Merging precipitation data from rain gauge-, microwave link-, and radar measurements for incorporation into a fully coupled data assimilation framework

项目P3旨在根据稀疏和/或不准确的观测结果改进降水时空分布的表示。我们将进一步开发不同观测类型的合并方法，包括雨量计，商业微波链路和天气雷达，我们将使用我们的合并降水产品作为超级观测数据同化在完全耦合的陆地系统建模平台（TerrSysMP）。合并方法包括一个基于Copula的重建高分辨率降水场从不同的组合观测类型。随后，将实施和评价将合并降水产品纳入数据同化框架的不同方法。方法将包括用耦合模式的陆地部分的降水产品替换模式降水（插入），通过限制同化大气部分的弱耦合数据同化，以及完全耦合的数据同化。将对结果进行评价，以便分析和预测降水量时空分布的模拟（弱耦合和强耦合方法）和预测误差最小化。在这里，也被认为是降水与其他水文气象变量的相互关系，以及计算成本。

Project P3 aims at an improved representation of the spatiotemporal distribution of precipitation based on sparse and/or inaccurate observations. We will further develop merging methods for different observation types including rain gauges, commercial microwave links, and weather radars, and we will use our merged precipitation product as super-observations for data assimilation in the fully coupled terrestrial Systems Modeling Platform (TerrSysMP). The merging methodology encompasses a copula-based reconstruction of high-resolution precipitation fields from different combinations of observation types. Subsequently, different approaches for incorporating the merged precipitation product within the data assimilation framework will be implemented and evaluated. Approaches will range from replacement of the model precipitation by the precipitation product for the land components of the coupled model (insertion), to weakly coupled data assimilation by restricting assimilation to the atmosphere component, and to a fully coupled data assimilation. The results will be evaluated for both the analyses and predictions with respect to the minimization of errors in the simulated (for both the weakly and strongly coupled approach) and predicted spatiotemporal distribution of precipitation. Here, also the mutual relation of precipitation with other hydro-meteorological variables is considered, as well as computational costs.