Developing an Ensemble Kalman Filter calibration and data assimilation (EnC/DA) approach for integrating geodetic and remote sensing data into a global hydrological model

项目P1的主要目标是开发和实施有效的集合卡尔曼滤波器，以便将数据同化和参数校准与水文模拟结合起来。第一阶段的重点是同时整合多个数据集和创建观测算子；开发和研究基于集合的模型参数校准。在建立了一个工作环境后，第二阶段的工作将转向改进我们的方法，比较整个RU的方法，开发一个包括参数校准的全球同化系统，并创建新的全球尺度水通量和储水量的定量描述，包括对不确定性的表征。

Main objective of project P1 is the development and implementation of an effective Ensemble Kalman Filter for the integration of data assimilation and parameter calibration with hydrological modelling. Foci within the first phase were simultaneously integrating multiple data sets and creating observation operators; and developing and investigating the ensemble-based model parameter calibration. After setting up a working environment, work in the 2nd phase will turn towards refining our method, comparing approaches across the RU, developing a global assimila-tion system including parameter calibration, and creating new quantitative descriptions of global-scale water fluxes and storages, including a characterization of the uncertainty.