Suppr超能文献

长期天气条件的天气尺度趋势有利于美国中部重大龙卷风的发生。

Long term temporal trends in synoptic-scale weather conditions favoring significant tornado occurrence over the central United States.

机构信息

Department of Mathematics and Statistics, Cleveland State University, Cleveland, OH, United States of America.

Department of Geography, Virginia Tech, Blacksburg, VA, United States of America.

出版信息

PLoS One. 2023 Feb 22;18(2):e0281312. doi: 10.1371/journal.pone.0281312. eCollection 2023.

Abstract

We perform a statistical climatological study of the synoptic- to meso-scale weather conditions favoring significant tornado occurrence to empirically investigate the existence of long term temporal trends. To identify environments that favor tornadoes, we apply an empirical orthogonal function (EOF) analysis to temperature, relative humidity, and winds from the Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2) dataset. We consider MERRA-2 data and tornado data from 1980 to 2017 over four adjacent study regions that span the Central, Midwestern, and Southeastern United States. To identify which EOFs are related to significant tornado occurrence, we fit two separate groups of logistic regression models. The first group (LEOF models) estimates the probability of occurrence of a significant tornado day (EF2-EF5) within each region. The second group (IEOF models) classifies the intensity of tornadic days either as strong (EF3-EF5) or weak (EF1-EF2). When compared to approaches using proxies such as convective available potential energy, our EOF approach is advantageous for two main reasons: first, the EOF approach allows for the discovery of important synoptic- to mesoscale variables previously not considered in the tornado science literature; second, proxy-based analyses may not capture important aspects of three-dimensional atmospheric conditions represented by the EOFs. Indeed, one of our main novel findings is the importance of a stratospheric forcing mode on occurrence of significant tornadoes. Other important novel findings are the existence of long-term temporal trends in the stratospheric forcing mode, in a dry line mode, and in an ageostrophic circulation mode related to the jet stream configuration. A relative risk analysis also indicates that changes in stratospheric forcings are partially or completely offsetting increased tornado risk associated with the dry line mode, except in the eastern Midwest region where tornado risk is increasing.

摘要

我们进行了一项统计气候学研究,分析有利于重大龙卷风发生的天气条件,以实证研究长期时间趋势的存在。为了确定有利于龙卷风的环境,我们应用经验正交函数(EOF)分析方法对温度、相对湿度和现代回顾分析研究与应用版本 2(MERRA-2)数据集的风进行分析。我们考虑了 1980 年至 2017 年期间覆盖美国中部、中西部和东南部的四个相邻研究区域的 MERRA-2 数据和龙卷风数据。为了确定哪些 EOF 与重大龙卷风发生有关,我们拟合了两组独立的逻辑回归模型。第一组(LEOF 模型)估计每个区域内重大龙卷风日(EF2-EF5)发生的概率。第二组(IEOF 模型)将有龙卷风的日子分为强(EF3-EF5)和弱(EF1-EF2)两类。与使用如对流有效位能等代理的方法相比,我们的 EOF 方法有两个主要优势:首先,EOF 方法允许发现以前在龙卷风科学文献中未考虑的重要天气尺度变量;其次,代理分析可能无法捕捉 EOF 所代表的三维大气条件的重要方面。事实上,我们的一个主要新发现是平流层强迫模式对重大龙卷风发生的重要性。其他重要的新发现是平流层强迫模式、干锋模式和与急流配置有关的非地转环流模式中存在长期时间趋势。相对风险分析还表明,平流层强迫的变化部分或完全抵消了与干锋模式相关的龙卷风风险增加,除了中西部东部地区,那里的龙卷风风险正在增加。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/76d2/9946245/dcb5dea4298e/pone.0281312.g001.jpg

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验