Abstract In this study, the performances of variational (three-dimensional variational; 3DVAR), ensemble-based (ensemble Kalman filter; EnKF), and hybrid (E3DVAR) data assimilation (DA) methods based on the Advanced Research Weather Research and Forecasting (WRF) model are investigated over East Asia for two one-month period of January and July in 2016. Before a comparison between three methods for two one-month periods, a single observation experiment is conducted to tune and optimize background error covariance depending on each method, so that all methods have similar influence radius. For a comparison between three methods for two one-month period by assimilating conventional observations, the E3DVAR outperforms 3DVAR and EnKF for both two seasons. The 3DVAR outperforms EnKF in January, whereas EnKF outperforms 3DVAR in July. The root mean of difference total energy (RM-DTE) for January increases as a forecast time increases, saturating at the value less than 5 m s−1. On the contrary, RM-DTE in July keeps increasing until 72 h forecast time reaching at the value less than 7 m s−1. Relatively larger moisture error in initial condition for summer season can grow rapidly and change large-scale feature considerably, which can contribute to the continuous growth of RM-DTE in July. Furthermore, rank histogram and spread statistics results confirm that ensemble spreads are represented reasonably for January and July in 2016, although spreads in July are slightly overestimated compared to those in January. In conclusion, the hybrid DA method (E3DVAR) is the most appropriate among three DA methods over East Asia. In addition, for the better performance, it is necessary to tune and optimize the DA system depending on DA method for the given area.

中文翻译：

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东亚两个一个月期间变分、集合和混合数据同化方法的比较

摘要 在本研究中，基于Advanced Research Weather Research and Forecasting (Advanced Research Weather Research and Forecasting) 的变分（三维变分；3DVAR）、基于集合（集合卡尔曼滤波器；EnKF）和混合（E3DVAR）数据同化（DA）方法的性能WRF) 模型在 2016 年 1 月和 7 月的两个一个月期间在东亚进行了调查。 在比较两个一个月期间的三种方法之前，进行了一次观测实验，以根据每种方法调整和优化背景误差协方差方法，使所有方法具有相似的影响半径。为了通过同化常规观测来比较两种为期一个月的三种方法，E3DVAR 在两个季节都优于 3DVAR 和 EnKF。3DVAR 在 1 月份的表现优于 EnKF，而 EnKF 在 7 月份的表现优于 3DVAR。1 月份的差分总能量 (RM-DTE) 的均值随着预测时间的增加而增加，在小于 5 m s−1 的值处饱和。相反，7 月份的 RM-DTE 不断增加，直到 72 h 预测时间达到小于 7 m s−1 的值。夏季初始条件相对较大的水分误差可以快速增长并显着改变大尺度特征，这有助于7月份RM-DTE的持续增长。此外，排名直方图和价差统计结果证实，2016 年 1 月和 7 月的整体价差是合理的，尽管与 1 月相比，7 月的价差略有高估。总之，混合DA方法（E3DVAR）是东亚三种DA方法中最合适的。此外，为了更好的性能，