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A Comparison of Three Kinds of Multimodel Ensemble Forecast Techniques Based on the TIGGE Data
Authors:Xiefei Zhi  Haixia Qi  Yongqing Bai  Chunze Lin
Institution:1. Key Laboratory of Meteorological Disaster of Ministry of Education,Nanjing University of Information Science & Technology, Nanjing 210044
2. Wuhan Institute of Heavy Rain, China Meteorological Administration, Wuhan 430074
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.
Keywords:multimodel superensemble  bias-removed ensemble mean  multiple linear regression  neural network  running training period  TIGGE
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