Advanced Search
Impact Factor: 5.8

Volume 26 Issue 3

May  2009

Article Contents
Article >  ADVANCES IN ATMOSPHERIC SCIENCES  > 2009 >  26(3): 465-470

A Comparison Study of the Methods of Conditional Nonlinear Optimal Perturbations and Singular Vectors in Ensemble Prediction

  • 1.

    State Key Laboratory of Severe Weather (LaSW), Chinese Academy of Meteorological Sciences, Beijing 100081; State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics, Chinese;State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics ( LASG), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029


doi: 10.1007/s00376-009-0465-6

  • The authors apply the technique of conditional nonlinear optimal perturbations (CNOPs) as a means of providing initial perturbations for ensemble forecasting by using a barotropic quasi-geostrophic (QG) model in a perfect-model scenario. Ensemble forecasts for the medium range (14 days) are made from the initial states perturbed by CNOPs and singular vectors (SVs). 13 different cases have been chosen when analysis error is a kind of fast growing error. Our experiments show that the introduction of CNOP provides better forecast skill than the SV method. Moreover, the spread-skill relationship reveals that the ensemble samples in which the first SV is replaced by CNOP appear superior to those obtained by SVs from day 6 to day 14. Rank diagrams are adopted to compare the new method with the SV approach. The results illustrate that the introduction of CNOP has higher reliability for medium-range ensemble forecasts.
  • [1] Sijia LI, Yuan WANG, Huiling YUAN, Jinjie SONG, Xin XU, 2016: Ensemble Mean Forecast Skill and Applications with the T213 Ensemble Prediction System, ADVANCES IN ATMOSPHERIC SCIENCES, 33, 1297-1305.  doi: 10.1007/s00376-016-6155-2
    [2] Jun Kyung KAY, Hyun Mee KIM, Young-Youn PARK, Joohyung SON, 2013: Effect of Doubling the Ensemble Size on the Performance of Ensemble Prediction in the Warm Season Using MOGREPS Implemented at the KMA, ADVANCES IN ATMOSPHERIC SCIENCES, 30, 1287-1302.  doi: 10.1007/s00376-012-2083-y
    [3] TAN Jiqing, XIE Zhenghui, JI Liren, 2003: A New Way to Predict Forecast Skill, ADVANCES IN ATMOSPHERIC SCIENCES, 20, 837-841.  doi: 10.1007/BF02915409
    [4] Zheng HE, Pangchi HSU, Xiangwen LIU, Tongwen WU, Yingxia GAO, 2019: Factors Limiting the Forecast Skill of the Boreal Summer Intraseasonal Oscillation in a Subseasonal-to-Seasonal Model, ADVANCES IN ATMOSPHERIC SCIENCES, 36, 104-118.  doi: 10.1007/s00376-018-7242-3
    [5] Xiaogu ZHENG, 2009: An Adaptive Estimation of Forecast Error Covariance Parameters for Kalman Filtering Data Assimilation, ADVANCES IN ATMOSPHERIC SCIENCES, 26, 154-160.  doi: 10.1007/s00376-009-0154-5
    [6] Qian ZOU, Quanjia ZHONG, Jiangyu MAO, Ruiqiang DING, Deyu LU, Jianping LI, Xuan LI, 2023: Impact of Perturbation Schemes on the Ensemble Prediction in a Coupled Lorenz Model, ADVANCES IN ATMOSPHERIC SCIENCES, 40, 501-513.  doi: 10.1007/s00376-022-1376-z
    [7] Zhizhen XU, Jing CHEN, Zheng JIN, Hongqi LI, Fajing CHEN, 2020: Representing Model Uncertainty by Multi-Stochastic Physics Approaches in the GRAPES Ensemble, ADVANCES IN ATMOSPHERIC SCIENCES, 37, 328-346.  doi: 10.1007/s00376-020-9171-1
    [8] Temesgen Gebremariam ASFAW, Jing-Jia LUO, 2022: Seasonal Prediction of Summer Precipitation over East Africa Using NUIST-CFS1.0, ADVANCES IN ATMOSPHERIC SCIENCES, 39, 355-372.  doi: 10.1007/s00376-021-1180-1
    [9] LIU Xiangwen, WU Tongwen, YANG Song, JIE Weihua, NIE Suping, LI Qiaoping, CHENG Yanjie, LIANG Xiaoyun, 2015: Performance of the Seasonal Forecasting of the Asian Summer Monsoon by BCC_CSM1.1(m), ADVANCES IN ATMOSPHERIC SCIENCES, 32, 1156-1172.  doi: 10.1007/s00376-015-4194-8
    [10] Gill M. MARTIN, Nick J. DUNSTONE, Adam A. SCAIFE, Philip E. BETT, 2020: Predicting June Mean Rainfall in the Middle/Lower Yangtze River Basin, ADVANCES IN ATMOSPHERIC SCIENCES, 37, 29-41.  doi: 10.1007/s00376-019-9051-8
    [11] Jing WANG, Bin WANG, Juanjuan LIU, Yongzhu LIU, Jing CHEN, Zhenhua HUO, 2020: Application and Characteristic Analysis of the Moist Singular Vector in GRAPES-GEPS, ADVANCES IN ATMOSPHERIC SCIENCES, 37, 1164-1178.  doi: 10.1007/s00376-020-0092-9
    [12] Chen Jiabin, A.J. Simmons, 1990: Sensitivity of Medium-Range Weather Forecasts to the Use of Reference Atmosphere, ADVANCES IN ATMOSPHERIC SCIENCES, 7, 275-293.  doi: 10.1007/BF03179761
    [13] Qiu Yongyan, Zhu Yafen, 1987: MEDIUM-RANGE OSCILLATIONS OF SYNOPTIC SYSTEMS IN SUMMER, ADVANCES IN ATMOSPHERIC SCIENCES, 4, 395-402.  doi: 10.1007/BF02656740
    [14] Masoud NOBAKHT, Bahram SAGHAFIAN, Saleh AMINYAVARI, 2021: Skill Assessment of Copernicus Climate Change Service Seasonal Ensemble Precipitation Forecasts over Iran, ADVANCES IN ATMOSPHERIC SCIENCES, 38, 504-521.  doi: 10.1007/s00376-020-0025-7
    [15] Lu Jingxi, Ding Yihui, 1989: Medium-Range Oscillations in the Summer Tropical Easterlies at 200 hPa, ADVANCES IN ATMOSPHERIC SCIENCES, 6, 301-312.  doi: 10.1007/BF02661536
    [16] Jiangshan ZHU, Fanyou KONG, Xiao-Ming HU, Yan GUO, Lingkun RAN, Hengchi LEI, 2018: Impact of Soil Moisture Uncertainty on Summertime Short-range Ensemble Forecasts, ADVANCES IN ATMOSPHERIC SCIENCES, 35, 839-852.  doi: 10.1007/s00376-017-7107-1
    [17] Swati Basu, K. J. Ramesh, Z. N. Begum, 1999: Medium Range Prediction of Summer Monsoon Activities over India vis-a-vis Their Correspondence with the Observational Features, ADVANCES IN ATMOSPHERIC SCIENCES, 16, 133-146.  doi: 10.1007/s00376-999-0009-0
    [18] Chen Longxun, Jin Zuhui, 1984: THE MEDIUM-RANGE VARIATIONS OF THE SUMMER MONSOON CIRCULATION SYSTEM OVER EAST ASIA, ADVANCES IN ATMOSPHERIC SCIENCES, 1, 224-245.  doi: 10.1007/BF02678135
    [19] Lu Longhua, Chen Xianji, Zhu Fukang, 1985: THE INTERANNUAL VARIATION OF MEDIUM-RANGE OSCILLATION CHARACTERISTICS IN THE UPPER TROPOSPHERE OVER THE SUBTROPICAL REGION, ADVANCES IN ATMOSPHERIC SCIENCES, 2, 54-62.  doi: 10.1007/BF03179737
    [20] Huang Boyin, Zhao Gaoxiang, Ji Liren, 1992: A Numerical Research on the Influences of the Diurnal Variation of Solar Radiation on the Medium-Range Weather Processes, ADVANCES IN ATMOSPHERIC SCIENCES, 9, 231-236.  doi: 10.1007/BF02657513
PDF

Get Citation+

shu
Export:  

Share Article

Article Metrics

Article Views: 1129 Times

PDF downloads: 1247 Times

Cited by: Times
  • 加载中

    • Manuscript History

    Manuscript received: 10 May 2009
    Manuscript revised: 10 May 2009
    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    A Comparison Study of the Methods of Conditional Nonlinear Optimal Perturbations and Singular Vectors in Ensemble Prediction

    • 1. State Key Laboratory of Severe Weather (LaSW), Chinese Academy of Meteorological Sciences, Beijing 100081; State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics, Chinese;State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics ( LASG), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029
    • Manuscript received: 2009-05-10
    • Manuscript revised: 2009-05-10

    Abstract: The authors apply the technique of conditional nonlinear optimal perturbations (CNOPs) as a means of providing initial perturbations for ensemble forecasting by using a barotropic quasi-geostrophic (QG) model in a perfect-model scenario. Ensemble forecasts for the medium range (14 days) are made from the initial states perturbed by CNOPs and singular vectors (SVs). 13 different cases have been chosen when analysis error is a kind of fast growing error. Our experiments show that the introduction of CNOP provides better forecast skill than the SV method. Moreover, the spread-skill relationship reveals that the ensemble samples in which the first SV is replaced by CNOP appear superior to those obtained by SVs from day 6 to day 14. Rank diagrams are adopted to compare the new method with the SV approach. The results illustrate that the introduction of CNOP has higher reliability for medium-range ensemble forecasts.

      HTML

    Catalog

      Publish with AAS

      Contact us

      Links

      Copyright © 2018-2028 ADVANCES IN ATMOSPHERIC SCIENCES 京ICP备14024088号-7

      Supported by: Beijing Renhe Information Technology Co. Ltd  support: info@rhhz.net  

      /

      DownLoad:  Full-Size Img  PowerPoint
      Return
      Return