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Volume 22 Issue 6

Nov.  2005

Article Contents
Article >  ADVANCES IN ATMOSPHERIC SCIENCES  > 2005 >  22(6): 781-788

Ensemble Forecast: A New Approach to Uncertainty and Predictability

  • 1.

    Environmental Modeling Center, NCEP/NWS/NOAA, Washington DC, USA


doi: 10.1007/BF02918678

  • Ensemble techniques have been used to generate daily numerical weather forecasts since the 1990s in numerical centers around the world due to the increase in computation ability. One of the main purposes of numerical ensemble forecasts is to try to assimilate the initial uncertainty (initial error) and the forecast uncertainty (forecast error) by applying either the initial perturbation method or the multi-model/multiphysics method. In fact, the mean of an ensemble forecast offers a better forecast than a deterministic (or control) forecast after a short lead time (3 5 days) for global modelling applications. There is about a 1-2-day improvement in the forecast skill when using an ensemble mean instead of a single forecast for longer lead-time. The skillful forecast (65% and above of an anomaly correlation) could be extended to 8 days (or longer) by present-day ensemble forecast systems. Furthermore, ensemble forecasts can deliver a probabilistic forecast to the users, which is based on the probability density function (PDF)instead of a single-value forecast from a traditional deterministic system. It has long been recognized that the ensemble forecast not only improves our weather forecast predictability but also offers a remarkable forecast for the future uncertainty, such as the relative measure of predictability (RMOP) and probabilistic quantitative precipitation forecast (PQPF). Not surprisingly, the success of the ensemble forecast and its wide application greatly increase the confidence of model developers and research communities.
  • [1] LI Shan, RONG Xingyao, LIU Yun, LIU Zhengyu, Klaus FRAEDRICH, 2013: Dynamic Analogue Initialization for Ensemble Forecasting, ADVANCES IN ATMOSPHERIC SCIENCES, 30, 1406-1420.  doi: 10.1007/s00376-012-2244-z
    [2] 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
    [3] Se-Hwan YANG, LI Chaofan, and LU Riyu, 2014: Predictability of Winter Rainfall in South China as Demonstrated by the Coupled Models of ENSEMBLES, ADVANCES IN ATMOSPHERIC SCIENCES, 31, 779-786.  doi: 10.1007/s00376-013-3172-2
    [4] BEI Naifang, Fuqing ZHANG, 2014: Mesoscale Predictability of Moist Baroclinic Waves: Variable and Scale-dependent Error Growth, ADVANCES IN ATMOSPHERIC SCIENCES, 31, 995-1008.  doi: 10.1007/s00376-014-3191-7
    [5] SONG Xiang and ZENG Xiaodong*, , 2014: Investigation of Uncertainties of Establishment Schemes in Dynamic Global Vegetation Models, ADVANCES IN ATMOSPHERIC SCIENCES, 31, 85-94.  doi: 10.1007/s00376-013-3031-1
    [6] Feifan ZHOU, Wansuo DUAN, He ZHANG, Munehiko YAMAGUCHI, 2018: Possible Sources of Forecast Errors Generated by the Global/Regional Assimilation and Prediction System for Landfalling Tropical Cyclones. Part II: Model Uncertainty, ADVANCES IN ATMOSPHERIC SCIENCES, 35, 1277-1290.  doi: 10.1007/s00376-018-7095-9
    [7] Ruiqiang DING, Baojia LIU, Bin GU, Jianping LI, Xuan LI, 2019: Predictability of Ensemble Forecasting Estimated Using the Kullback-Leibler Divergence in the Lorenz Model, ADVANCES IN ATMOSPHERIC SCIENCES, , 837-846.  doi: 10.1007/s00376-019-9034-9
    [8] ZHU Jiang, LIN Caiyan, WANG Zifa, 2009: Dust Storm Ensemble Forecast Experiments in East Asia, ADVANCES IN ATMOSPHERIC SCIENCES, 26, 1053-1070.  doi: 10.1007/s00376-009-8218-0
    [9] T. N. Krishnamurti, Mukul Tewari, Ed Bensman, Wei Han, Zhan Zhang, William K. M. Lau, 1999: An Ensemble Forecast of the South China Sea Monsoon, ADVANCES IN ATMOSPHERIC SCIENCES, 16, 159-182.  doi: 10.1007/BF02973080
    [10] Chenxi WANG, Zhihua ZENG, Ming YING, 2020: Uncertainty in Tropical Cyclone Intensity Predictions due to Uncertainty in Initial Conditions, ADVANCES IN ATMOSPHERIC SCIENCES, 37, 278-290.  doi: 10.1007/s00376-019-9126-6
    [11] Jianguo LIU, Binghao JIA, Zhenghui XIE, Chunxiang SHI, 2016: Ensemble Simulation of Land Evapotranspiration in China Based on a Multi-Forcing and Multi-Model Approach, ADVANCES IN ATMOSPHERIC SCIENCES, 33, 673-684.  doi: 10.1007/s00376-016-5213-0
    [12] WANG Huijun, FAN Ke, SUN Jianqi, LI Shuanglin, LIN Zhaohui, ZHOU Guangqing, CHEN Lijuan, LANG Xianmei, LI Fang, ZHU Yali, CHEN Hong, ZHENG Fei, 2015: A Review of Seasonal Climate Prediction Research in China, ADVANCES IN ATMOSPHERIC SCIENCES, 32, 149-168.  doi: 10.1007/s00376-014-0016-7
    [13] HU Shujuan, CHOU Jifan, 2004: Uncertainty of the Numerical Solution of a Nonlinear System's Long-term Behavior and Global Convergence of the Numerical Pattern, ADVANCES IN ATMOSPHERIC SCIENCES, 21, 767-774.  doi: 10.1007/BF02916373
    [14] Deliang CHEN, Christine ACHBERGER, Jouni R¨AIS¨ANEN, Cecilia HELLSTR¨OM, 2006: Using Statistical Downscaling to Quantify the GCM-Related Uncertainty in Regional Climate Change Scenarios: A Case Study of Swedish Precipitation, ADVANCES IN ATMOSPHERIC SCIENCES, 23, 54-60.  doi: 10.1007/s00376-006-0006-5
    [15] Chengjun XIE, Tongwen WU, Jie ZHANG, Kalli FURTADO, Yumeng ZHOU, Yanwu ZHANG, Fanghua WU, Weihua JIE, He ZHAO, Mengzhe ZHENG, 2023: Spatial Inhomogeneity of Atmospheric CO2 Concentration and Its Uncertainty in CMIP6 Earth System Models, ADVANCES IN ATMOSPHERIC SCIENCES, 40, 2108-2126.  doi: 10.1007/s00376-023-2294-4
    [16] Zhenhua HUO, Wansuo DUAN, Feifan ZHOU, 2019: Ensemble Forecasts of Tropical Cyclone Track with Orthogonal Conditional Nonlinear Optimal Perturbations, ADVANCES IN ATMOSPHERIC SCIENCES, 36, 231-247.  doi: 10.1007/s00376-018-8001-1
    [17] Saleh AMINYAVARI, Bahram SAGHAFIAN, Majid DELAVAR, 2018: Evaluation of TIGGE Ensemble Forecasts of Precipitation in Distinct Climate Regions in Iran, ADVANCES IN ATMOSPHERIC SCIENCES, 35, 457-468.  doi: 10.1007/s00376-017-7082-6
    [18] Jihang LI, Zhiyan ZHANG, Lu LIU, Xubin ZHANG, Jingxuan QU, Qilin WAN, 2021: The Simulation of Five Tropical Cyclones by Sample Optimization of Ensemble Forecasting Based on the Observed Track and Intensity, ADVANCES IN ATMOSPHERIC SCIENCES, 38, 1763-1777.  doi: 10.1007/s00376-021-0353-2
    [19] XU Hui, DUAN Wansuo, 2008: What Kind of Initial Errors Cause the Severest Prediction Uncertainty of El Nino in Zebiak-Cane Model, ADVANCES IN ATMOSPHERIC SCIENCES, 25, 577-584.  doi: 10.1007/s00376-008-0577-4
    [20] Yunyun LIU, Zeng-Zhen HU, Renguang WU, Xing YUAN, 2022: Causes and Predictability of the 2021 Spring Southwestern China Severe Drought, ADVANCES IN ATMOSPHERIC SCIENCES, 39, 1766-1776.  doi: 10.1007/s00376-022-1428-4
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    • Manuscript History

    Manuscript received: 10 November 2005
    Manuscript revised: 10 November 2005
    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

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    Ensemble Forecast: A New Approach to Uncertainty and Predictability

    • 1. Environmental Modeling Center, NCEP/NWS/NOAA, Washington DC, USA
    • Manuscript received: 2005-11-10
    • Manuscript revised: 2005-11-10

    Abstract: Ensemble techniques have been used to generate daily numerical weather forecasts since the 1990s in numerical centers around the world due to the increase in computation ability. One of the main purposes of numerical ensemble forecasts is to try to assimilate the initial uncertainty (initial error) and the forecast uncertainty (forecast error) by applying either the initial perturbation method or the multi-model/multiphysics method. In fact, the mean of an ensemble forecast offers a better forecast than a deterministic (or control) forecast after a short lead time (3 5 days) for global modelling applications. There is about a 1-2-day improvement in the forecast skill when using an ensemble mean instead of a single forecast for longer lead-time. The skillful forecast (65% and above of an anomaly correlation) could be extended to 8 days (or longer) by present-day ensemble forecast systems. Furthermore, ensemble forecasts can deliver a probabilistic forecast to the users, which is based on the probability density function (PDF)instead of a single-value forecast from a traditional deterministic system. It has long been recognized that the ensemble forecast not only improves our weather forecast predictability but also offers a remarkable forecast for the future uncertainty, such as the relative measure of predictability (RMOP) and probabilistic quantitative precipitation forecast (PQPF). Not surprisingly, the success of the ensemble forecast and its wide application greatly increase the confidence of model developers and research communities.

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