High-resolution Reanalyses and Impact Studies for Improving Process Understanding and Precipitation Forecast Skill based on the COPS Data Set

基于 COPS 数据集的高分辨率重新分析和影响研究，以提高过程理解和降水预报技能

• 批准号: 5426792
• 负责人: Dr. Gerd Gendt
• 金额: --
• 依托单位: Deutscher Wetterdienst (DWD)
• 依托单位国家: 德国
• 项目类别: Priority Programmes
• 财政年份: 2004
• 资助国家: 德国
• 起止时间: 2003-12-31 至 2010-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/5426792?language=en
• 关键词: resolution; Reanalyses; Impact; Studies; Improving

Quantitative Precipitation Forecast (QPF) is one of the major challenges in numerical weather prediction (NWP). This is true for QPF ranging from synoptic-scale to small-scale convection. The main goal of this proposal is to improve the short-range QPF on scales of a few hundred km and a few hours. To achieve a significant improvement of QPF on these scales the atmospheric variables, which are representing the pre-convective conditions, need to be determined. Hence an innovative combination of data assimilation techniques and observations is proposed. Within this project, 4D water vapour, as well as wind and cloud data are considered. The focus is on advanced observing systems with high future potential such as GPS, lidar, passive remote sensing from geostationary satellites as well as novel in-situ sensors. Different state-of-the-art assimilation techniques will be compared to find the optimal approach to improve QPF. After the development of suitable observation operators, the optimal use of advanced remote sensing systems will be investigated using various Observing System Experiments (OSE's) and Observing System Simulation Experiments (OSSE's). To quantify the success, model independent data sets inferred from MSG/radar, GPS, and an innovative sounding system will be used for validation.

定量降水预报是数值天气预报的主要挑战之一。对于从天气尺度到小尺度对流的QPF来说，情况也是如此。这项提议的主要目标是在几百公里和几个小时的尺度上改善短程QPF。为了在这些尺度上实现QPF的显著改善，需要确定代表对流前条件的大气变量。因此，提出了一种数据同化技术和观测的创新组合。在这个项目中，考虑了4D水蒸气以及风和云的数据。重点是具有高未来潜力的先进观测系统，如全球定位系统、激光雷达、来自地球静止卫星的被动遥感以及新型现场传感器。将比较不同的最先进的同化技术，以找到改善QPF的最佳方法。在开发出合适的观测操作员之后，将通过各种观测系统实验(OSE‘s)和观测系统模拟实验(OSSE’s)来研究先进遥感系统的最佳使用。为了量化成功，将使用从MSG/雷达、GPS和创新的探测系统推断的独立数据集建模进行验证。