基于弓形回波机器学习识别的美国厄尔尼诺气候学(2004-2021 年

IF 11.2 1区 地球科学 Q1 GEOSCIENCES, MULTIDISCIPLINARY
Jianfeng Li, Andrew Geiss, Zhe Feng, L. Ruby Leung, Yun Qian, Wenjun Cui
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引用次数: 0

摘要

摘要。由于持续大范围的强风,龙卷风对人类安全和财产构成重大威胁,其危害性和致命性不亚于许多龙卷风和飓风。然而,由于缺乏连续的时空观测数据,以及定义这种现象的标准非常复杂,因此自动探测龙卷风仍然具有挑战性。本研究提出了一个基于物理的 "德雷乔斯 "定义,该定义包含了文献中描述的 "德雷乔斯 "的主要特征,并允许使用观测数据或模型模拟自动客观地识别 "德雷乔斯"。自动检测由三种算法组成:用于跟踪中尺度对流系统(MCSs)的灵活目标跟踪算法、用于识别弓形回波的语义分割卷积神经网络,以及用于将中尺度对流系统分类为德雷赫斯或非德雷赫斯事件的综合算法。利用新方法,我们开发了一个新的高分辨率(4 公里和每小时)观测数据集,用于观测 2004 年至 2021 年洛基山脉以东美国上空的低回声。通过分析该数据集,我们记录了美国的厄尔尼诺气候。与之前的估计(每年约 6-21 次)相比,数据集中发现的厄尔尼诺现象要多得多(增加了约 50-400%)(每年约 31 次),但其空间分布和季节变化规律与之前的研究相似,暖季时大平原和中西部地区出现厄尔尼诺现象的高峰。此外,在数据集期间,美国落基山以东地区约有 20% 的破坏性阵风(≥ 25.93 m s-1)报告是由德雷乔斯造成的。该数据集可查阅 https://doi.org/10.5281/zenodo.10884046(Li 等人,2024 年)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A derecho climatology (2004–2021) in the United States based on machine learning identification of bow echoes
Abstract. Due to their persistent widespread severe winds, derechos pose significant threats to human safety and property, and they are as hazardous and fatal as many tornadoes and hurricanes. Yet, automated detection of derechos remains challenging due to the absence of spatiotemporally continuous observations and the complex criteria employed to define the phenomenon. This study proposes a physically based definition of derechos that contains the key features of derechos described in the literature and allows their automated objective identification using either observations or model simulations. The automated detection is composed of three algorithms: the Flexible Object Tracker algorithm to track mesoscale convective systems (MCSs), a semantic segmentation convolutional neural network to identify bow echoes, and a comprehensive algorithm to classify MCSs as derechos or non-derecho events. Using the new approach, we develop a novel high-resolution (4 km and hourly) observational dataset of derechos over the United States east of the Rocky Mountains from 2004 to 2021. The dataset is analyzed to document the derecho climatology in the United States. Many more derechos (increased by ~50–400 %) are identified in the dataset (~31 events per year) than in previous estimations (~6–21 events per year), but the spatial distribution and seasonal variation patterns resemble earlier studies with a peak occurrence in the Great Plains and Midwest during the warm season. In addition, around 20 % of damaging gust (≥ 25.93 m s-1) reports are produced by derechos during the dataset period over the United States east of the Rocky Mountains. The dataset is available at https://doi.org/10.5281/zenodo.10884046 (Li et al., 2024).
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来源期刊
Earth System Science Data
Earth System Science Data GEOSCIENCES, MULTIDISCIPLINARYMETEOROLOGY-METEOROLOGY & ATMOSPHERIC SCIENCES
CiteScore
18.00
自引率
5.30%
发文量
231
审稿时长
35 weeks
期刊介绍: Earth System Science Data (ESSD) is an international, interdisciplinary journal that publishes articles on original research data in order to promote the reuse of high-quality data in the field of Earth system sciences. The journal welcomes submissions of original data or data collections that meet the required quality standards and have the potential to contribute to the goals of the journal. It includes sections dedicated to regular-length articles, brief communications (such as updates to existing data sets), commentaries, review articles, and special issues. ESSD is abstracted and indexed in several databases, including Science Citation Index Expanded, Current Contents/PCE, Scopus, ADS, CLOCKSS, CNKI, DOAJ, EBSCO, Gale/Cengage, GoOA (CAS), and Google Scholar, among others.
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