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

提出了区域高分辨率天气预报模式对短期定量降水预报的改进。我们将把改进的区域集合建模与根据最新的遥感信息选择最佳成员相结合，并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报整合。前一步将引入蒙特卡罗技术，以一种新的方式在云和雨同化中反演一个高度非线性和严重不连续的微物理问题。将使用物理初始化技术、轻推技术和变分方法，利用来自遥感的最及时的可用信息，包括雷达反射率、云参数和水汽含量。通过这种叠加过程，我们预计将大大减少背景场中的相位误差的影响，这些误差目前阻碍了区域数值天气预报模式对短期降水预报观测的成功同化。该项目将在德国研究界建立先进的集合预报能力。验证工作将探索和量化预报中的不确定性来源，特别是在对流条件下。首先将通过研究地形陡峭程度不同的环境中的对流风暴的一组案例研究，探讨在不同气象条件下对预报系统的不同要求。一些案例研究将围绕现场试验的可能地点进行，以帮助规划行动并为实时预报做准备。

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.