CMA-GFS全球预报模式在东亚地区中期预报性能极端下降原因研究

Investigation into the causes for the severe degradation of medium-range prediction capabilities of the CMA Global Forecast System in East Asia

李莉 ¹杨学胜 ²陈起英 ²孙思远 ¹宋晓萌¹

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作者信息

1. 中国气象局地球系统数值预报中心,北京,100081
2. 中国气象局地球系统数值预报中心,北京,100081;中国气象科学研究院灾害天气国家重点实验室,北京,100081
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摘要

针对CMA-GFS模式在东亚地区中期预报性能极端下降事件,选取2021年12月30日12时(世界时)预报个例,利用向后误差回溯法和集合敏感性分析进行误差溯源,并通过松弛试验确认误差源区为格陵兰岛以东地区.模式在该地区第24小时预报偏差及其向东传播是导致模式在东亚地区第6天出现预报性能骤降的主要原因.在此基础上,松弛不同模式预报变量以追踪分析每个预报变量对东亚地区预报误差的贡献,发现东亚地区预报误差主要由误差敏感区的位温(θ)引起,随着模式积分时间延长,经向风(v)也有一定贡献.格陵兰岛以东地区预报偏差的减小可以大幅度提高CMA-GFS模式在东亚和北半球的预报性能.

Abstract

Aiming at the event of medium-range forecast"drop-outs"or forecast busts of CMA-GFS in East Asia and using the forecast case from 12:00 UTC on 30 December 2021,backward error tracking and ensemble sensitivity are used to give a first guess for the source region and relaxation experiments are used to confirm that the region is east of Greenland.The results indicate that the 24th hour forecast bias in the east of Greenland and downstream propagation finally lead to the forecast drop-outs in East Asia.On this basis,by relaxing different model prognostic variables we can track and analyze the contribution of each variable to the total forecast errors and its downstream impacts.It is found that the forecast bias are caused by the prognostic variable θ in the key error region,and v component also contribute lesser with the time integration.The reduction of the forecast error in the east of Greenland region can improve the forecast performance of the CMA-GFS greatly in East Asia and Northern Hemisphere.

关键词

预报性能极端下降/误差敏感区/松弛/向后误差回溯法/集合敏感性分析

Key words

Forecast drop-out or forecast busts/Key error origin/Relaxation/Error tracking/Ensemble sensitivity

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出版年

2024

气象学报

中国气象学会

气象学报

CSTPCDCSCD北大核心

影响因子：1.565

ISSN：0577-6619

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