Identifiability of soil and ecosystem states and parameters of integrated subsurface-land surface-atmosphere models by multivariate data assimilation

我们将实施一种同化地表温度的策略，通过在一天内同时考虑多个观测数据来更新敏感的地表参数和状态。我们将开发一个运算器来同化叶面积指数和更新动态植被状态和敏感生态系统参数。综合试验将这些数据与观测到的土壤湿度结合起来进行估算。特别是，我们将检测在包括多个不确定性来源和几个未知参数的现实场景下，我们是否可以改进参数估计，以及某些数据类型的同化在多大程度上改善了其他隔间的状态。首先使用P5、P6、P7联合测试用例对TerrSysMP-PDAF （CLM-ParFlow-PDAF）的陆地分量进行了综合测试。我们将为C1协调的完整TerrSysMP-PDAF所做的实验做出贡献。最后，将对鲁尔集水区进行测试。

We will implement a strategy to assimilate land surface temperature by simultaneously considering multiple observations during a day to update sensitive land surface parameters and states. We will develop an operator to assimilate leaf area index and update the dynamical vegetation states and sensitive ecosystem parameters. Synthetic experiments estimate the value of these data together with observed soil moisture. In particular, we will detect under realistic scenarios including multiple sources of uncertainty and several unknown parameters, whether we can improve parameter estimation, and to what extent the assimilation of certain data types improves the state of other compartments. Synthetic tests are carried out first for the land component of TerrSysMP-PDAF (CLM-ParFlow-PDAF) using a joint test case with P5, P6 and P7. We will contribute to the experiments made with the complete TerrSysMP-PDAF coordinated by C1. Finally, tests will be made for the Rur catchment.