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: 2312315
• 负责人: Brian Ancell
• 金额: $ 38.09万
• 依托单位: Texas Tech University
• 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=2312315&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.

龙卷风，大冰雹和洪水等恶劣天气的预测继续改善，使天气预报更好地帮助社会为危险和破坏性风暴做准备。这种改进的大部分是通过使用模型详细模拟大量大气的模型来理解严重雷暴的原因，这一过程需要现代计算机的速度和性能。这样的计算能力允许创建多个林业者，而不仅仅是在给定的风暴情况下创建一个森林人，从而突出了大气中的特征（例如水分或风剖面），导致了不同严重程度的风暴。这些简单的森林还揭示了即将到来的暴风雨可能严重的可能性，因为不同的森林都同意严重的条件（严重事件的可能性很高），或者森林显示出广泛幅度的风暴（较低的严重事件可能性）。尽管这些研究方法的重点是每天理解和改善严重的风暴预测，但在不断变化的气候下，高影响力天气事件的可预测性尚不清楚。该研究旨在了解由于地球的气候温暖，是否可以更好地预测严重的风暴及其相关危害。这项研究是独一无二的，因为它超越了其他研究，这些研究试图发现严重的风暴是否会或多或少会变得频繁，而是确定它们是否或多或少是可预测的，这是与不同气候支撑的一般大气条件相关的特征。这项工作将通过在最近（20世纪末）和未来（21世纪末）气候的情况下创建和分析严重风暴的数字天气模型森林的大型数据集来进行。具体来说，将评估不同世纪以来的森林如何以及为什么森林在不同的世纪中有所不同，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产生了许多预期的影响。了解严重风暴的有问题的森林是否会增加或减少不确定性，这可能表明是否可以在社交应用中有效使用此类森林。这样的例子包括水库操作，这些操作依赖于洪水风险的准确预测来有效管理水资源。如果洪水变得更容易预测，那么从预测确定性中受益的应用可能会变得更加普遍，从而实质上有助于区域供水，并减轻成为驾驶员地区气候变化的负面影响。这项研究还将涉及创建大量严重的风暴抑制模拟数据集，从而使那些希望分析数据的科学家研究此处建议的严重暴风雨气候关系的其他方面。几个研究生和本科生将通过多种方式参与研究：通过期刊文章，学术课程以及大学座谈会和专业科学会议的演讲，研究生和本科研究和传播。围绕参与大学的社区中的公众和K-12学生也将受益于计划的外展活动，包括在大学博物馆举行的周末活动，被大学批准的夏令营以及开放式活动，这些活动促进了与项目科学家在休闲环境中进行一对1互动的开放式活动。该项目是由该项目共同资助的，该项目由气候和大型动态和动力学氛围和综合氛围和动力学氛围以及综合氛围，以及综合氛围，并在氛围中，综合氛围，综合氛围，包括氛围，综合氛围，包括氛围，综合氛围，包括综合氛围。如Geo Embrace DCL中概述的各种机构类型的研究能力，能力和基础设施的项目的地理科学旨在提高研究能力，能力和基础设施。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响审查标准来通过评估来诚实地认为，通过评估诚实地认为。