Data Assimilation and Ensemble Modelling for the Improvement of Short Range Quantitative Precipitation Forecasts (DAQUA)

用于改进短程定量降水预报 (DAQUA) 的数据同化和集合建模

• 批准号: 5426639
• 负责人: Professor Dr. George Craig
• 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/5426639?language=en
• 关键词: Data; Assimilation; Ensemble; Modelling; Improvement

We propose the improvement of short range quantitative precipitation forecasting by regional high resolution weather forecast models. We will combine improved regional ensemble modelling with best member selection based on the most recent remote sensing information, with further broadening and narrowing of the distribution by a new evolutionary approach, followed by improved data assimilation and further forecast integration. The former step will introduce a Monte Carlo technique to invert a highly nonlinear and critically discontinuous microphysical problem in a novel way in cloud and rain assimilation. Physical initialisation techniques, nudging techniques and variational approaches with the most timely available information from remote sensing including Radar reflectivities, cloud parameters, and water vapour content will be employed. With this stacked procedure we expect to substantially reduce the influence of phase errors in the background field, which currently impede the successful assimilation of observations for short range precipitation forecasting by regional numerical weather forecast models. The project will establish an advanced capability for ensemble forecasting in the German research community. Validation efforts will explore and quantify the sources of uncertainty in forecasts especially under convective conditions. The differing requirements for the forecasting System under different meteorological conditions will be explored in the first instance by examining a set of case studies of convective storms in environments with orography of varying degrees of steepness. Some of the case studies will be orientated around the likely location of the field experiment, to aid in planning of the operations and to prepare for real-time forecasting.

提出了利用区域高分辨率天气预报模式改进短时降水定量预报的方法。我们将改进的区域集合模型与基于最新遥感信息的最佳成员选择相结合，通过一种新的进化方法进一步扩大和缩小分布，然后改进数据同化和进一步的预测集成。前一步将引入蒙特卡罗技术，以一种新颖的方式反演云和雨同化中的高度非线性和临界不连续微物理问题。将采用物理初始化技术、助推技术和从遥感获得的最及时信息的变分方法，包括雷达反射率、云参数和水蒸气含量。利用这种叠加过程，我们期望大大减少背景场相位误差的影响，这一影响目前阻碍了区域数值天气预报模式成功同化观测资料用于短期降水预报。该项目将在德国研究界建立一种综合预报的先进能力。验证工作将探索和量化预报中不确定性的来源，特别是在对流条件下。在不同的气象条件下，对预报系统的不同要求将首先通过一系列在不同陡峭地形环境下的对流风暴案例研究来探讨。一些个案研究将围绕实地试验的可能地点进行，以协助规划行动和为实时预测作准备。