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Abstract
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.
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Citation
ZHI Xiefei, QI Haixia, BAI Yongqing, LIN Chunze. 2012: A Comparison of Three Kinds of Multimodel Ensemble Forecast Techniques Based on the TIGGE Data. Journal of Meteorological Research, 26(1): 41-51. DOI: 10.1007/s13351-012-0104-5
ZHI Xiefei, QI Haixia, BAI Yongqing, LIN Chunze. 2012: A Comparison of Three Kinds of Multimodel Ensemble Forecast Techniques Based on the TIGGE Data. Journal of Meteorological Research, 26(1): 41-51. DOI: 10.1007/s13351-012-0104-5
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ZHI Xiefei, QI Haixia, BAI Yongqing, LIN Chunze. 2012: A Comparison of Three Kinds of Multimodel Ensemble Forecast Techniques Based on the TIGGE Data. Journal of Meteorological Research, 26(1): 41-51. DOI: 10.1007/s13351-012-0104-5
ZHI Xiefei, QI Haixia, BAI Yongqing, LIN Chunze. 2012: A Comparison of Three Kinds of Multimodel Ensemble Forecast Techniques Based on the TIGGE Data. Journal of Meteorological Research, 26(1): 41-51. DOI: 10.1007/s13351-012-0104-5
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