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云海-2掩星探测资料在全球数值天气预报模式中的同化效果评估 被引量:1

Evaluation of Assimilation Effect of Yunhai-2 Occultation Data in Global Numerical Weather Prediction Model
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摘要 本文基于T799L91全球数值天气预报模式及其四维变分同化系统,构建了云海-2掩星探测资料的同化流程,并以2019年7月开展了为期一个月的云海-2掩星探测资料同化预报试验。对东亚区域、北半球和南半球的同化预报结果评估表明:在全球数值天气预报模式中,同化云海-2掩星探测资料能够有效提高预报场的准确性,提高的幅度随着预报时效和预报日数的增加逐渐增大;在更长时效的72 h、120 h、168 h预报中,云海-2掩星探测资料与GPS(Global Positioning System)掩星探测资料的同化效果在中前期相当,在中后期,同化云海-2掩星探测资料逐渐优于GPS掩星探测资料;随着预报日数的增加,联合同化云海-2掩星探测资料和GPS掩星探测资料的优势逐步显现。 Based on the T799L91 global numerical weather forecast model and its four-dimensional variational assimilation system,the assimilation process of Yunhai-2 occultation data was constructed,and a one-month assimilationforecast experiment for Yunhai-2 occultation data was carried out in July 2019.The evaluation of the assimilation forecast results for East Asia,Northern Hemisphere and Southern Hemisphere shows that:In the global numerical weather forecast model,assimilating Yunhai-2 occultation data can effectively improve the accuracy of the forecast field.With the extension of the forecast time and the increase of the forecast days,the improvement range gradually increases.With the longer time efforts of the 72-hour,120-hour and 168-hour forecasting,the assimilation effects of Yunhai-2 occultation data and GPS(Global Positioning System)occultation data are similar in the middle and early days,while the assimilation of Yunhai-2 occultation data is gradually better than GPS occultation data in the middle and later days.With the increase of the forecasting days,the advantage of the joint assimilation of Yunhai-2 occultation data with GPS occultation data gradually appears.
作者 王业桂 张斌 李娟 蔡其发 兰伟仁 郭贤鹏 王广杰 WANG Yegui;ZHANG Bin;LI Juan;CAI Qifa;LAN Weiren;GUO Xianpeng;WANG Guangjie(Unit 61741,PLA,Beijing 100094)
机构地区 解放军
出处 《大气科学》 CSCD 北大核心 2021年第4期901-914,共14页 Chinese Journal of Atmospheric Sciences
基金 国家自然科学基金项目41375105。
关键词 云海-2掩星 GPS(Global Positioning System) 同化 全球数值天气预报模式 均方根误差 相关系数 Yunhai-2 occultation GPS(Global Positioning System) Assimilation Global numerical weather forecast model Root mean square error Correlation coefficient
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