Infrastructure project: Multi-Sensor Compositing for Hydrometeor Classification, High-Impact Weather, Nowcasting and Data Assimilation

RealPEP遵循这样的假设，即通过利用这个中央服务项目将提供的全部降水产生大气的观测状态演变信息，可以显著改进数值天气预报(NWP)的定量降水估计(QPE)、定量降水预报(QPN)和定量降水预报(QPF)。C1将建立和维护一个数据收集和利用平台，该平台将所有与云和降水有关的观测结合在一个3D合成中，水平分辨率至少为1公里x 1公里，垂直分辨率为500米，时间分辨率为5分钟，覆盖德国。这种基于网格观测的状态演变构成了项目P1(QPE)、P2(QPN)和P3(QPF)的主要数据库，这些项目反过来又以已观测、现预报和预报的地表降雨强度和类型的形式为P4(洪水预报)提供中央输入。除综合资料外，C1将在其固有坐标系中提供经处理的单传感器数据，用于在P1中发展最高质量的基于衰减的降水反演，并用于在P3中进行集合卡尔曼滤波方法的数据同化试验。此外，DWD网络的偏振雷达观测和数值预报分析和预报C1将整合基于卫星的近红外和热红外观测、全球导航卫星系统得出的总柱水汽场和闪电数据。与P2合作，水汽总场的平流和辐合将丰富基于观测的短时预报，提供对流启动和加强降水的迹象。数据收集和开发平台将使用DWD C处理平台Polara，该平台已经包括几个标准的雷达处理和分析工具，适用于创建P1至P4的初始数据，并将在RealPEP过程中扩展。C1将根据RealPEP和其他来源的发展情况启动和指导Polara的更新，并将其整合到从QPE到洪水预测的RealPEP过程链中。

RealPEP follows the hypothesis, that Quantitative Precipitation Estimation (QPE), Quantitative Precipitation Nowcasting (QPN) and Quantitative Precipitation Forecasting (QPF) by Numerical Weather Prediction (NWP) can be significantly improved by exploiting the full observational state evolution information of the precipitation-generating atmosphere, which will be provided by this central service project. C1 will build and maintain a data collection and exploitation platform, which combines all cloud and precipitation-related observations in a 3D composite with at least 1 km x 1 km horizontal, 500 m vertical, and 5 minute temporal resolution covering Germany. This gridded observation-based state evolution constitutes the prime data base for projects P1 (QPE), P2 (QPN), and P3 (QPF), which in turn all provide the central input to P4 (Flood Prediction) in the form of observed, nowcasted, and predicted surface rainfall intensity and type. In addition to the composite, C1 will provide processed single-sensor data in their innate coordinate systems for the development of highest-quality attenuation-based precipitation retrievals in P1 and for data assimilation experiments in the Ensemble Kalman Filter approach in P3.Besides polarimetric radar observations of the DWD network and NWP analyses and predictions C1 will integrate satellite-based near- and thermal infrared observations, GNSS-derived total column water vapor fields and lightning data. In cooperation with P2, advection and convergence of total water vapor fields will enrich observation-based nowcasting with indications of convection initiation and intensifying precipitation.The data collection and exploitation platform will employ the DWD C++ processing platform POLARA, which includes already several standard radar processing and analysis tools suitable for creating initial data for P1 to P4, and which will be expanded during the course of RealPEP. C1 will initiate and guide the updating of POLARA with developments by RealPEP and from other sources, and its integration into the RealPEP process chain from QPE to flood prediction.