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

预防事件轨道 2：协作研究：影响强对流天气事件可预测性的多尺度过程

基本信息

批准号：
1854886
负责人：
Lance Bosart
金额：
$ 52万
依托单位：
SUNY at Albany
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-08-01 至 2024-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1854886&HistoricalAwards=false
关键词：
PREEVENTS Track Collaborative Research Multi

项目摘要

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.

威胁生命和财产的极端天气事件（母羊）可能会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，延长的干旱和过度降水。在区域尺度上，母羊会在强烈的降水系统产生龙卷风，冰雹，直线风和山洪泛滥时会发展。这项研究旨在改善与对流风暴相关的母羊的短期和长期预测，并更好地了解其可预测性限制。该项目的目标是1）对抑制熟练的局部危害预测的多尺度物理过程的理解，以及2）通过更好地表示预测不确定性来提高电子母羊的数值模型预测的有用性。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进行了一系列会议，以帮助操作预报员和应急管理人员了解他们的研究结果。这些会议还允许预报员和应急管理人员向PI展示他们如何将预测指导和不确定性信息纳入他们的决策过程，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。该研究分为三个阶段。第一阶段确定了多天的恶劣天气事件，以及一系列过程，导致了在美国中部和东部的对流爆发中发展有利的环境条件。包括基于对象的验证，时间平均的物理趋势诊断和集合灵敏度分析的工具用于对高影响力天气事件进行基于过程的研究。 II阶段使用该模型进行跨量表的预测来产生对流母羊的对流的集合预测。然后，在第一阶段开发的诊断方法被用来了解物理过程中的不确定性如何导致母羊前体的发展及其对下游前对流环境的影响。 III期研究物理过程表示对模型分辨率和积云参数化的敏感性。这项研究特别介绍了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的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的影响来通过评估来支持的。