A derecho climatology (2004–2021) in the United States based on machine learning identification of bow echoes
<p>Due to their persistent widespread severe winds, derechos pose significant threats to human safety and property, with impacts comparable to many tornadoes and hurricanes. Yet, automated detection of derechos remains challenging due to the absence of spatiotemporally continuous observations...
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Copernicus Publications
2025-08-01
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| Series: | Earth System Science Data |
| Online Access: | https://essd.copernicus.org/articles/17/3721/2025/essd-17-3721-2025.pdf |
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| author | J. Li A. Geiss Z. Feng L. R. Leung Y. Qian W. Cui W. Cui |
| author_facet | J. Li A. Geiss Z. Feng L. R. Leung Y. Qian W. Cui W. Cui |
| author_sort | J. Li |
| collection | DOAJ |
| description | <p>Due to their persistent widespread severe winds, derechos pose significant threats to human safety and property, with impacts comparable to 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 presents an objective derecho detection approach capable of automatically identifying derechos through both observations and model results. The approach is grounded in a physically based definition of derechos and integrates three algorithms: (1) the Python Flexible Object Tracker (PyFLEXTRKR) algorithm to track mesoscale convective systems (MCSs), (2) a semantic segmentation convolutional neural network to identify bow echoes, and (3) a comprehensive classification algorithm to detect derechos within MCS life cycles and distinguish derecho-producing from non-derecho-producing MCSs. Using this approach, we developed a novel high-resolution (4 km and hourly) observational dataset of derechos and accompanying derecho-producing MCSs over the United States east of the Rocky Mountains from 2004 to 2021. The dataset consists of two subsets based on different gust speed data sources and is analyzed to document the climatology of derechos in the United States. On average, 12–15 derechos are identified per year, aligning with previous estimations (<span class="inline-formula">∼6</span>–21 events annually). The spatial distribution and seasonal variation patterns are consistent with prior studies, showing peak occurrences in the Great Plains and the Midwest during the warm season. Additionally, during the study period, derechos account for approximately 3.1 % of measured damaging gusts (<span class="inline-formula">≥25.93</span> m s<span class="inline-formula"><sup>−1</sup></span>) over the eastern United States. The dataset is publicly available at <a href="https://doi.org/10.5281/zenodo.14835362">https://doi.org/10.5281/zenodo.14835362</a> (Li et al., 2025).</p> |
| format | Article |
| id | doaj-art-0520039fd21a4bef86c535e4ceb232ff |
| institution | DOAJ |
| issn | 1866-3508 1866-3516 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Earth System Science Data |
| spelling | doaj-art-0520039fd21a4bef86c535e4ceb232ff2025-08-20T03:18:53ZengCopernicus PublicationsEarth System Science Data1866-35081866-35162025-08-01173721374010.5194/essd-17-3721-2025A derecho climatology (2004–2021) in the United States based on machine learning identification of bow echoesJ. Li0A. Geiss1Z. Feng2L. R. Leung3Y. Qian4W. Cui5W. Cui6Atmospheric, Climate, and Earth Sciences Division, Pacific Northwest National Laboratory, Richland, Washington, USAAtmospheric, Climate, and Earth Sciences Division, Pacific Northwest National Laboratory, Richland, Washington, USAAtmospheric, Climate, and Earth Sciences Division, Pacific Northwest National Laboratory, Richland, Washington, USAAtmospheric, Climate, and Earth Sciences Division, Pacific Northwest National Laboratory, Richland, Washington, USAAtmospheric, Climate, and Earth Sciences Division, Pacific Northwest National Laboratory, Richland, Washington, USACooperative Institute for Severe and High-Impact Weather Research and Operations, University of Oklahoma, Norman, Oklahoma, USANational Severe Storms Laboratory, National Oceanic and Atmospheric Administration, Norman, Oklahoma, USA<p>Due to their persistent widespread severe winds, derechos pose significant threats to human safety and property, with impacts comparable to 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 presents an objective derecho detection approach capable of automatically identifying derechos through both observations and model results. The approach is grounded in a physically based definition of derechos and integrates three algorithms: (1) the Python Flexible Object Tracker (PyFLEXTRKR) algorithm to track mesoscale convective systems (MCSs), (2) a semantic segmentation convolutional neural network to identify bow echoes, and (3) a comprehensive classification algorithm to detect derechos within MCS life cycles and distinguish derecho-producing from non-derecho-producing MCSs. Using this approach, we developed a novel high-resolution (4 km and hourly) observational dataset of derechos and accompanying derecho-producing MCSs over the United States east of the Rocky Mountains from 2004 to 2021. The dataset consists of two subsets based on different gust speed data sources and is analyzed to document the climatology of derechos in the United States. On average, 12–15 derechos are identified per year, aligning with previous estimations (<span class="inline-formula">∼6</span>–21 events annually). The spatial distribution and seasonal variation patterns are consistent with prior studies, showing peak occurrences in the Great Plains and the Midwest during the warm season. Additionally, during the study period, derechos account for approximately 3.1 % of measured damaging gusts (<span class="inline-formula">≥25.93</span> m s<span class="inline-formula"><sup>−1</sup></span>) over the eastern United States. The dataset is publicly available at <a href="https://doi.org/10.5281/zenodo.14835362">https://doi.org/10.5281/zenodo.14835362</a> (Li et al., 2025).</p>https://essd.copernicus.org/articles/17/3721/2025/essd-17-3721-2025.pdf |
| spellingShingle | J. Li A. Geiss Z. Feng L. R. Leung Y. Qian W. Cui W. Cui A derecho climatology (2004–2021) in the United States based on machine learning identification of bow echoes Earth System Science Data |
| title | A derecho climatology (2004–2021) in the United States based on machine learning identification of bow echoes |
| title_full | A derecho climatology (2004–2021) in the United States based on machine learning identification of bow echoes |
| title_fullStr | A derecho climatology (2004–2021) in the United States based on machine learning identification of bow echoes |
| title_full_unstemmed | A derecho climatology (2004–2021) in the United States based on machine learning identification of bow echoes |
| title_short | A derecho climatology (2004–2021) in the United States based on machine learning identification of bow echoes |
| title_sort | derecho climatology 2004 2021 in the united states based on machine learning identification of bow echoes |
| url | https://essd.copernicus.org/articles/17/3721/2025/essd-17-3721-2025.pdf |
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