Bulletin of the American Meteorological Society最新文献_第7页

Anthropogenic Influence on 2022 June Extreme Rainfall over the Pearl River Basin 人类活动对 2022 年 6 月珠江流域极端降雨的影响

IF 8 1区地球科学

Bulletin of the American Meteorological Society Pub Date : 2024-02-26 DOI: 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

引用次数: 0

A Machine Learning Approach to Improve the Usability of Severe Thunderstorm Wind Reports 提高强雷暴大风报告可用性的机器学习方法

IF 8 1区地球科学

Bulletin of the American Meteorological Society Pub Date : 2024-02-23 DOI: 10.1175/bams-d-22-0268.1

Elizabeth Tirone, Subrata Pal, William A Gallus, Somak Dutta, Ranjan Maitra, Jennifer Newman, Eric Weber, Israel Jirak

{"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":"174 1","pages":""},"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}

引用次数: 0

Tailored investments needed to support weather, water, ice and climate services in the Polar Regions 支持极地地区天气、水、冰和气候服务所需的定制投资

IF 8 1区地球科学

Bulletin of the American Meteorological Society Pub Date : 2024-02-21 DOI: 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

{"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":"72 1","pages":""},"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}

引用次数: 0

COVID Impacts Cause Critical Gaps in the Indian Ocean Observing System COVID 影响导致印度洋观测系统出现严重缺口

IF 8 1区地球科学

Bulletin of the American Meteorological Society Pub Date : 2024-02-15 DOI: 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

{"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":"16 1","pages":""},"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}

引用次数: 0

Can we do better at teaching mathematics to undergraduate atmospheric science students? 我们能否更好地向大气科学本科生教授数学？

IF 8 1区地球科学

Bulletin of the American Meteorological Society Pub Date : 2024-02-13 DOI: 10.1175/bams-d-22-0245.1

Elizabeth M. Page, Samuel S. P. Shen, Richard C. J. Somerville

引用次数: 0

The Signal-to-Noise Paradox in Climate Forecasts: Revisiting our Understanding and Identifying Future Priorities 气候预测中的信噪比悖论：重新审视我们的理解并确定未来的优先事项

IF 8 1区地球科学

Bulletin of the American Meteorological Society Pub Date : 2024-02-12 DOI: 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

引用次数: 0

Record High 2022 September-Mean Temperature in Western North America 北美西部 2022 年 9 月平均气温创历史新高

IF 8 1区地球科学

Bulletin of the American Meteorological Society Pub Date : 2024-02-05 DOI: 10.1175/bams-d-23-0148.1

Jinbo Xie, Qi Tang, Jean-Christophe Golaz, Wuyin Lin

引用次数: 0

Advances in machine learning techniques can assist across a variety of stages in sea ice applications 机器学习技术的进步可为海冰应用的各个阶段提供帮助

IF 8 1区地球科学

Bulletin of the American Meteorological Society Pub Date : 2024-02-01 DOI: 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

引用次数: 0

Envisioning the Future of Community Physics 展望社区物理的未来

IF 8 1区地球科学

Bulletin of the American Meteorological Society Pub Date : 2024-01-31 DOI: 10.1175/bams-d-24-0001.1

Grant Firl, Ligia Bernardet, Lulin Xue, Dustin Swales, Laura Fowler, Courtney Peverly, Ming Xue, Fanglin Yang

引用次数: 0

Geostationary satellite-derived positioning of a tropical cyclone center using artificial intelligence algorithms over the western North Pacific 利用人工智能算法对北太平洋西部热带气旋中心进行地球静止卫星定位

IF 8 1区地球科学

Bulletin of the American Meteorological Society Pub Date : 2024-01-31 DOI: 10.1175/bams-d-23-0130.1

Chang-Hoi Ho, Donggyu Hyeon, Minhee Chang, Greg McFarquhar, Seong-Hee Won

{"title":"Geostationary satellite-derived positioning of a tropical cyclone center using artificial intelligence algorithms over the western North Pacific","authors":"Chang-Hoi Ho, Donggyu Hyeon, Minhee Chang, Greg McFarquhar, Seong-Hee Won","doi":"10.1175/bams-d-23-0130.1","DOIUrl":"https://doi.org/10.1175/bams-d-23-0130.1","url":null,"abstract":"Abstract Artificial intelligence (AI) models were developed to determine the center of tropical cyclones (TCs) in the western North Pacific. These models integrated information from six channels of geostationary satellite imagery: the brightness temperature of four infrared (IR) and one shortwave IR channels, as well as the reflectivity of one visible channel. The first model is a convolutional neural network designed for spatial data processing, and the second is a convolutional long short-term memory model that effectively captures spatiotemporal information. For training, verification, and testing purposes, spatial images from six channels were obtained from the Japanese Himawari-8 satellite from 2016–2021. The position of the European Center for Medium-range Weather Forecast 6- or 12-h prediction was assigned as an initial value to the AI models. Errors in the initial value were 20–50 km compared to the Joint Typhoon Warning Center best track, depending on TC intensity. Weak (strong) TCs exhibited large (small) errors. This error dependency was found in Automated Rotational Center Hurricane Eye Retrieval (ARCHER) product, which is currently used by several operational organizations. ARCHER errors were typically small when observations from both geostationary and polar orbiting satellites were included. Significant errors remained in the absence of microwave channel information from polar orbiting satellites. This study successfully developed two AI models that consistently determined the location of the TC center using only six-channel images from geostationary satellites. These models exhibited comparable or better performance than the ARCHER products. The newly developed AI models can potentially be implemented for operational use.","PeriodicalId":9464,"journal":{"name":"Bulletin of the American Meteorological Society","volume":"2 1","pages":""},"PeriodicalIF":8.0,"publicationDate":"2024-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139649612","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}

引用次数: 0