Integral Radar Volume Descriptors for Quantitative Areal Precipitation

该建议的主要目标是说明综合雷达体积描述符(IRVD)及其在定量面雨量估计中的应用。我们假设雷达反射率的时空结构包含了足够的信息，以便与目前使用的局部Z-R关系相比，显著改进对地面降水量的估计。这一假设的正确性将通过对现有雷达体积数据的客观尺度分析以及对降雨强度的同步地面观测来证明。次要目标是基于客观结构的杂波消除、有效的雷达体积存储和可视化。雷达数据中降水过程指标的识别将通过对4维雷达测量的时空结构进行基于小波的分析来实现。它们与地表降水在不同时间和空间尺度上的关系形成了多维、多标量的传递函数。在算法设计和开发方面，我们将使用卡尔斯鲁厄(AQUARADAR-C)存档的C波段雷达数据。与SPP1167/QUEST和AQUARADAR-E合作，天气预报模型的雷达模拟工具将扩展到包括极化。该工具将应用于中尺度天气预报模式(DWD的Lokal-modell)的输出，该模式配有新的云模块(由AQUARadar-C提供)，用于在特别观测期(SOP)期间进行个案研究，以生成伪雷达数据。对伪雷达资料进行IRVD-性能分析，将有助于更好地理解所提算法的性能，也有助于揭示中-y尺度模式在模拟降水过程方面的不足。最后，我们将参加SOP，通过提供2个MRR来确定雷达体积内雷达反射率的分尺度变异性，包括雨量计、微波剖面仪、碟度计和Ceileter。除了从4D雷达数据到地表降雨的映射功能外，在该项目中将研究的具体新目标是基于模式的杂波滤波、相关结构的识别以及使用保结构平滑算子、小波和正则化网络进行它们的检测。为了高效地存储和处理海量数据，将使用基于分层基础的压缩方法。

The primary goal of the proposal is the specification of Integral Radar Volume Descriptors (IRVD) and their application to quantitative areal precipitation estimates. We hypothesize that the spatio-temporal structure of radar reflectivity contains sufficient information in order to significantly improve the estimation of precipitation at the ground in comparison to currently used local Z-R-relations. The validity of this hypothesis will be proven by an objective scale-dependent analysis of existing radar volume data together with simultaneous surface observations of rain intensity. Secondary goals are objective structure-based clutter elimination, efficient radar volume storage and visualisation. The identification of precipitation process indicators in radar data will be achieved by a wavelet-based analysis of spatial and temporal structures in 4-dimensional radar measurements. Their relation to surface area rainfall on different scales in space and time results in a multi-dimensional, multi-scalar transfer function. For algorithm design and development, we will use the archived C-Band radar data from Karlsruhe (AQUARadar-C). In cooperation with SPP1167/QUEST and AQUARadar-E, a radar simulation tool for weather forecast models will be extended to include polarization. The tool will be applied to the output of a meso-scale weather prediction model (Lokal-Modell of DWD) supplied with new cloud modules (provided by AQUARadar-C) for case studies during the Special Observation Pediod (SOP) to generate pseudo-radar data. The analysis of IRVD-performance on the pseudo-radar data will both help to better understand the performance of the derived algorithms and shed light to deficiencies of the meso-y-scale model in simulating the precipitation process. Finally we will participate in the SOP in order to determine sub-scale variability of radar reflectivity within a radar volume by providing 2 MRR including rain gauges, microwave profiler, disdrometer and ceilometer. Besides the mapping function from 4D radar data to surface rainfall, specific novel objectives, which will be investigated during the project, are pattern based clutter filtering, identification of relevant structures and their detection using structure-conserving smoothing operators, wavelets and regularization networks. For the efficient storage and processing of the huge data volumes, compression methods based on hierarchical bases will be used.