Abstract The optimal observation placement in weather forecast and research (WRF) data assimilation is investigated using a sensitivity analysis method. The method quantifies the sensitivity of observation location to assimilated results as an unobservability index. The empirical observability Gramian matrix composed from a time series of WRF model outputs is used to obtain the unobservability index in the WRF domain. A three-dimensional variational data assimilation (3 D-VAR) method is employed in the WRF model to assimilate the observations of horizontal winds, whose locations are selected based on the unobservability index. The results from the identical-twin experiments show a correlation between improvement in the assimilated wind field and the magnitude of unobservability index. The temporal variation of the vertical component of vorticity is strongly related to the unobservability index, which confirms that an observation location exhibiting a high unobservability index contributes to error reduction in the data assimilation owing to the reduction in the uncertainty caused by the strong vorticity changes.

中文翻译：

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可观测性 Gramian 在 WRF 资料同化中定向观测中的应用

摘要 使用敏感性分析方法研究了天气预报和研究（WRF）数据同化中的最佳观测位置。该方法将观测位置对同化结果的敏感性量化为不可观测性指标。由 WRF 模型输出的时间序列组成的经验可观测性 Gramian 矩阵用于获得 WRF 域中的不可观测性指数。WRF模型采用三维变分数据同化（3D-VAR）方法对水平风的观测进行同化，其位置是根据不可观测性指数选择的。同卵双胞胎实验的结果表明同化风场的改善与不可观测性指数的大小之间存在相关性。