Pub Date : 2024-02-26DOI: 10.1175/bams-d-23-0110.1
Fan Mei, Hailong Wang, Zihua Zhu, Damao Zhang, Qi Zhang, Jerome D. Fast, William I. Gustafson, Xiangyu Li, Beat Schmid, Christopher Niedek, Jason Tomlinson, Connor Flynn
Abstract The spatial distribution of ambient aerosol particles significantly impacts aerosol- radiation-cloud interactions, which contribute to the largest uncertainty in global anthropogenic radiative forcing estimations. However, the atmospheric boundary layer and lower free troposphere have not been adequately sampled in terms of spatiotemporal resolution, hindering a comprehensive characterization of various atmospheric processes and impeding our understanding of the Earth system. To address this research data gap, we have leveraged the development of uncrewed aerial systems (UAS) and advanced measurement techniques to obtain mesoscale spatial data on aerosol microphysical and optical properties around the U.S. Southern Great Plains (SGP) atmospheric observatory. Our study also benefits from state-of-the-art laboratory facilities that include 3-dimensional molecular imaging techniques enabled by secondary ion mass spectrometry and nanogram-level chemical composition analysis via micronebulization aerosol mass spectrometry. Through our study, we have developed a framework for observation-modeling integration, enabling an examination of how various assumptions about the organic-inorganic components mixing state, inferred from chemical analysis, affect clouds and radiation in observation-constrained model simulations. By integrating observational constraints (derived from offline chemical analysis of the aerosol surface using collected samples) with in-situ UAS observations, we have identified a prominent role of organic-enriched nanometer layers located at the surface of aerosol particles in determining profiles of aerosol optical and hygroscopic properties over the SGP observatory. Furthermore, we have improved the agreement between predicted clouds and ground-based cloud lidar measurements. This UAS-model-laboratory integration exemplifies how these new advanced capabilities can significantly enhance our understanding of aerosol-radiation-cloud interactions.
{"title":"Bridging new observational capabilities and process-level simulation: Insights into aerosol roles in the Earth system","authors":"Fan Mei, Hailong Wang, Zihua Zhu, Damao Zhang, Qi Zhang, Jerome D. Fast, William I. Gustafson, Xiangyu Li, Beat Schmid, Christopher Niedek, Jason Tomlinson, Connor Flynn","doi":"10.1175/bams-d-23-0110.1","DOIUrl":"https://doi.org/10.1175/bams-d-23-0110.1","url":null,"abstract":"Abstract The spatial distribution of ambient aerosol particles significantly impacts aerosol- radiation-cloud interactions, which contribute to the largest uncertainty in global anthropogenic radiative forcing estimations. However, the atmospheric boundary layer and lower free troposphere have not been adequately sampled in terms of spatiotemporal resolution, hindering a comprehensive characterization of various atmospheric processes and impeding our understanding of the Earth system. To address this research data gap, we have leveraged the development of uncrewed aerial systems (UAS) and advanced measurement techniques to obtain mesoscale spatial data on aerosol microphysical and optical properties around the U.S. Southern Great Plains (SGP) atmospheric observatory. Our study also benefits from state-of-the-art laboratory facilities that include 3-dimensional molecular imaging techniques enabled by secondary ion mass spectrometry and nanogram-level chemical composition analysis via micronebulization aerosol mass spectrometry. Through our study, we have developed a framework for observation-modeling integration, enabling an examination of how various assumptions about the organic-inorganic components mixing state, inferred from chemical analysis, affect clouds and radiation in observation-constrained model simulations. By integrating observational constraints (derived from offline chemical analysis of the aerosol surface using collected samples) with in-situ UAS observations, we have identified a prominent role of organic-enriched nanometer layers located at the surface of aerosol particles in determining profiles of aerosol optical and hygroscopic properties over the SGP observatory. Furthermore, we have improved the agreement between predicted clouds and ground-based cloud lidar measurements. This UAS-model-laboratory integration exemplifies how these new advanced capabilities can significantly enhance our understanding of aerosol-radiation-cloud interactions.","PeriodicalId":9464,"journal":{"name":"Bulletin of the American Meteorological Society","volume":null,"pages":null},"PeriodicalIF":8.0,"publicationDate":"2024-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139969295","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-26DOI: 10.1175/bams-d-23-0132.1
Xinru Liu, Hang Jie, Yulin Zou, Shengjun Liu, Yamin Hu, Shuyi Liu, Dangfu Yang, Liang Zhao, Jian He
Abstract According to HadGEM3 (CMIP6) models, anthropogenic forcing reduced the probability of 2022-like June mean precipitation by about 32% (15%) and increased 5-day rainfall extreme probability by about 1.8 (1.3) times.
{"title":"Anthropogenic Influence on 2022 June Extreme Rainfall over the Pearl River Basin","authors":"Xinru Liu, Hang Jie, Yulin Zou, Shengjun Liu, Yamin Hu, Shuyi Liu, Dangfu Yang, Liang Zhao, Jian He","doi":"10.1175/bams-d-23-0132.1","DOIUrl":"https://doi.org/10.1175/bams-d-23-0132.1","url":null,"abstract":"Abstract According to HadGEM3 (CMIP6) models, anthropogenic forcing reduced the probability of 2022-like June mean precipitation by about 32% (15%) and increased 5-day rainfall extreme probability by about 1.8 (1.3) times.","PeriodicalId":9464,"journal":{"name":"Bulletin of the American Meteorological Society","volume":null,"pages":null},"PeriodicalIF":8.0,"publicationDate":"2024-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139969011","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-23DOI: 10.1175/bams-d-22-0268.1
Elizabeth Tirone, Subrata Pal, William A Gallus, Somak Dutta, Ranjan Maitra, Jennifer Newman, Eric Weber, Israel Jirak
Abstract Many concerns are known to exist with thunderstorm wind reports in the National Center for Environmental Information Storm Events Database, including the overestimation of wind speed, changes in report frequency due to population density, and differences in reporting due to damage tracers. These concerns are especially pronounced with reports that are not associated with a wind speed measurement, but are estimated, which make up almost 90% of the database. We have used machine learning to predict the probability that a severe wind report was caused by severe intensity wind, or wind ≥ 50 kt. A total of six machine learning models were trained on 11 years of measured thunderstorm wind reports, along with meteorological parameters, population density, and elevation. Objective skill metrics such as the area under the ROC curve (AUC), Brier score, and reliability curves suggest that the best performing model is the stacked generalized linear model, which has an AUC around 0.9 and a Brier score around 0.1. The outputs from these models have many potential uses such as forecast verification and quality control for implementation in forecast tools. Our tool was evaluated favorably at the Hazardous Weather Testbed Spring Forecasting Experiments in 2020, 2021, and 2022.
{"title":"A Machine Learning Approach to Improve the Usability of Severe Thunderstorm Wind Reports","authors":"Elizabeth Tirone, Subrata Pal, William A Gallus, Somak Dutta, Ranjan Maitra, Jennifer Newman, Eric Weber, Israel Jirak","doi":"10.1175/bams-d-22-0268.1","DOIUrl":"https://doi.org/10.1175/bams-d-22-0268.1","url":null,"abstract":"Abstract Many concerns are known to exist with thunderstorm wind reports in the National Center for Environmental Information Storm Events Database, including the overestimation of wind speed, changes in report frequency due to population density, and differences in reporting due to damage tracers. These concerns are especially pronounced with reports that are not associated with a wind speed measurement, but are estimated, which make up almost 90% of the database. We have used machine learning to predict the probability that a severe wind report was caused by severe intensity wind, or wind ≥ 50 kt. A total of six machine learning models were trained on 11 years of measured thunderstorm wind reports, along with meteorological parameters, population density, and elevation. Objective skill metrics such as the area under the ROC curve (AUC), Brier score, and reliability curves suggest that the best performing model is the stacked generalized linear model, which has an AUC around 0.9 and a Brier score around 0.1. The outputs from these models have many potential uses such as forecast verification and quality control for implementation in forecast tools. Our tool was evaluated favorably at the Hazardous Weather Testbed Spring Forecasting Experiments in 2020, 2021, and 2022.","PeriodicalId":9464,"journal":{"name":"Bulletin of the American Meteorological Society","volume":null,"pages":null},"PeriodicalIF":8.0,"publicationDate":"2024-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139949736","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-21DOI: 10.1175/bams-d-23-0159.1
Machiel Lamers, Gita Ljubicic, Rick Thoman, Jorge Carrasco, Jackie Dawson, Victoria J. Heinrich, Jelmer Jeuring, Daniela Liggett, Emma J. Stewart
Abstract The Polar Prediction Project (PPP), one of the flagship programmes of the World Meteorological Organisation’s (WMO) World Weather Research Programme (WWRP), has come to an end after a decade of intensive and coordinated international observing, modelling, verification, user engagement, and education activities. While PPP facilitated many advancements in modelling and forecasting, critical investment is now required to turn prediction science into salient environmental services for the Polar Regions. In this commentary, the members of the Societal and Economic Research and Applications task team of PPP, a group of social scientists and service delivery specialists, identify a number of insights and lessons that are critical for the implementation of the follow up programme Polar Coupled Analysis and Prediction for Services (PCAPS). We argue that in order to raise the societal value of polar environmental services we need: to better understand the diversity of highly specific user contexts; to tailor the actionability of weather, water, ice and climate (WWIC) service development in the Polar Regions through inclusive transdisciplinary approaches to co-production; to assess the societal impact of improved environmental services in the Polar Regions; and to invest and provide dedicated funding for involving the social sciences in research and tailoring processes across all the Polar Regions.
{"title":"Tailored investments needed to support weather, water, ice and climate services in the Polar Regions","authors":"Machiel Lamers, Gita Ljubicic, Rick Thoman, Jorge Carrasco, Jackie Dawson, Victoria J. Heinrich, Jelmer Jeuring, Daniela Liggett, Emma J. Stewart","doi":"10.1175/bams-d-23-0159.1","DOIUrl":"https://doi.org/10.1175/bams-d-23-0159.1","url":null,"abstract":"Abstract The Polar Prediction Project (PPP), one of the flagship programmes of the World Meteorological Organisation’s (WMO) World Weather Research Programme (WWRP), has come to an end after a decade of intensive and coordinated international observing, modelling, verification, user engagement, and education activities. While PPP facilitated many advancements in modelling and forecasting, critical investment is now required to turn prediction science into salient environmental services for the Polar Regions. In this commentary, the members of the Societal and Economic Research and Applications task team of PPP, a group of social scientists and service delivery specialists, identify a number of insights and lessons that are critical for the implementation of the follow up programme Polar Coupled Analysis and Prediction for Services (PCAPS). We argue that in order to raise the societal value of polar environmental services we need: to better understand the diversity of highly specific user contexts; to tailor the actionability of weather, water, ice and climate (WWIC) service development in the Polar Regions through inclusive transdisciplinary approaches to co-production; to assess the societal impact of improved environmental services in the Polar Regions; and to invest and provide dedicated funding for involving the social sciences in research and tailoring processes across all the Polar Regions.","PeriodicalId":9464,"journal":{"name":"Bulletin of the American Meteorological Society","volume":null,"pages":null},"PeriodicalIF":8.0,"publicationDate":"2024-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139925735","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-15DOI: 10.1175/bams-d-22-0270.1
Janet Sprintall, Motoki Nagura, Juliet Hermes, M. K. Roxy, Michael J. McPhaden, E. Pattabhi Rama Rao, Srinivasa Kumar Tummala, Sidney Thurston, Jing Li, Mathieu Belbeoch, Victor Turpin
Abstract Observing and understanding the state of the Indian Ocean and its influence on climate and maritime resources is of critical importance to the populous nations that rim its border. Acute gaps have occurred in the Indian Ocean observing system, which underpins monitoring and forecasting of regional climate, since the start of the COVID pandemic. The pandemic disrupted the deployment and maintenance cruises for the observational array and also resulted in supply chain issues for procurement and refurbishment of equipment. In particular, the observational platforms that provide key measurements of upper ocean heat variability have experienced serious multi-year declines. There is now record-low data reporting and the platforms that are successfully reporting are old and quickly surpassing their expected period of reliable operation. The overall impact on the observing system will take a few years to fully comprehend. In the meantime, there is a critical need to document the gaps that have appeared over the past few years and how this will impact our ability to improve understanding and model representations of the real world that support regional weather and climate forecasts. The article outlines the expected slow road to recovery for the Indian Ocean observing system, documents case studies of successful international collaborative efforts that will revive the observing system and provides guidelines for resilience from unexpected external factors in the future.
{"title":"COVID Impacts Cause Critical Gaps in the Indian Ocean Observing System","authors":"Janet Sprintall, Motoki Nagura, Juliet Hermes, M. K. Roxy, Michael J. McPhaden, E. Pattabhi Rama Rao, Srinivasa Kumar Tummala, Sidney Thurston, Jing Li, Mathieu Belbeoch, Victor Turpin","doi":"10.1175/bams-d-22-0270.1","DOIUrl":"https://doi.org/10.1175/bams-d-22-0270.1","url":null,"abstract":"Abstract Observing and understanding the state of the Indian Ocean and its influence on climate and maritime resources is of critical importance to the populous nations that rim its border. Acute gaps have occurred in the Indian Ocean observing system, which underpins monitoring and forecasting of regional climate, since the start of the COVID pandemic. The pandemic disrupted the deployment and maintenance cruises for the observational array and also resulted in supply chain issues for procurement and refurbishment of equipment. In particular, the observational platforms that provide key measurements of upper ocean heat variability have experienced serious multi-year declines. There is now record-low data reporting and the platforms that are successfully reporting are old and quickly surpassing their expected period of reliable operation. The overall impact on the observing system will take a few years to fully comprehend. In the meantime, there is a critical need to document the gaps that have appeared over the past few years and how this will impact our ability to improve understanding and model representations of the real world that support regional weather and climate forecasts. The article outlines the expected slow road to recovery for the Indian Ocean observing system, documents case studies of successful international collaborative efforts that will revive the observing system and provides guidelines for resilience from unexpected external factors in the future.","PeriodicalId":9464,"journal":{"name":"Bulletin of the American Meteorological Society","volume":null,"pages":null},"PeriodicalIF":8.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139762356","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-13DOI: 10.1175/bams-d-22-0245.1
Elizabeth M. Page, Samuel S. P. Shen, Richard C. J. Somerville
"Can we do better at teaching mathematics to undergraduate atmospheric science students?" published on 13 Feb 2024 by American Meteorological Society.
"美国气象学会于 2024 年 2 月 13 日发表了 "我们能否更好地向大气科学本科生教授数学？
{"title":"Can we do better at teaching mathematics to undergraduate atmospheric science students?","authors":"Elizabeth M. Page, Samuel S. P. Shen, Richard C. J. Somerville","doi":"10.1175/bams-d-22-0245.1","DOIUrl":"https://doi.org/10.1175/bams-d-22-0245.1","url":null,"abstract":"\"Can we do better at teaching mathematics to undergraduate atmospheric science students?\" published on 13 Feb 2024 by American Meteorological Society.","PeriodicalId":9464,"journal":{"name":"Bulletin of the American Meteorological Society","volume":null,"pages":null},"PeriodicalIF":8.0,"publicationDate":"2024-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139762419","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-12DOI: 10.1175/bams-d-24-0019.1
Antje Weisheimer, Laura H. Baker, Jochen Bröcker, Chaim I. Garfinkel, Steven C. Hardiman, Dan L.R. Hodson, Tim N. Palmer, Jon I. Robson, Adam A. Scaife, James A. Screen, Theodore G. Shepherd, Doug M. Smith, Rowan T. Sutton
"The Signal-to-Noise Paradox in Climate Forecasts: Revisiting our Understanding and Identifying Future Priorities" published on 12 Feb 2024 by American Meteorological Society.
{"title":"The Signal-to-Noise Paradox in Climate Forecasts: Revisiting our Understanding and Identifying Future Priorities","authors":"Antje Weisheimer, Laura H. Baker, Jochen Bröcker, Chaim I. Garfinkel, Steven C. Hardiman, Dan L.R. Hodson, Tim N. Palmer, Jon I. Robson, Adam A. Scaife, James A. Screen, Theodore G. Shepherd, Doug M. Smith, Rowan T. Sutton","doi":"10.1175/bams-d-24-0019.1","DOIUrl":"https://doi.org/10.1175/bams-d-24-0019.1","url":null,"abstract":"\"The Signal-to-Noise Paradox in Climate Forecasts: Revisiting our Understanding and Identifying Future Priorities\" published on 12 Feb 2024 by American Meteorological Society.","PeriodicalId":9464,"journal":{"name":"Bulletin of the American Meteorological Society","volume":null,"pages":null},"PeriodicalIF":8.0,"publicationDate":"2024-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139762408","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-05DOI: 10.1175/bams-d-23-0148.1
Jinbo Xie, Qi Tang, Jean-Christophe Golaz, Wuyin Lin
Abstract Human-induced warming is estimated to have increased occurrence probability (magnitude) of the record-breaking September 2022 heat event in western North America by 6–67 times (0.6–1 K) by E3SMv2 and even higher by coupled regional refined model (RRM) simulations.
{"title":"Record High 2022 September-Mean Temperature in Western North America","authors":"Jinbo Xie, Qi Tang, Jean-Christophe Golaz, Wuyin Lin","doi":"10.1175/bams-d-23-0148.1","DOIUrl":"https://doi.org/10.1175/bams-d-23-0148.1","url":null,"abstract":"Abstract Human-induced warming is estimated to have increased occurrence probability (magnitude) of the record-breaking September 2022 heat event in western North America by 6–67 times (0.6–1 K) by E3SMv2 and even higher by coupled regional refined model (RRM) simulations.","PeriodicalId":9464,"journal":{"name":"Bulletin of the American Meteorological Society","volume":null,"pages":null},"PeriodicalIF":8.0,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139762353","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-01DOI: 10.1175/bams-d-23-0332.1
Clare Eayrs, Won Sang Lee, Emilia Jin, Jean-François Lemieux, François Massonnet, Martin Vancoppenolle, Lorenzo Zampieri, Luke G. Bennetts, Ed Blockley, Eui-Seok Chung, Alexander D. Fraser, Yoo-geun Ham, Jungho Im, Baek-min Kim, Beong-Hoon Kim, Jinsuk Kim, Joo-Hong Kim, Seong-Joong Kim, Seung Hee Kim, Anton Korosov, Choon-Ki Lee, Donghyuck Lee, Hyun-Ju Lee, Jeong-Gil Lee, Jiyeon Lee, Jisung Na, In-woo Park, Jikang Park, Xianwei Wang, Shiming Xu, Sukyoung Yun
"Advances in machine learning techniques can assist across a variety of stages in sea ice applications" published on 01 Feb 2024 by American Meteorological Society.
{"title":"Advances in machine learning techniques can assist across a variety of stages in sea ice applications","authors":"Clare Eayrs, Won Sang Lee, Emilia Jin, Jean-François Lemieux, François Massonnet, Martin Vancoppenolle, Lorenzo Zampieri, Luke G. Bennetts, Ed Blockley, Eui-Seok Chung, Alexander D. Fraser, Yoo-geun Ham, Jungho Im, Baek-min Kim, Beong-Hoon Kim, Jinsuk Kim, Joo-Hong Kim, Seong-Joong Kim, Seung Hee Kim, Anton Korosov, Choon-Ki Lee, Donghyuck Lee, Hyun-Ju Lee, Jeong-Gil Lee, Jiyeon Lee, Jisung Na, In-woo Park, Jikang Park, Xianwei Wang, Shiming Xu, Sukyoung Yun","doi":"10.1175/bams-d-23-0332.1","DOIUrl":"https://doi.org/10.1175/bams-d-23-0332.1","url":null,"abstract":"\"Advances in machine learning techniques can assist across a variety of stages in sea ice applications\" published on 01 Feb 2024 by American Meteorological Society.","PeriodicalId":9464,"journal":{"name":"Bulletin of the American Meteorological Society","volume":null,"pages":null},"PeriodicalIF":8.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139664629","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-31DOI: 10.1175/bams-d-24-0001.1
Grant Firl, Ligia Bernardet, Lulin Xue, Dustin Swales, Laura Fowler, Courtney Peverly, Ming Xue, Fanglin Yang
"Envisioning the Future of Community Physics" published on 31 Jan 2024 by American Meteorological Society.
"美国气象学会于 2024 年 1 月 31 日出版的《展望社区物理学的未来》。
{"title":"Envisioning the Future of Community Physics","authors":"Grant Firl, Ligia Bernardet, Lulin Xue, Dustin Swales, Laura Fowler, Courtney Peverly, Ming Xue, Fanglin Yang","doi":"10.1175/bams-d-24-0001.1","DOIUrl":"https://doi.org/10.1175/bams-d-24-0001.1","url":null,"abstract":"\"Envisioning the Future of Community Physics\" published on 31 Jan 2024 by American Meteorological Society.","PeriodicalId":9464,"journal":{"name":"Bulletin of the American Meteorological Society","volume":null,"pages":null},"PeriodicalIF":8.0,"publicationDate":"2024-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139645119","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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