Scale-Problems in Assimilating of Passive Microwave Observation into Coupled Models

这个项目探索了被动L波段卫星观测在基于集合的数据同化中的使用和价值，以及用于中尺度流域的全耦合陆地系统模式。这种模型的陆地部分的分辨率通常为100米，大气部分的分辨率为1公里，这比卫星观测的分辨率小得多，通常为数十公里。基于集合的数据同化需要通过观测算子从陆地系统模型生成合成观测，并将其与生成分析集合的观测进行比较。由于模型状态不一定包括观测操作员所需的所有信息，观测操作员在很大程度上是一个辐射传输模型，因此必须从外部数据推断缺失的信息。该项目的主要目标是开发一个合适的观测操作员，它能够以尽可能好的方式模拟L波段的卫星观测，并将其用于与陆地系统建模平台(TerrSysMP，Shrestha等人)在数据同化背景下利用这种观测。2014)与并行数据同化框架(PDAF、Nerger等人)相耦合。2013)。第一阶段主要侧重于编制灵活的观测操作员及其与实际观测的比较，而第二阶段将进一步改进观测操作员对植被的表示及其可操作性，但主要侧重于将其用于数据同化。这包括：(A)量化数据同化模型产生的观测值与来自虚拟和真实现实的观测值之间的偏差；(B)数据同化实验，根据其他观测值(如降雨量)量化这类观测值；(C)为更好地利用大规模观测值的信息含量而采取的预处理和过滤方法。

This project explores the use and value of passive L-band satellite observations for ensemble-based data assimilation with fully-coupled terrestrial system models for mesoscale catchments. Model resolutions are typically of the order of 100 m for land components and 1 km for the atmospheric component of such models, which is much smaller than for the satellite observations with typically tens of kilometers. Ensemble-based data assimilation requires the generation of synthetic observations from the terrestrial system model via an observation operator, which are compared with observations for the generation of the analysis ensemble. Since the model state does not necessarily include all information required for the observation operator, which is in large parts a radiative transfer model, missing information must be inferred from external data.The main objectives of the project are the development of a suitable observation operator, which is able to mimic L-Band satellite observations in the best possible way, and its use for the exploitation of such observations in the data assimilation context with the Terrestrial Systems Modeling Platform (TerrSysMP, Shrestha et al. 2014) coupled to the Parallelized Data Assimilation Framework (PDAF, Nerger et al. 2013). While Phase I focused mainly on the compilation of a flexible observation operator and its comparison with real observations, Phase II will pursue further improvements concerning the representation of vegetation by the observation operator and its operationalization, but mainly concentrate on its use for data assimilation. This includes (a) the quantification of biases between observations generated from the data assimilation model and observations both from the virtual and true reality, (b) data assimilation experiments which quantify the value of such observations given other observations (e.g. precipitation), and (c) pre-processing and filtering methods to better exploit the information content of the large-scale observations.