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黄海海雾短临近预报中云水路径的EnKF同化研究
高小雨, 高山红
中国海洋大学海洋与大气学院
摘要:
在海雾的短临近预报中,初始场的水汽凝结状态扮演着重要角色。为了改进初始场的云水含量,本文提出直接同化雾体云水信息的思路。针对2011年5月一次大范围的黄海海雾,借助EnKF (Ensemble Kalman Filter) 方法,尝试进行了极轨卫星反演云水路径数据的同化试验。结果表明:1)通过在EnKF中将云水混合比增加到背景场和分析场的控制变量中,构建云水观测数据与背景场之间的关系,实现云水路径数据的直接同化是可行的;2)EnKF同化云水路径,可显著改善海面气温与湿度状态,大幅提高海雾预报效果;3)EnKF中基于集合体动态统计流依赖背景误差协方差,是取得良好同化效果的主要原因。值得指出的是,受集合样本误差的影响,需要特别关注云水含量与风之间的相关关系。
关键词:  黄海海雾  短临近预报  EnKF同化  云水路径  海雾雾区
DOI:10.11693/hyhz20181000243
分类号:
基金项目:国家重点研发计划项目,2017YFC1404200号与2017YFC1404100号;国家自然科学基金项目,41276009号;广州市产学研协同创新重大专项课题,201704020169号
EnKF Assimilation of Cloud Water Path in Nowcasting Sea Fog over the Yellow Sea
Gao Xiaoyu, Gao Shanhong
College of Oceanic and Atmospheric Sciences, Ocean University of China
Abstract:
The condensation state of water vapor in the initial condition plays an important role in sea fog nowcasting. In order to improve the cloud water structure in the initial condition, this paper proposes an idea to directly assimilate the cloud water information within fog body. Based on the EnKF (Ensemble Kalman Filter) method, focusing on a sea fog event that occurred widely over the Yellow Sea during May 2011, assimilation experiments on the retrieved cloud water path (Cw) from polar satellites are conducted. The conclusions can be outlined as: 1) it is a possible approach to assimilate Cw in the EnKF by adding mixing ratio of cloud water into the control variables in both background and analysis fields, and constructing the correlations between observations and background field; 2) assimilation of Cw can significantly make the status of temperature and moisture better, improving sea fog forecasting greatly; 3) the positive assimilation effect is contributed from the flow-dependent background error covariance calculated dynamically in the EnKF. Additionally, it is worth mentioning that the correlation between cloud water content and wind should be paid special attention.
Key words:  the Yellow Sea fog  nowcasting forecast  EnKF assimilation  cloud water path  sea fog area
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