CAJ | 학술논문

提基于 TIGGE 资料中 CMA、ECMWF、JMA 和 NCEP 四中心2010、2011和2012年3年的资料，采用集合平均（EMN）、加权集合平均（WEMN）、消除偏差集合平均（BREM）和加权消除偏差集合平均（即超级集合，SUP）四种方法，对西太平洋地区热带气旋路径与中心气压进行时效为24、48、72、96和120 h 的多模式集成预报，评估了不同单中心预报结果，并分析了不同多模式集成预报方法的特点，对比了不同多模式集成方法的效果。结果表明，对于热带气旋路径和中心气压预报，各中心预报技巧不同，其中3年的 CMA 预报效果均较差，2010、2011年的 ECMWF 预报和2012年的 NCEP 预报效果最优；总体上几种多模式集成方法在120 h 预报时效内均优于单模式预报，其中 EMN 作为一种最简单的集成预报方法，具有一定的局限性，而 WEMN 由于给不同单模式预报赋予了权重，因此相对于 EMN 能够得到更好的多模式集成预报结果；BREM 方案由于消除了模式预报中的系统性偏差，因此集成预报效果也优于 EMN；由于去除了模式预报偏差，同时给不同模式赋予了权重，SUP 方案得到的集成预报效果最优。
제기우 TIGGE 자료중 CMA、ECMWF、JMA 화 NCEP 사중심2010、2011화2012년3년적자료，채용집합평균（EMN）、가권집합평균（WEMN）、소제편차집합평균（BREM）화가권소제편차집합평균（즉초급집합，SUP）사충방법，대서태평양지구열대기선로경여중심기압진행시효위24、48、72、96화120 h 적다모식집성예보，평고료불동단중심예보결과，병분석료불동다모식집성예보방법적특점，대비료불동다모식집성방법적효과。결과표명，대우열대기선로경화중심기압예보，각중심예보기교불동，기중3년적 CMA 예보효과균교차，2010、2011년적 ECMWF 예보화2012년적 NCEP 예보효과최우；총체상궤충다모식집성방법재120 h 예보시효내균우우단모식예보，기중 EMN 작위일충최간단적집성예보방법，구유일정적국한성，이 WEMN 유우급불동단모식예보부여료권중，인차상대우 EMN 능구득도경호적다모식집성예보결과；BREM 방안유우소제료모식예보중적계통성편차，인차집성예보효과야우우 EMN；유우거제료모식예보편차，동시급불동모식부여료권중，SUP 방안득도적집성예보효과최우。
In this paper,four multimodel ensemble methods,namely ensemble mean (EMN),weighted ensemble mean (WEMN),bias-removed ensemble mean (BREM)and weighted bias-removed ensemble mean (superensemble,SUP)have been constructed based on TIGGE datasets,from which four single model forecasts of recent three years from CMA,ECMWF,JMA and NCEP are selected to conduct multi-model ensemble combination and comparison.Single and multimodel ensemble forecasts on the tracks and central pressure of tropical cyclone over Western Pacific with forecast lead time 24 h,48 h,72 h,96 h and 120 h are evaluated and compared.The results show that different single models have different forecast skills.The CMA forecast has the worst performance during the three years,and the best single model forecast in 2010 and 2011 is ECMWF while NCEP is the best one in 2012.Generally,multimodels have significant superiority over the single models within 120 h forecast lead times.As the simplest method within all the four multimodels,EMN shows limited superiority.WEMN has better skill than EMN be-cause it can give different weights to the member models,while the BREM exhibits superiority for the sys-tematic bias is removed.Because of considering both the weights of member models and bias elimination, the SUP has a better skill in general.