Storm-Scale Quantitative Precipitation Forecasting Using Advanced Data Assimilation Techniques: Methods, Impacts and Sensitivities

对流风暴及其伴随的强风和强降水每年造成数十亿美元的损失和无数人死亡；同时，对恶劣天气和降水量的准确预测是气象学中最具挑战性的任务之一。首席调查员将开发和应用预报局地降水的先进技术和工具，并将研究同化数据的影响以及与初始和边界条件以及模式物理有关的敏感性。该项目将包括对高分辨率数值模拟的详细分析，以便了解决定对流风暴如何、何时和在哪里启动的基本物理过程。从过程和敏感性研究中获得的知识将用于设计和改进数据同化系统。这项研究将利用在以前的实地活动中收集的观察结果。智力优势：根本性的进步将包括以最佳方式利用来自国家多普勒雷达(WSR-88D)网络的海量实时数据的能力显著提高，用于高分辨率数值预报模式的初始化和对严重、高影响天气的准确短期预报。将改进和测试具有成本效益的3D变分分析系统，并结合复杂的云分析程序。该项目预计将首次利用组合卡尔曼滤波法从实际观测中分析对流系统及其环境，从而能够对个别风暴进行长达数小时的准确预测。在对流引发过程的基本理解方面也将取得重大进展。了解预报对初始条件的敏感性以及与之相关的误差增长将为高分辨率观测网络的优化设计和部署提供重要指导，并有助于更好地理解对流系统的可预测性。使用先进的3DVAR技术处理倾斜路径GPS(全球定位系统)数据的工作预计将为部署国家高密度GPS地面接收器网络提供进一步支持。更广泛的影响：这项研究将直接涉及美国天气研究计划的三个关键研究主题之一，即通过优化使用观测数据和改进数值降水指导来改进暴雨和洪水预报。该项目将在高级数据同化、数值天气预报和集合预报等日益重要的领域为研究生和博士后科学家提供急需的教育和培训。研究成果将通过该集团参与天气研究和预报(WRF)模型的开发和测试，以及他们与国家气象局国家环境预报中心目标业务使用的WRF 3DVAR版本的合作，为业务提供直接的途径。因此，这项研究将直接有助于对降水天气系统的了解，并有助于改善数值预报。

Convective storms and associated strong winds and heavy precipitation cause billions of dollars of damage and numerous deaths annually; at the same time, accurate forecasting of severe weather and precipitation amounts are among the most challenging tasks in meteorology. The Principal Investigator will develop and apply advanced techniques and tools for predicting localized precipitation, and will study the impact of assimilated data and the sensitivities associated with initial and boundary conditions and model physics. The project will include detailed analyses of high-resolution numerical simulations in order to understand fundamental physical processes that determine how, when and where convective storms are initiated. The knowledge gained from the process and sensitivity studies will be applied to the design and improvement of data assimilation systems. The research will exploit observations collected in prior field campaigns. Intellectual Merit: Fundamental advances will include a significant improvement in the ability to optimally utilize the huge volume of real-time data from the national Doppler radar (WSR-88D) network for the initialization of high-resolution NWP models and for accurate short-term prediction of severe, high-impact weather. A cost-effective 3D variational analysis system coupled with a complex cloud analysis procedure will be refined and tested. The project is expected to produce, for the first time, analyses of convective systems, together with their environment, using the ensemble Kalman filter method from real observations, that would allow accurate predictions of individual storms for up to several hours. Significant progress will also be made in the fundamental understanding of convective initiation processes. The understanding of forecast sensitivity to initial conditions as well as the associated error growth will provide important guidance for the optimal design and deployment of high-resolution observational networks and can lead to a better understanding of the predictability of convective systems. The work with the slant-path GPS (Global Positioning System) data using an advanced 3DVAR technique is expected to lend further support for the deployment of a national high-density GPS surface receiver network. Broader Impacts: The research will directly address one of the three key research themes of the US Weather Research Program, namely, the improvement of forecasting heavy precipitation and flooding through optimal use of observational data and improved numerical precipitation guidance. This project will provide much needed education and training for graduate students and post-doctoral scientists in the increasingly important areas of advanced data assimilation, numerical weather prediction and ensemble forecasting. The research findings will have a direct path to operations through the group's involvement in the Weather Research and Forecast (WRF) model development and testing, and their work with the version of WRF 3DVAR targeted for operational use at the National Centers for Environmental Prediction of National Weather Service. The research therefore will directly contribute to the understanding of precipitating weather systems and to the improvement of NWP.