Towards consistent predictions of water and energy cycles in intermediate scale catchments

拟议的项目继续了项目S和项目C的工作，这些项目是1598年CAOS副总干事研究小组第一阶段的工作。我们的目标是通过研究模型复杂性和简约性之间的最佳平衡以及不同观测对模型识别和操作的价值，来改进中等尺度(几到几百千米)流域的水、能量和质量循环的分布式建模。特别是，我们将进一步开发第一阶段开始的CAOS模型的功能核心，研究动态和分层分组模型元素以避免重复计算的新概念的潜力，并进一步开发和应用基于信息的多参数、多尺度验证概念。在低中尺度上的分布式预报需要分布式输入；因此，我们将进一步开发基于多传感器观测(极化雷达、散射计和垂直雷达)的定量降水估计技术，以及基于WRF高分辨率天气模拟和数据同化的新方法。我们将使用CAOS第一阶段产生的大量观测数据来建立、运行和比较最先进的中尺度水文模式(CAOS、ROGER、CATFLOW、NOAH-MP)的代表性样本，以评估它们的相对优势，开发一个结构化模式相互比较的框架，并评估不同观测数据的信息价值。

The proposed project continues the work of projects S and C within the first phase of the DFG Research Group FOR 1598 CAOS. Our goal is to improve distributed modeling of water-, energy- and mass cycles in intermediate scale catchments (few to few hundred km^) by investigating the optimum balance between model complexity and parsimony and the value of different observations for model identification and operation. In particular, we will further develop the functional core of the CAOS model as started in phase 1, investigate the potential of a novel concept for dynamical and hierarchical grouping of model elements to avoid redundant computations, and further develop and apply an information-based multi-parameter, multi-scale verification concept. Distributed predictions on the lower mesoscale require distributed input; we will therefore further develop quantitative precipitation estimation techniques based on multi-sensor observations (Polarimetrie radar, distrometers, and vertical radar) and a novel approach based on high-resolution weather modeling with WRF in combination with data assimilation. We will use the large set of observations generated in CAOS phase 1 to set up, run and compare a representative sample of state-of-the-art mesoscale hydrological models (CAOS, ROGeR, CATFLOW, NOAH-MP) to assess their relative strengths, develop a framework for structured model intercomparison and to assess the informative value of different observables.