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

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Journal of Meteorological Research : Volume 25

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

Content Provider	Springer Nature Link
Author	Zhi, Xiefei Qi, Haixia Bai, Yongqing Lin, Chunze
Copyright Year	2012
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.
Starting Page	41
Ending Page	51
Page Count	11
File Format	PDF
ISSN	08940525
Journal	Journal of Meteorological Research
Volume Number	26
Issue Number	1
e-ISSN	21914788
Language	English
Publisher	The Chinese Meteorological Society
Publisher Date	2012-03-17
Publisher Place	Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	multimodel superensemble bias-removed ensemble mean multiple linear regression neural network running training period TIGGE Geophysics and Environmental Physics Atmospheric Protection/Air Quality Control/Air Pollution Meteorology/Climatology Climate Change
Content Type	Text
Resource Type	Article
Subject	Ocean Engineering Atmospheric Science

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