Integral Radar Volume Descriptors for Quantitative Areal Precipitation

该提案的主要目标是规范积分雷达体积描述符（IRVD）及其在定量面积降水估计中的应用。我们假设雷达反射率的时空结构包含足够的信息，与目前使用的局部z -r关系相比，可以显著改善地面降水的估计。这一假设的有效性将通过对现有雷达体积数据的客观尺度相关分析以及同时进行的地面雨强观测来证明。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基于小波的四维雷达测量空间和时间结构分析来实现。在不同的空间和时间尺度上，它们与地表降水的关系形成了一个多维、多标量的传递函数。对于算法的设计和开发，我们将使用来自卡尔斯鲁厄的存档c波段雷达数据（AQUARadar-C）。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。该工具将应用于一个中尺度天气预报模式（DWD的局部模式）的输出，该模式由新的云模块（由AQUARadar-C提供）提供，用于特殊观测期（SOP）的案例研究，以生成伪雷达数据。对拟雷达数据的irvd性能分析，不仅有助于更好地理解推导算法的性能，也有助于揭示中尺度模式在模拟降水过程中的不足。最后，我们将参与SOP，以确定雷达体积内雷达反射率的亚尺度变异性，提供2个MRR，包括雨量计、微波廓线仪、disdrometer和ceilometer。除了从四维雷达数据到地面降雨的映射功能外，该项目还将研究基于模式的杂波滤波、相关结构的识别以及使用结构保持平滑算子、小波和正则化网络的检测。为了有效地存储和处理海量数据，将采用基于分层基的压缩方法。

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 filering, 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.