Numerical modeling of sea fog is highly sensitive to initial conditions, especially to moisture in the marine atmospheric boundary layer (MABL). Data assimilation plays a vital role in the improvement of initial MABL moisture for sea fog modeling over the Yellow Sea. In this study, the weather research and forecasting (WRF) model and its three-dimensional variational (3DVAR) data assimilation module are employed for sea fog simulations. Two kinds of background error (BE) covariances with different control variables (CV) used in WRF-3DVAR, that is, CV5 and multivariate BE (CV6), are compared in detail by explorative case studies and a series of application experiments. Statistical verification metrics including probability of detection (POD) and equitable threat scores (ETS) of forecasted sea fog area are computed and compared for simulations with the implementations of CV5 and CV6 in the WRF-3DVAR system. The following is found: (1) there exists a dominant negative correlation between temperature and moisture in CV6 near the sea surface, which makes it possible to improve the initial moisture condition in the MABL by assimilation of observed temperature; (2) in general, the performance of the WRF-3DVAR assimilation with CV6 is distinctly better, and the results of 10 additional sea fog cases clearly suggest that CV6 is more suitable than CV5 for sea fog modeling. Compared to those with CV5, the average POD and ETS of forecasted sea fog area using 3DVAR with CV6 can be improved by 27.6% and 21.0%, respectively.

中文翻译：

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多变量背景误差协方差对黄海雾建模的WRF-3DVAR同化的影响

海雾的数值模拟对初始条件高度敏感，特别是对海洋大气边界层（MABL）中的水分非常敏感。数据同化对于改善黄海中海雾建模的初始MABL湿度起着至关重要的作用。在这项研究中，将天气研究和预报（WRF）模型及其三维变分（3DVAR）数据同化模块用于海雾模拟。通过探索性案例研究和一系列应用实验，详细比较了WRF-3DVAR中使用的具有不同控制变量（CV）的两种背景误差（BE）协方差，即CV5和多元BE（CV6）。计算统计验证指标，包括预测海雾区域的检测概率（POD）和公平威胁评分（ETS），并与WRF-3DVAR系统中CV5和CV6的实现进行仿真比较。发现：（1）海面附近CV6中的温度与湿度之间存在显着的负相关性，这可以通过吸收观测温度来改善MABL中的初始湿度条件。（2）通常，WRF-3DVAR与CV6的同化性能明显更好，另外10个海雾案例的结果清楚地表明，CV6比CV5更适合于海雾建模。与CV5相比，使用3DVAR和CV6预测的海雾面积的平均POD和ETS可以分别提高27.6％和21.0％。