Training Period Superensemble (R_SUP), to integrate the data from different sources, and the metrics for performance evaluation include Talagrand distribution, correlation coefficient, Root Mean Square Error (RMSE), and Brier Skill Score (BSS). A comparison of the outputs of these models shows significant variation in forecast performance. The results indicate that the UKMO model has the best forecast skill for the 500 hPa geopotential height among all control forecasts, while the ECMWF model ranks on the top of all ensemble forecasts. From the improvement of RMSE, both BREM and R_SUP can significantly reduce the RMSE of the integrated forecast results compared to the original control forecasts in TIGGE, but EMN does not show similar improvement. However, none of the three integration methods shows discernable improvement of ensemble forecast of the 500 hPa geopotential height, with all having less skills than ECMWF single model ensemble forecast.

Key words: TIGGE, WPSH, multi-model ensemble, forecast

中图分类号:

TP 391

颜妍，周任君，柯宗建，刘长征，杜良敏，苏琪骅. 基于TIGGE数据的西太平洋副热带高压多模式集成预报及检验[J]. 中国科学技术大学学报, 2017, 47(5): 392-402.

YAN Yan, ZHOU Renjun, KE Zongjian, LIU Changzheng, DU Liangmin, SU Qihua. Ensemble forecast and verification of the Western Pacific Subtropical High based on multi-model data from TIGGE[J]. Journal of University of Science and Technology of China, 2017, 47(5): 392-402.

参考文献

［1］
朱乾根, 林锦瑞，寿绍文,等. 天气学原理和方法[M]. 4版. 北京：气象出版社，2007: 475-484.
[2] 黄士松, 余志豪. 副热带高压结构及其同大气环流有关若干问题的研究[J]. 气象学报, 1962, 31(4): 339-359.
[3] FUJITA T T, WATANABE K, IZAWA T. Formation and structure of equatorial anticyclones caused by large-scale cross-equatorial flows determined by ATS-I photographs[J]. Journal of Applied Meteorology, 1969, 8(4): 649-667.
[4] SADLER J C, BRETT W R, HARRIS B E, et al. Forecasting minimum cloudiness over the Red River Delta during the summer monsoon[R]. Honolulu：Hawaii Institute of Geophysics, 1968.
[5] 陶诗言, 朱福康. 夏季亚洲南部 100 毫巴流型的变化及其与西太平洋副热带高压进退的关系[J]. 气象学报, 1964, 34(4): 385-396.
[6] 陶诗言, 王作述, 朱福康. 中国夏季副热带天气系统若干问题的研究[M]. 北京：科学出版社,1963.
[7] HUANG R, CHEN J, WANG L, et al. Characteristics, processes, and causes of the spatio-temporal variabilities of the East Asian monsoon system[J]. Advances in Atmospheric Sciences, 2012, 29(5): 910-942.
[8] HE C, ZHOU T, WU B. The key oceanic regions responsible for the interannual variability of the western North Pacific subtropical high and associated mechanisms[J]. Journal of Meteorological Research, 2015, 29(4): 562-575.
[9] MATSUMURA S, HORINOUCHI T. Pacific Ocean decadal forcing of long-term changes in the western Pacific subtropical high[J]. Scientific Reports, 2016（6）：37765.
[10] 吴国雄, 丑纪范, 刘屹岷, 等. 副热带高压形成和变异的动力学问题[M]. 北京: 科学出版社, 2002: 312.
[11] HE Jinhai, ZHOU Bing, WEN Min, et al. Vertical circulation structure,interannual variation features and variation mechanism of western Pacific subtropical high[J]. Advances in Atmospheric Sciences, 2001, 18(4): 497-510.
[12] 赵声蓉, 宋正山. 华北汛期旱涝与中高纬大气环流异常[J]. 高原气象, 1999, 18(4): 535-540.
[13] 韦道明, 李崇银, 谭言科. 夏季西太平洋副热带高压南北位置变动特征及其影响[J]. 气候与环境研究, 2011, 16(3): 255-272.
[14] 任广成, 吴小林, 李旺. 盛夏副高脊线异常变化对我国气温影响及海气背景分析[J]. 气象与环境科学, 2009, 32(3): 1-5.
[15] 张韧. 基于前传式网络逼近的太平洋副热带高压活动的诊断预测[J]. 大气科学, 2001, 25（5）：650-660.
[16] 刘科峰, 张韧, 洪梅, 等. 基于最小二乘支持向量机的副热带高压预测模型[J]. 应用气象学报, 2009, 20(3): 354.
[17] 杨杰, 封国林, 赵俊虎, 等. 夏季西太平洋副热带高压的客观定量化预测及其对汛期降水的指示[J]. 气象学报, 2012, 70(5): 1032.
[18] 贾亚俊, 胡轶佳, 钟中, 等. 夏季西太平洋副热带高压指数的统计预测模型[J]. 高原气象, 2015 (5): 1369-1378.
[19] SUI C H, CHUNG P H, LI T. Interannual and interdecadal variability of the summertime western North Pacific subtropical high[J]. Geophysical Research Letters, 2007, 34(11) :93-104.
[20] LU R, DING H, RYU C S, et al. Midlatitude westward propagating disturbances preceding intraseasonal oscillations of convection over the subtropical western North Pacific during summer[J]. Geophysical Research Letters, 2007, 34(21) :393-407.
[21] ZHANG Z, KRISHNAMURTI T N. Ensemble forecasting of hurricane tracks[J]. Bulletin of the American Meteorological Society, 1997, 78(12): 2785-2795.
[22] 郑飞. ENSO集合预报研究[D]. 北京：中国科学院研究生院（大气物理研究所）,2007.
[23] 周文友,智协飞.2009年夏季西太平洋台风路径和强度的多模式集成预报[J].气象科学,2012,32(5): 492-499．
[24] 林春泽, 智协飞, 韩艳, 等. 基于 TIGGE 资料的地面气温多模式超级集合预报[J]. 应用气象学报, 2009，20（6）:706-712.
[25] ZHI X, QI H, BAI Y, et al. A comparison of three kinds of multimodel ensemble forecast techniques based on the TIGGE data[J]. Acta Meteorologica Sinica, 2012, 26: 41-51.
[26] 崔慧慧, 智协飞. 基于 TIGGE 资料的地面气温延伸期多模式集成预报[J]. 大气科学学报, 2013, 36(2): 165-173.
[27] 智协飞, 季晓东, 张璟, 等. 基于 TIGGE 资料的地面气温和降水的多模式集成预报[J]. 大气科学学报, 2013, 36(3): 257-266.
[28] RICHARDSON D, BUIZZA R, HAGEDORN R. Final report of the 1st Workshop on the THORPEX Interactive Grand Global Ensemble (TIGGE)[R]. World Meteorological Organization WMO/TD, 2005.
[29] Sounding data[DB/OL].[2017-01-20].http://weather.uwyo.edu/upperair/seasia.html
[30] TALAGRAND O, VAUTARD R, STRAUSS B. Evaluation of probabilistic prediction systems[C]//Proceedings of ECMWF Workshop on Predictability, 1997, 1: 25.
[31] BRIER G W. Verification of forecasts expressed in terms of probability[J]. Monthly Weather Review, 1950, 78(1): 1-3.
[32] 李莉, 李应林, 田华, 等. T213 全球集合预报系统性误差订正研究[J]. 气象, 2011, 37(1): 31-38.
[33] HAMILL T M, D'ENTREMONT R P, BUNTIN J T. A description of the air force real-time nephanalysis model[J]. Weather and Forecasting, 1992, 7(2): 288-306.
[34] HAMILL T M, WHITAKER J S, SNYDER C. Distance-dependent filtering of background error covariance estimates in an ensemble Kalmanfilter[J]. Monthly Weather Review, 2001, 129(11): 2776-2790.
[35] CARTWRIGHT T J, KRISHNAMURTI T N. Warm season mesoscale superensemble precipitation forecasts in the southeastern United States[J]. Weather and Forecasting, 2007, 22(4): 873-886.
[36] KRISHNAMURTI T N, KISHTAWAL C M, ZHANG Z, et al. Multimodel ensemble forecasts for weather and seasonal climate[J]. Journal of Climate, 2000, 13(23): 4196-4216.
[37] HE C, ZHI X, YOU Q, et al. Multi-model ensemble forecasts of tropical cyclones in 2010 and 2011 based on the Kalman Filter method[J]. Meteorology and Atmospheric Physics, 2015, 127(4): 467-479.
[38] MURPHY A H, EPSTEIN E S. Skill scores and correlation coefficients in model verification[J]. Monthly Weather Review, 1989, 117(3): 572-582.
[39] NIU R, ZHAI P. Synoptic verification of medium-extended-range forecasts of the Northwest Pacific subtropical high and South Asian high based on multi-center TIGGE data[J]. Acta Meteorologica Sinica, 2013, 27(5): 725-741.
[40] NIU R, ZHAI P, ZHOU B. Evaluation of forecast performance of Asian summer monsoon low-level winds using the TIGGE dataset[J]. Weather and Forecasting, 2015, 30(2): 455-470.

基于TIGGE数据的西太平洋副热带高压多模式集成预报及检验

Ensemble forecast and verification of the Western Pacific Subtropical High based on multi-model data from TIGGE

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 1

编辑推荐

Metrics

本文评价