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

对流风暴和相关的强风和强降水每年造成数十亿美元的损失和许多人死亡;与此同时，准确预测恶劣天气和降水量是气象学中最具挑战性的任务之一。 首席研究员将开发和应用预测局部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及模型物理学有关的敏感性。 该项目将包括对高分辨率数值模拟的详细分析，以了解决定对流风暴如何、何时和何地开始的基本物理过程。 从过程和敏感性研究中获得的知识将应用于数据同化系统的设计和改进。 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：根本性的进展将包括大大提高能力，以最佳方式利用来自国家多普勒雷达（WSR-88 D）网络的大量实时数据，用于高分辨率数值预报模式的初始化和严重、高影响天气的准确短期预报。 一个具有成本效益的三维变分分析系统加上一个复杂的云分析程序将得到完善和测试。 预计该项目将首次利用集合卡尔曼滤波法根据真实的观测结果对对流系统及其环境进行分析，从而能够对个别风暴进行长达数小时的准确预测。 在对流起始过程的基本认识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，并能更好地了解对流系统的可预测性。 利用先进的三维变差分析技术处理倾斜路径全球定位系统数据的工作预计将进一步支持部署国家高密度全球定位系统地面接收器网络。更广泛的影响：该研究将直接解决美国天气研究计划的三个关键研究主题之一，即通过最佳利用观测数据和改进数值降水指导来改善强降水和洪水预报。 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。 研究结果将通过该小组参与天气研究和预报（WRF）模型开发和测试，以及他们与WRF 3DVAR版本的工作直接进入运营，该版本的目标是在国家气象局的国家环境预测中心使用。 因此，这项研究将直接有助于了解降水天气系统和改进数值预报。

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.