Collaborative Research: Mesoscale Predictability Across Climate Regimes

合作研究：跨气候机制的中尺度可预测性

• 批准号: 2312316
• 负责人: Yonggang Wang
• 金额: $ 18.32万
• 依托单位: SUNY College at Oswego
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2312316&HistoricalAwards=false
• 关键词: Collaborative; Research; Mesoscale; Predictability; Across

The prediction of severe weather such as tornadoes, large hail, and flooding continues to improve, allowing weather forecasts to better help society prepare for dangerous and damaging storms. Much of this improvement has come through understanding the causes of severe thunderstorms using models that simulate large portions of the atmosphere in detail, a procedure that requires the speed and performance of modern-day computers. Such computational capability allows the creation of multiple forecasts instead of just one for a given storm situation, highlighting the features in the atmosphere – like the degree of moisture or the wind profile – that lead to storms of different severity. These simultaneous forecasts also reveal how likely it is that upcoming storms may be severe, based on whether the different forecasts all agree on severe conditions (high likelihood of a severe event) or if forecasts show storms with a wide range of magnitudes (lower likelihood of a severe event). While these research methods have focused on understanding and improving severe storm prediction on a day-to-day basis, the predictability of high-impact weather events in a changing climate is unclear. The research aims to understand whether severe storms and their associated hazards can be better predicted as Earth's climate warms. This research is unique in that it goes beyond other studies that seek to uncover whether severe storms will become more or less frequent, instead determining if they are more or less predictable, a characteristic linked to the general atmospheric conditions that different climates support. The work will be performed by creating and analyzing big datasets of numerical weather model forecasts of severe storms in both recent (end of 20th century) and future (end of 21st century) climates. Specifically, how and why forecasts for severe storm situations evolve differently in different centuries will be assessed to understand the role climate change plays in atmospheric prediction.There are numerous expected impacts of this work on the scientific community and society. Understanding if probabilistic forecasts of severe storms will have increased or decreased uncertainty could show whether such forecasts could be used effectively in societal applications. One such example includes water reservoir operations, which rely on accurate predictions of flood risk to efficiently manage water resources. If flooding were to become more predictable, applications like this that benefit from forecast certainty could become more common, substantially helping regional water supply and mitigating the negative consequences of climate change in areas that become drier. The research will also involve the creation of a large dataset of severe storm-resolving simulations, allowing scientists who wish to analyze the data to investigate other aspects of severe storm-climate relationships beyond that suggested here. Several graduate and undergraduate students will be involved in the research in several ways: graduate and undergraduate research and dissemination through journal articles, academic coursework, and presentations at university symposia and professional scientific conferences. The general public and K-12 students in communities surrounding the participating universities will also benefit from planned outreach events including weekend events at university museums, university-sanctioned summer camps, and open house events that promote 1-on-1 interaction in casual environments with project scientists.This project is jointly funded by the Climate and Large-Scale Dynamics and Physical and Dynamic Meteorology programs in the Division of Atmospheric and Geospace Sciences as well as the Division of Atmospheric and Geospace Sciences to support projects that increase research capabilities, capacity and infrastructure at a wide variety of institution types, as outlined in the GEO EMBRACE DCL.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.

对龙卷风、大冰雹和洪水等恶劣天气的预测不断改进，使天气预报能够更好地帮助社会为危险和破坏性风暴做好准备，这种改进很大程度上是通过使用模拟大型雷暴的模型来了解严重雷暴的原因。详细地描述大气的各个部分，这一过程需要现代计算机的速度和性能，这种计算能力可以针对给定的风暴情况创建多个预测，而不是仅一个预测，从而突出显示大气中的特征，例如程度。湿度或风廓线 – 即这些同步预报还揭示了即将到来的风暴有多可能是严重的，这取决于不同的预报是否都同意恶劣的情况（严重事件的可能性很高）或者预报是否显示风暴具有广泛的范围。虽然这些研究方法的重点是了解和改进日常的强风暴预测，但气候变化研究中高影响天气事件的可预测性尚不清楚。旨在了解是否有严重风暴随着地球气候变暖，可以更好地预测其相关的危害，这项研究的独特之处在于，它超越了其他旨在揭示严重风暴是否会变得更加频繁或更少的研究，而是确定它们是否或多或少是可预测的，这是一个特征。这项工作将通过创建和分析近期（20 世纪末）和未来（21 世纪末）气候中严重风暴的数值天气模型预测的大数据集来进行。 ， 如何以及为什么对不同世纪的强风暴情况的预测会有不同的演变，以了解气候变化在大气预测中的作用。这项工作对科学界和社会有许多预期的影响。了解强风暴的概率预报是否会产生影响。增加或减少不确定性可以表明这种预测是否可以有效地用于社会应用，其中一个例子包括水库运行，它依赖于洪水风险的准确预测来有效管理水资源，如果洪水变得更加可预测，那么像这样的应用。从预测确定性中获益可能会变得更加普遍，该研究还将涉及创建一个大型的解决严重风暴的模拟数据集，使希望分析数据的科学家能够调查其他方面的情况。一些研究生和本科生将以多种方式参与研究：通过期刊文章、学术课程以及在大学研讨会和专业科学会议上的演讲进行研究生和本科生的研究和传播。参与大学周边社区的 K-12 学生也将受益于计划中的外展活动，包括大学博物馆的周末活动、大学批准的夏令营以及促进在休闲环境中与项目科学家进行一对一互动的开放日活动。该项目由大气和地球空间科学司的气候和大尺度动力学以及物理和动态气象学计划以及大气和地球空间科学司共同资助，以支持提高研究能力、能力和基础设施的项目正如 GEO EMBRACE DCL 中概述的那样，机构类型多种多样。该奖项反映了 NSF 的法定使命，并且通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。