Acta Meteorologica Sinica

, Volume 26, Issue 1, pp 41–51 | Cite as

A comparison of three kinds of multimodel ensemble forecast techniques based on the TIGGE data

  • Xiefei Zhi (智协飞)Email author
  • Haixia Qi (祁海霞)
  • Yongqing Bai (白永清)
  • Chunze Lin (林春泽)


Based on the ensemble mean outputs of the ensemble forecasts from the ECMWF (European Centre for Medium-Range Weather Forecasts), JMA (Japan Meteorological Agency), NCEP (National Centers for Environmental Prediction), and UKMO (United Kingdom Met Office) in THORPEX (The Observing System Research and Predictability Experiment) Interactive Grand Global Ensemble (TIGGE) datasets, for the Northern Hemisphere (10°–87.5°N, 0°–360°) from 1 June 2007 to 31 August 2007, this study carried out multimodel ensemble forecasts of surface temperature and 500-hPa geopotential height, temperature and winds up to 168 h by using the bias-removed ensemble mean (BREM), the multiple linear regression based superensemble (LRSUP), and the neural network based superensemble (NNSUP) techniques for the forecast period from 8 to 31 August 2007.

The forecast skills are verified by using the root-mean-square errors (RMSEs). Comparative analysis of forecast results by using the BREM, LRSUP, and NNSUP shows that the multimodel ensemble forecasts have higher skills than the best single model for the forecast lead time of 24–168 h. A roughly 16% improvement in RMSE of the 500-hPa geopotential height is possible for the superensemble techniques (LRSUP and NNSUP) over the best single model for the 24–120-h forecasts, while it is only 8% for BREM. The NNSUP is more skillful than the LRSUP and BREM for the 24–120-h forecasts. But for 144–168-h forecasts, BREM, LRSUP, and NNSUP forecast errors are approximately equal. In addition, it appears that the BREM forecasting without the UKMO model is more skillful than that including the UKMO model, while the LRSUP forecasting in both cases performs approximately the same.

A running training period is used for BREM and LRSUP ensemble forecast techniques. It is found that BREM and LRSUP, at each grid point, have different optimal lengths of the training period. In general, the optimal training period for BREM is less than 30 days in most areas, while for LRSUP it is about 45 days.

Key words

multimodel superensemble bias-removed ensemble mean multiple linear regression neural network running training period TIGGE 


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Copyright information

© The Chinese Meteorological Society and Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Xiefei Zhi (智协飞)
    • 1
    Email author
  • Haixia Qi (祁海霞)
    • 1
  • Yongqing Bai (白永清)
    • 1
  • Chunze Lin (林春泽)
    • 2
  1. 1.Key Laboratory of Meteorological Disaster of Ministry of EducationNanjing University of Information Science & TechnologyNanjingChina
  2. 2.Wuhan Institute of Heavy RainChina Meteorological AdministrationWuhanChina

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