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

开发集成卡尔曼滤波器校准和数据同化 (EnC/DA) 方法，将大地测量和遥感数据集成到全球水文模型中

• 批准号: 397590167
• 负责人: Professor Dr.-Ing. Jürgen Kusche
• 金额: --
• 依托单位: Lehrstuhl für Astronomische, Physikalische und Mathematische Geodäsie
• 依托单位国家: 德国
• 项目类别: Research Units
• 财政年份: 2018
• 资助国家: 德国
• 起止时间: 2017-12-31 至 2021-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/397590167?language=en
• 关键词: Developing; Ensemble; Kalman; Filter; calibration

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.

项目P1的主要目标是开发和实施一种有效的环境卡尔曼滤波器，用于将数据同化和参数校准与水文建模相结合。第一阶段的重点是同时整合多个数据集并创建观测算子;以及开发和研究基于集合的模型参数校准。在建立工作环境后，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，开发一个全球同化系统，包括参数校准，并创建新的全球尺度水通量和储存的定量描述，包括不确定性的表征。