The ensemble Kalman filter (EnKF) has been widely used in atmosphere, ocean, and land applications. The observing network has been significantly developed, and thus, observations with highly dense temporal resolutions have become available. To better extract information from dense temporal observations, one straightforward strategy is to increase the assimilation frequency. However, more frequent assimilation may exacerbate the model imbalance and result in degraded forecasts. To combat the imbalance caused by ensemble‐based data assimilation due to sampling error and covariance localization, three‐ and four‐dimensional incremental analysis update (IAU) were proposed, which gradually introduce the analysis increments into model rather than intermittently updating the state. The trade‐off between the assimilation frequency and imbalance is systematically explored here by using an idealized two‐layer model and the NOAA GFS. Results from the idealized two‐layer model show that increasing assimilation frequency can reduce errors for state variables that are not sensitive to imbalances. For state variable that carries the signal of the external gravity mode and is sensitive to imbalances, increasing assimilation frequency without (with) IAU reduces (increases) errors. Without IAU, more frequent updates result in smaller increments and less insertion noise, while the initialization of IAU cannot effectively mitigate the imbalances with increased assimilation frequency. Results with a low‐resolution version of the NOAA GFS demonstrate that increasing assimilation frequency from 6 to 2 h improves the errors and biases of forecasts verified with conventional and radiance observations, although gravity wave noise in the forecast is increased.

中文翻译：

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同化频率对集合卡尔曼滤波数据同化和不平衡的影响

集成卡尔曼滤波器（EnKF）已广泛用于大气，海洋和陆地应用。观测网络已经得到了很大的发展，因此，具有高度密集的时间分辨率的观测已经变得可用。为了更好地从密集的时间观测中提取信息，一种直接的策略是增加同化频率。但是，更频繁的同化可能会加剧模型不平衡并导致预测降低。为了解决由于采样误差和协方差局部化导致的基于集合的数据同化所引起的不平衡，提出了三维增量分析更新（IAU）的三维方法，该方法逐步将分析增量引入模型中，而不是间歇地更新状态。本文通过理想化的两层模型和NOAA GFS系统地研究了同化频率与不平衡之间的权衡。理想化两层模型的结果表明，提高同化频率可以减少对不平衡不敏感的状态变量的误差。对于携带外部重力模式信号并且对不平衡敏感的状态变量，在没有（有）IAU的情况下增加同化频率会减少（增加）误差。如果没有IAU，则更频繁的更新将导致较小的增量和较小的插入噪声，而IAU的初始化无法通过增加同化频率来有效地缓解不平衡。