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

被引：0
|
作者：
Xiefei Zhi
Haixia Qi
Yongqing Bai
Chunze Lin
机构：
[1] Nanjing University of Information Science & Technology,Key Laboratory of Meteorological Disaster of Ministry of Education
[2] China Meteorological Administration,Wuhan Institute of Heavy Rain
来源：
Acta Meteorologica Sinica | 2012年 / 26卷
关键词：
multimodel superensemble; bias-removed ensemble mean; multiple linear regression; neural network; running training period; TIGGE;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
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.
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页码：41 / 51
页数：10
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