Coordination Funds

协调基金

• 批准号: 404446103
• 负责人: Privatdozentin Dr. Silke Trömel, Ph.D.
• 金额: --
• 依托单位: Abteilung Meteorologie
• 依托单位国家: 德国
• 项目类别: Research Units
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/404446103?language=en
• 关键词: Coordination; Funds

High-quality near-real time Quantitative Precipitation Estimation (QPE) and its prediction for the next hours (Quantitative Precipitation Nowcasting, QPN) is of high importance for many applications in meteorology, hydrology, agriculture, construction, water and sewer system management. Especially for the prediction of floods in small to meso-scale catchments and of intense precipitation over cities timely, the value of high-resolution, and high-quality QPE/QPN cannot be overrated. Polarimetric weather radars provide the undisputed core information for QPE/QPN due to their area-covering and high-resolution observations, which allow estimating precipitation intensity, hydrometeor types, and wind. Despite extensive investments in such weather radars, QPE is still based primarily on rain gauge measurements since more than 100 years and no operational flood forecasting system actually dares to employ radar observations for QPE. RealPEP will advance QPE/QPN to a stage, that it verifiably outperforms rain gauge observations when employed for flood predictions in small to medium-sized catchments. To this goal state-of-the¿art radar polarimetry will be sided with attenuation estimates from commercial microwave link networks for QPE improvement, and information on convection initiation and evolution from satellites and lightning counts from surface networks will be exploited to improve QPN. With increasing forecast horizons the predictive power of observation-based nowcasting quickly deteriorates and is outperformed by Numerical Weather Prediction (NWP) based on data assimilation, which fails, however, for the first hours due to the lead time required for model integration and spin-up. Thus, RealPEP will merge observation-based QPN with NWP towards seamless prediction in order to provide optimal forecasts from the time of observation to days ahead. Despite recent advances in simulating surface and sub-surface hydrology with distributed, physicsbased models, hydrologic components for operational flood prediction are still conceptual, need calibration, and are unable to objectively digest observational information on the state of the catchments. RealPEP will prove that in combination with advanced QPE/QPN physics-based hydrological models sided with assimilation of catchment state observations will outperform traditional flood forecasting in small to meso-scale catchments

高质量的近实时定量降水估算 (QPE) 及其未来几个小时的预测（定量降水临近预报，QPN）对于气象、水文、农业、建筑、供水和污水系统管理中的许多应用非常重要。特别是对于中小规模流域洪水和城市强降水的及时预报，高分辨率、高质量的QPE/QPN的价值不可低估。偏振天气雷达由于其区域覆盖和高分辨率观测，为 QPE/QPN 提供了无可争议的核心信息，可以估计降水强度、水凝物类型和风。尽管对此类天气雷达进行了大量投资，但 100 多年来，QPE 仍然主要基于雨量计测量，并且没有任何可运行的洪水预报系统真正敢于将雷达观测用于 QPE。 RealPEP 将把 QPE/QPN 推进到一个阶段，即在用于中小型流域的洪水预测时，它的性能可验证优于雨量计观测。为了实现这一目标，最先进的雷达极化测量将与商用微波链路网络的衰减估计相结合，以改善 QPE，并且将利用来自卫星的对流起始和演变以及来自地面网络的闪电计数的信息来改善 QPN。随着预报范围的增加，基于观测的临近预报的预测能力迅速下降，并且优于基于数据同化的数值天气预报（NWP），但由于模型集成和启动所需的准备时间，数值天气预报在最初的几个小时内失败了。因此，RealPEP 将基于观测的 QPN 与 NWP 相结合，实现无缝预测，以便提供从观测时到未来几天的最佳预测。尽管最近在利用分布式物理模型模拟地表和地下水文方面取得了进展，但用于业务洪水预测的水文成分仍然是概念性的，需要校准，并且无法客观地消化有关流域状况的观测信息。 RealPEP 将证明，与先进的 QPE/QPN 基于物理的水文模型相结合，并同化流域状态观测，在中小型流域中将优于传统的洪水预报