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Impact of perturbation schemes on the ensemble prediction in a coupled Lorenz model

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This work was jointly supported by the National Natural Science Foundation of China (Grant No. 41790474, 41975070)


doi:  10.1007/s00376-022-1376-z

  • Based on a simple coupled Lorenz model, we investigate how to consider a suitable initial perturbation scheme for ensemble forecasting in a multiscale system involving slow dynamics and fast dynamics. Four initial perturbation approaches are used in the ensemble forecasting experiments: random perturbation (RP), the bred vector (BV), the ensemble transform Kalman filter (ETKF) and the nonlinear local Lyapunov vector (NLLV) methods. Results show that, regardless of the method used, the ensemble averages behave indistinguishably from the control forecasts during the first few time steps. Due to different error growth in different time-scale systems, the ensemble averages perform better than the control forecast after a very short period of lead time in a fast subsystem, but after a relatively long period of time in a slow subsystem. As a result of coupled dynamic processes, whether adding perturbations to fast variables or to slow variables can contribute to an improvement in the forecasting skill for fast variables and slow variables. When it comes to the initial perturbation approaches, the NLLVs show higher forecasting skill than BVs or RPs overall. NLLVs and ETKFs had nearly equivalent prediction skill, and NLLVs won by a narrow margin. In particular, when adding perturbations to slow variables, independent perturbations (NLLVs and ETKFs) perform much better in the ensemble prediction. These results are simply implied in a real coupled air–sea model. For the prediction of oceanic variables, independent perturbations (NLLVs) and adding perturbations to oceanic variables will be expected to perform better in the ensemble prediction.
  • [1] Keon Tae SOHN, Sun Min PARK, 2008: Guidance on the Choice of Threshold for Binary Forecast Modeling, ADVANCES IN ATMOSPHERIC SCIENCES, 25, 83-88.  doi: 10.1007/s00376-008-0083-8
    [2] YOU Wei, ZANG Zengliang, PAN Xiaobin, ZHANG Lifeng, LI Yi, 2015: Statistical Analysis of Thunderstorms on the Eastern Tibetan Plateau Based on Modified Thunderstorm Indices, ADVANCES IN ATMOSPHERIC SCIENCES, 32, 515-527.  doi: 10.1007/s00376-014-4039-x
    [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] 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
    [5] 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
    [6] Yan Shaojin, Peng Yongqing, Guo guang, 1995: Neuroid BP-type Model Applied to the Study of Monthly Rainfall Forecasting, ADVANCES IN ATMOSPHERIC SCIENCES, 12, 335-342.  doi: 10.1007/BF02656982
    [7] CHEN Lianshou, 2004: An Overview of Tropical Cyclone and Tropical Meteorology Research Progress, ADVANCES IN ATMOSPHERIC SCIENCES, 21, 505-514.  doi: 10.1007/BF02915577
    [8] Matthew A. LAZZARA, Jordan G. POWERS, Carol A. COSTANZA, David H. BROMWICH, Scott CARPENTIER, Steve R. COLWELL, 2018: The 12 th Workshop on Antarctic Meteorology and Climate, ADVANCES IN ATMOSPHERIC SCIENCES, 35, 753-756.  doi: 10.1007/s00376-018-8061-2
    [9] Ji Zhongzhen, Wang Bin, 1993: Constructions and Applied Examinations of a Kind of Square-Conservative Schemes in High Precision in the Time Direction, ADVANCES IN ATMOSPHERIC SCIENCES, 10, 315-324.  doi: 10.1007/BF02658137
    [10] WANG Dongxiao, QIN Zenghao, SHI Ping, 2004: Progress in Marine Meteorology Studies in China during 1999-2002, ADVANCES IN ATMOSPHERIC SCIENCES, 21, 485-496.  doi: 10.1007/BF02915575
    [11] Gong Jiuding, 1986: APPLICATION OF MULTI-DIMENSIONAL SEQUENCE SIMILARITY METHOD IN METEOROLOGY, ADVANCES IN ATMOSPHERIC SCIENCES, 3, 94-104.  doi: 10.1007/BF02680048
    [12] WANG Dongxiao, ZHANG Yan, ZENG Lili, LUO Lin, 2009: Marine Meteorology Research Progress of China from 2003 to 2006, ADVANCES IN ATMOSPHERIC SCIENCES, 26, 17-30.  doi: 10.1007/s00376-009-0017-0
    [13] Jiping LIU, David BROMWICH, Dake CHEN, Raul CORDERO, Thomas JUNG, Marilyn RAPHAEL, John TURNER, Qinghua YANG, 2020: Preface to the Special Issue on Antarctic Meteorology and Climate: Past, Present and Future, ADVANCES IN ATMOSPHERIC SCIENCES, 37, 421-422.  doi: 10.1007/s00376-020-2001-7
    [14] Matthew A. LAZZARA, Sophie A. ORENDORF, Taylor P. NORTON, Jordan G. POWERS, David H. BROMWICH, Scott CARPENTIER, John J. CASSANO, Steven R. COLWELL, Arthur M. CAYETTE, Kirstin WERNER, 2020: The 13th and 14th Workshops on Antarctic Meteorology and Climate, ADVANCES IN ATMOSPHERIC SCIENCES, 37, 423-430.  doi: 10.1007/s00376-019-9215-6
    [15] 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
    [16] Ngar-Cheung LAUInstitute of Environment, Energy and Sustainability, and Department of Geography and Resource Management, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong, 2017: The Pioneering Works of Professor Duzheng YE on Atmospheric Dispersion, Tibetan Plateau Meteorology, and Air-Sea Interaction, ADVANCES IN ATMOSPHERIC SCIENCES, 34, 1137-1149.  doi: 10.1007/s00376-017-6256-6
    [17] David H. BROMWICH, Matthew A. LAZZARA, Arthur M. CAYETTE, Jordan G. POWERS, Kirstin WERNER, John J. CASSANO, Steven R. COLWELL, Scott CARPENTIER, Xun ZOU, 2022: The 16th Workshop on Antarctic Meteorology and Climate and 6th Year of Polar Prediction in the Southern Hemisphere Meeting, ADVANCES IN ATMOSPHERIC SCIENCES, 39, 536-542.  doi: 10.1007/s00376-021-1384-4
    [18] Jiehong XIE, Jinhua YU, Haishan CHEN, Pang-Chi HSU, 2020: Sources of Subseasonal Prediction Skill for Heatwaves over the Yangtze River Basin Revealed from Three S2S Models, ADVANCES IN ATMOSPHERIC SCIENCES, 37, 1435-1450.  doi: 10.1007/s00376-020-0144-1
    [19] XIAO Ziniu, LIU Hua, ZHANG De, 2012: Progress in Climate Prediction and Weather Forecast Operations in China, ADVANCES IN ATMOSPHERIC SCIENCES, 29, 943-957.  doi: 10.1007/s00376-012-1194-9
    [20] Keon Tae SOHN, Jeong Hyeong LEE, Young Seuk CHO, 2009: Ternary Forecast of Heavy Snowfall in the Honam Area, Korea, ADVANCES IN ATMOSPHERIC SCIENCES, 26, 327-332.  doi: 10.1007/s00376-009-0327-2

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Manuscript History

Manuscript received: 23 September 2021
Manuscript revised: 19 May 2022
Manuscript accepted: 01 June 2022
通讯作者: 陈斌, bchen63@163.com
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Impact of perturbation schemes on the ensemble prediction in a coupled Lorenz model

Abstract: Based on a simple coupled Lorenz model, we investigate how to consider a suitable initial perturbation scheme for ensemble forecasting in a multiscale system involving slow dynamics and fast dynamics. Four initial perturbation approaches are used in the ensemble forecasting experiments: random perturbation (RP), the bred vector (BV), the ensemble transform Kalman filter (ETKF) and the nonlinear local Lyapunov vector (NLLV) methods. Results show that, regardless of the method used, the ensemble averages behave indistinguishably from the control forecasts during the first few time steps. Due to different error growth in different time-scale systems, the ensemble averages perform better than the control forecast after a very short period of lead time in a fast subsystem, but after a relatively long period of time in a slow subsystem. As a result of coupled dynamic processes, whether adding perturbations to fast variables or to slow variables can contribute to an improvement in the forecasting skill for fast variables and slow variables. When it comes to the initial perturbation approaches, the NLLVs show higher forecasting skill than BVs or RPs overall. NLLVs and ETKFs had nearly equivalent prediction skill, and NLLVs won by a narrow margin. In particular, when adding perturbations to slow variables, independent perturbations (NLLVs and ETKFs) perform much better in the ensemble prediction. These results are simply implied in a real coupled air–sea model. For the prediction of oceanic variables, independent perturbations (NLLVs) and adding perturbations to oceanic variables will be expected to perform better in the ensemble prediction.

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