PREEVENTS Track 2: Collaborative Research: Multi-scale processes impacting the predictability of severe convective weather events

威胁生命和财产的极端天气事件（EWE）可能发生在各种时间和空间制度中。在最大的尺度上，持续的天气模式会产生聚集的对流天气事件、持续的干旱和过度的降水。在区域尺度上，当强降水系统引起龙卷风、冰雹、直线风和山洪暴发时，EWE就会发展。这项研究旨在改善与对流风暴相关的EWE的短期和长期预测，并更好地了解其可预测性限制。该项目的目标是：1）发展多尺度物理过程的理解，抑制熟练的本地灾害预测，和2）通过更好地代表预测的不确定性，提高数值模型预测的EWE的有用性。该项目具有社会价值，因为它专注于提供更好的预测，以保护公众免受恶劣天气危害。PI召开一系列会议，以帮助业务预报员和应急管理人员了解他们的研究结果。 会议还允许预报员和应急管理人员向PI展示他们如何将预报指导和不确定性信息纳入决策过程，从而促进建设性的双向对话。 该项目还为两名研究生和一名博士后研究助理提供教育和培训。研究分三个阶段进行。第一阶段确定多日的恶劣天气事件，以及导致美国中部和东部对流爆发的有利环境条件发展的过程链。工具，包括基于对象的验证，时间平均的物理趋势诊断，和合奏灵敏度分析进行基于过程的调查的高影响天气事件。第二阶段使用跨尺度预测模型来产生一套对流EWE的对流允许集合预测。 在第一阶段开发的诊断，然后用来了解如何在物理过程中的不确定性导致不确定性的EWE前体的演变及其对下游的预对流环境的影响。 第三阶段研究物理过程表示对模式分辨率和积云参数化的敏感性。 该研究特别关注物理过程表示的简化如何限制下游恶劣天气的预测技能。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Extreme weather events (EWEs) that threaten life and property can occur across a variety of time and space regimes. On the largest scales, persistent weather patterns produce clustered convective weather events, extended drought, and excessive precipitation. On regional scales, EWEs develop when intense precipitation systems give rise to tornadoes, hail, straight-line winds, and flash flooding. This research seeks to improve short- and long-range predictions of EWEs associated with convective storms and to better understand their predictability limits. The objectives of the project are to 1) develop understanding of the multi-scale physical processes that inhibit skillful local hazard prediction, and 2) improve the usefulness of numerical model forecasts of EWEs by better representing forecast uncertainty. The project has societal value due to its focus on providing better predictions to protect the public from severe weather hazards. The PIs conduct a series of meetings to help operational forecasters and emergency managers understand their research results. The meetings also allow forecasters and emergency managers to show the PIs how they incorporate forecast guidance and uncertainty information into their decision process, thereby promoting a constructive two-way dialogue. The project also provides education and training to two graduate students and a postdoctoral research associate. The research is conducted in three phases. Phase I identifies multi-day severe weather episodes, and the chain of processes leading to the development of favorable environmental conditions for convective outbreaks across the central and eastern United States. Tools including object-based verification, time-averaged physics tendency diagnostics, and ensemble sensitivity analysis are used to conduct a process-based investigation of high-impact weather events. Phase II uses the Model for Prediction Across Scales to produce a suite of convection-permitting ensemble predictions of convective EWEs. Diagnostics developed in Phase I are then used to understand how uncertainty in physical processes leads to uncertainty in the evolution of EWE precursors and their impact on downstream pre-convective environment. Phase III investigates the sensitivity of physical process representation to model resolution and cumulus parameterization. The research looks specifically at how simplifications in physical process representation limit predictive skill for downstream severe weather.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.