摘要
Based on the tropical cyclone data from the Central Meteorological Observatory of China, Japan Meteorological Agency, Joint Typhoon Warning Center and European Centre for Medium-Range Weather Forecasts (ECMWF) during the period of 2004 to 2009, three consensus methods are used in tropical cyclone (TC) track forecasts. Operational consensus results show that the objective forecasts of ECMWF help to improve consensus skill by 2%, 3%-5% and 3%-5%, decrease track bias by 2.5 kin, 6-9 km and 10-12 km for the 24 h, 48 h and 72 h forecasts respectively over the years of 2007 to 2009. Analysis also indicates that consensus forecasts hold positive skills relative to each member. The multivariate regression composite is a method that shows relatively low skill, while the methods of arithmetic averaging and composite (in which the weighting coefficient is the reciprocal square of mean error of members) have almost comparable skills among members. Consensus forecast for a lead time of 96 h has negative skill relative to the ECMWF objective forecast.
Based on the tropical cyclone data from the Central Meteorological Observatory of China,Japan Meteorological Agency,Joint Typhoon Warning Center and European Centre for Medium-Range Weather Forecasts(ECMWF)during the period of 2004 to 2009,three consensus methods are used in tropical cyclone(TC)track forecasts.Operational consensus results show that the objective forecasts of ECMWF help to improve consensus skill by 2%,3%-5%and 3%-5%,decrease track bias by 2.5 km,6-9 km and10-12 km for the 24 h,48 h and 72 h forecasts respectively over the years of 2007 to 2009.Analysis also indicates that consensus forecasts hold positive skills relative to each member.The multivariate regression composite is a method that shows relatively low skill,while the methods of arithmetic averaging and composite(in which the weighting coefficient is the reciprocal square of mean error of members)have almost comparable skills among members.Consensus forecast for a lead time of 96 h has negative skill relative to the ECMWF objective forecast.
基金
National Natural Science Foundation of Ningbo City(2013A610124)
Ningbo Planning Project of Science and Technology(2012C50044)
Nanhai Disaster Mitigation Fund of Hainan Provincial Meteorological Bureau(NH2008ZY02)