Development of Advanced Guidance for Forecasting High-Impact Weather

9714154 Fritsch 美国天气研究计划 (USWRP) 是一项跨机构活动，旨在开展和实施必要的研究，以改善向国家提供的天气服务。 根据该计划，美国国家科学基金会、美国国家海洋和大气管理局、美国国家航空航天局和海军研究办公室正在联合评估和支持 USWRP 的高度优先研究。 有据可查的是，通过统计后处理可以大大提高原始观测值和数值模型输出的效用。 数值建模和新观测系统安装的最新进展为重新设计现有后处理技术和开发新技术创造了机会和需求。 首席研究员将设计、构建和测试新的统计后处理程序，该程序将利用新兴的集成技术，并将操作数值模型的变化对统计后处理程序的影响降至最低。 他还将构建一个先进的基于观测的统计预报系统，为高影响天气提供可靠的概率短期指导。 基于观测的系统将利用WSR-88D多普勒雷达等高频观测平台和卫星信息。 所提出的工作的主要工具将是通用加性模型的应用，该模型允许统计回归关系中的某些非线性。 预计由此产生的技术将显着缩短敏感天气参数的预报时间，并为高影响天气事件期间的短期决策提供急需的概率指导。 ***

9714154 Fritsch The U.S. Weather Research Program (USWRP) is an interagency activity designed to perform and implement the research necessary to improve the delivery of weather services to the nation. Under this Program, the National Science Foundation, the National Oceanic and Atmospheric Administration, the National Aeronautics and Space Administration and the Office of Naval Research are jointly evaluating and supporting research of high priority to the USWRP. It is well documented that the utility of raw observations and numerical model output can be greatly enhanced by statistical post-processing. Recent advances in numerical modeling and the installation of new observing systems have created an opportunity and a need to redesign existing post-processing techniques and to develop new techniques. The Principal Investigator will design, construct, and test new statistical post-processing procedures that will capitalize on the emerging ensemble technology and will minimize the effects of changes in operational numerical models on statistical post-processing procedures. He will also construct an advanced observations-based statistical forecasting system for providing reliable probabilistic short-term guidance on high-impact weather. The observations- based system will capitalize on high-frequency observing platforms such as WSR-88D Doppler radars and satellite information. A primary tool of the proposed work will be the application of general additive models that allow for certain non- linearities in statistical regression relationships. It is expected that the resulting techniques will yield significant lead time advances in forecasts of sensible weather parameters and provide much-needed probabilistic guidance for short-term decision making during high-impact weather events. ***