Han Xujun, Li Xin, Rigon Riccardo, Jin Rui, Endrizzi Stefano
Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou, PR China.
Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou, PR China; CAS Center for Excellence in Tibetan Plateau Earth Sciences, Chinese Academy of Sciences, Beijing, PR China.
PLoS One. 2015 Jan 30;10(1):e0116435. doi: 10.1371/journal.pone.0116435. eCollection 2015.
The observation could be used to reduce the model uncertainties with data assimilation. If the observation cannot cover the whole model area due to spatial availability or instrument ability, how to do data assimilation at locations not covered by observation? Two commonly used strategies were firstly described: One is covariance localization (CL); the other is observation localization (OL). Compared with CL, OL is easy to parallelize and more efficient for large-scale analysis. This paper evaluated OL in soil moisture profile characterizations, in which the geostatistical semivariogram was used to fit the spatial correlated characteristics of synthetic L-Band microwave brightness temperature measurement. The fitted semivariogram model and the local ensemble transform Kalman filter algorithm are combined together to weight and assimilate the observations within a local region surrounding the grid cell of land surface model to be analyzed. Six scenarios were compared: 1_Obs with one nearest observation assimilated, 5_Obs with no more than five nearest local observations assimilated, and 9_Obs with no more than nine nearest local observations assimilated. The scenarios with no more than 16, 25, and 36 local observations were also compared. From the results we can conclude that more local observations involved in assimilation will improve estimations with an upper bound of 9 observations in this case. This study demonstrates the potentials of geostatistical correlation representation in OL to improve data assimilation of catchment scale soil moisture using synthetic L-band microwave brightness temperature, which cannot cover the study area fully in space due to vegetation effects.
该观测结果可用于通过数据同化来减少模型的不确定性。如果由于空间可用性或仪器能力，观测无法覆盖整个模型区域，那么在未被观测覆盖的位置如何进行数据同化呢？首先描述了两种常用策略：一种是协方差本地化（CL）；另一种是观测本地化（OL）。与CL相比，OL易于并行化，并且在大规模分析中效率更高。本文评估了OL在土壤湿度剖面特征描述中的应用，其中利用地统计半方差图来拟合合成L波段微波亮温测量的空间相关特征。将拟合的半方差图模型与局部集合变换卡尔曼滤波算法相结合，对围绕待分析陆面模型网格单元的局部区域内的观测进行加权和同化。比较了六种情景：情景1_Obs同化一个最近的观测值，情景5_Obs同化不超过五个最近的局部观测值，情景9_Obs同化不超过九个最近的局部观测值。还比较了局部观测值不超过16、25和36的情景。从结果可以得出结论，在这种情况下，更多参与同化的局部观测值将改善估计，上限为9个观测值。本研究证明了在OL中利用地统计相关性表示来改进集水区尺度土壤湿度数据同化的潜力，该同化使用合成L波段微波亮温，由于植被效应，其在空间上不能完全覆盖研究区域。
