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":"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}
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":"63 1","pages":""},"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":"174 1","pages":""},"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":"26 1","pages":""},"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":"16 1","pages":""},"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":"21 1","pages":""},"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}
Pub Date : 2024-01-31DOI: 10.1175/bams-d-23-0130.1
Chang-Hoi Ho, Donggyu Hyeon, Minhee Chang, Greg McFarquhar, Seong-Hee Won
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.
{"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}
Anthropogenic influence contributed approximately 61% to the extreme high-temperature event in southern China in midsummer 2022, according to a dynamic adjustment methodology and supported by optimal fingerprinting analysis of CMIP6 models.
{"title":"Anthropogenic Contribution to the Unprecedented 2022 Midsummer Extreme High-Temperature Event in Southern China","authors":"Chenyu Cao, Xiaodan Guan, Chao Li, Zhaokui Gao, Tonghui Gu","doi":"10.1175/bams-d-23-0199.1","DOIUrl":"https://doi.org/10.1175/bams-d-23-0199.1","url":null,"abstract":"Anthropogenic influence contributed approximately 61% to the extreme high-temperature event in southern China in midsummer 2022, according to a dynamic adjustment methodology and supported by optimal fingerprinting analysis of CMIP6 models.","PeriodicalId":9464,"journal":{"name":"Bulletin of the American Meteorological Society","volume":"13 1","pages":""},"PeriodicalIF":8.0,"publicationDate":"2024-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139664845","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 : 2024-01-30DOI: 10.1175/bams-d-23-0012.1
Brian J. Carroll, W. Alan Brewer, Edward Strobach, Neil Lareau, Steven S. Brown, M. Miguel Valero, Adam Kochanski, Craig B. Clements, Ralph Kahn, Katherine T. Junghenn Noyes, Amanda Makowiecki, Maxwell W. Holloway, Michael Zucker, Kathleen Clough, Jack Drucker, Kristen Zuraski, Jeff Peischl, Brandi McCarty, Richard Marchbanks, Scott Sandberg, Sunil Baidar, Yelena L. Pichugina, Robert M. Banta, Siyuan Wang, Andrew Klofas, Braeden Winters, Tyler Salas
Abstract The social, economic, and ecological impacts of wildfires are increasing over much of the U.S. and globally, partially due to changing climate and build-up of fuels from past forest management practices. This creates a need to improve coupled fire-atmosphere forecast models. However, model performance is difficult to evaluate due to scarcity of observations for many key fire-atmosphere interactions, including updrafts and plume injection height, plume entrainment processes, fire intensity and rate-of-spread, and plume chemistry. Intensive observations of such fire-atmosphere interactions during active wildfires are rare due to the logistical challenges and scales involved. The California Fire Dynamics Experiment (CalFiDE) was designed to address these observational needs, using Doppler lidars, high-resolution multispectral imaging, and in-situ air quality instruments on a NOAA Twin Otter research aircraft, and Doppler lidars, radar, and other instrumentation on multiple ground-based mobile platforms. Five wildfires were studied across northern California and southern Oregon over 16 flight days from 28 August to 25 September 2022, including a breadth of fire stages from large blow-up days to smoldering air quality observations. Missions were designed to optimize the observation of the spatial structure and temporal evolution of each fire from early afternoon until sunset during multiple consecutive days. The coordination of the mobile platforms enabled four-dimensional sampling strategies during CalFiDE that will improve understanding of fire-atmosphere dynamics, aiding in model development and prediction capability. Satellite observations contributed aerosol measurements and regional context. This article summarizes the scientific objectives, platforms and instruments deployed, coordinated sampling strategies, and presents first results.
{"title":"Measuring coupled fire-atmosphere dynamics: The California Fire Dynamics Experiment (CalFiDE)","authors":"Brian J. Carroll, W. Alan Brewer, Edward Strobach, Neil Lareau, Steven S. Brown, M. Miguel Valero, Adam Kochanski, Craig B. Clements, Ralph Kahn, Katherine T. Junghenn Noyes, Amanda Makowiecki, Maxwell W. Holloway, Michael Zucker, Kathleen Clough, Jack Drucker, Kristen Zuraski, Jeff Peischl, Brandi McCarty, Richard Marchbanks, Scott Sandberg, Sunil Baidar, Yelena L. Pichugina, Robert M. Banta, Siyuan Wang, Andrew Klofas, Braeden Winters, Tyler Salas","doi":"10.1175/bams-d-23-0012.1","DOIUrl":"https://doi.org/10.1175/bams-d-23-0012.1","url":null,"abstract":"Abstract The social, economic, and ecological impacts of wildfires are increasing over much of the U.S. and globally, partially due to changing climate and build-up of fuels from past forest management practices. This creates a need to improve coupled fire-atmosphere forecast models. However, model performance is difficult to evaluate due to scarcity of observations for many key fire-atmosphere interactions, including updrafts and plume injection height, plume entrainment processes, fire intensity and rate-of-spread, and plume chemistry. Intensive observations of such fire-atmosphere interactions during active wildfires are rare due to the logistical challenges and scales involved. The California Fire Dynamics Experiment (CalFiDE) was designed to address these observational needs, using Doppler lidars, high-resolution multispectral imaging, and in-situ air quality instruments on a NOAA Twin Otter research aircraft, and Doppler lidars, radar, and other instrumentation on multiple ground-based mobile platforms. Five wildfires were studied across northern California and southern Oregon over 16 flight days from 28 August to 25 September 2022, including a breadth of fire stages from large blow-up days to smoldering air quality observations. Missions were designed to optimize the observation of the spatial structure and temporal evolution of each fire from early afternoon until sunset during multiple consecutive days. The coordination of the mobile platforms enabled four-dimensional sampling strategies during CalFiDE that will improve understanding of fire-atmosphere dynamics, aiding in model development and prediction capability. Satellite observations contributed aerosol measurements and regional context. This article summarizes the scientific objectives, platforms and instruments deployed, coordinated sampling strategies, and presents first results.","PeriodicalId":9464,"journal":{"name":"Bulletin of the American Meteorological Society","volume":"29 1","pages":""},"PeriodicalIF":8.0,"publicationDate":"2024-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139645199","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 : 2024-01-29DOI: 10.1175/bams-d-23-0048.1
Daniel T. Lindsey, Andrew K. Heidinger, Pamela C. Sullivan, Joel McCorkel, Timothy J. Schmit, Michelle Tomlinson, Ryan Vandermeulen, Gregory J. Frost, Shobha Kondragunta, Scott Rudlosky
Abstract Geostationary Extended Observations, or GeoXO, is NOAA’s future geostationary satellite constellation, set to launch in the early 2030s and operate into the 2050s. Given changes to the Earth system, improvements in technology, and expanding needs of satellite data users, GeoXO will extend NOAA’s current observation suite by adding three new instruments and one new spacecraft. Improved versions of the imager and lightning mapper will again be placed on East and West satellites, where they will monitor severe storms, tropical cyclones, fires, and other hazards. They will be joined by an ocean color instrument designed for detection of harmful algal blooms, phytoplankton, chlorophyll-a, and other constituents. The third geostationary spacecraft will be placed in the center of the U.S. and will carry a hyperspectral infrared sounder, an atmospheric composition instrument, and potentially a partner payload. Radiances from the sounder will be assimilated into numerical weather prediction models to improve forecasts, and sounder-derived retrievals of vertical profiles of temperature and water vapor will allow forecasters to detect and track areas of enhanced instability. Retrievals of pollutants such as nitrogen dioxide and ozone from the new atmospheric composition instrument along with trace gas measurements from the sounder will be used to improve air quality monitoring, forecasts, and warnings in addition to climate monitoring. Once complete, the GeoXO constellation will contribute to an international “geo ring” of satellites that will be used for worldwide weather, oceans, climate, and air quality monitoring. This revolutionary new geostationary satellite constellation will provide critical observations for a changing Earth system.
Abstract Geostationary Extended Observations, or GeoXO, is NOAA's future geostationary satellite constellation, set to launch in early 2030s and operate into the 2050s.鉴于地球系统的变化、技术的改进以及卫星数据用户需求的不断扩大，GeoXO 将通过增加三个新仪器和一个新航天器来扩展 NOAA 目前的观测套件。改进版的成像仪和闪电绘图仪将再次安装在东西方卫星上，监测强风暴、热带气旋、火灾和其他灾害。此外，还将有一个海洋颜色仪器加入它们的行列，该仪器旨在探测有害藻类繁殖、浮游植物、叶绿素-a 和其他成分。第三个地球静止航天器将放置在美国的中心位置，将携带一个高光谱红外探测仪、一个大气成分仪器以及可能的一个伙伴有效载荷。探测仪的辐射将被同化到数值天气预报模型中，以改进预报，探测仪对温度和水汽垂直剖面的检索将使预报员能够探测和跟踪不稳定性增强的区域。除了气候监测外，新的大气成分仪器对二氧化氮和臭氧等污染物的检索以及探测仪对痕量气体的测量将用于改进空气质量监测、预报和预警。一旦完成，GeoXO 卫星星座将为国际卫星 "地球环 "做出贡献，该卫星将用于全球天气、海洋、气候和空气质量监测。这一革命性的新地球静止卫星星座将为不断变化的地球系统提供重要的观测数据。
{"title":"GeoXO: NOAA’s Future Geostationary Satellite System","authors":"Daniel T. Lindsey, Andrew K. Heidinger, Pamela C. Sullivan, Joel McCorkel, Timothy J. Schmit, Michelle Tomlinson, Ryan Vandermeulen, Gregory J. Frost, Shobha Kondragunta, Scott Rudlosky","doi":"10.1175/bams-d-23-0048.1","DOIUrl":"https://doi.org/10.1175/bams-d-23-0048.1","url":null,"abstract":"Abstract Geostationary Extended Observations, or GeoXO, is NOAA’s future geostationary satellite constellation, set to launch in the early 2030s and operate into the 2050s. Given changes to the Earth system, improvements in technology, and expanding needs of satellite data users, GeoXO will extend NOAA’s current observation suite by adding three new instruments and one new spacecraft. Improved versions of the imager and lightning mapper will again be placed on East and West satellites, where they will monitor severe storms, tropical cyclones, fires, and other hazards. They will be joined by an ocean color instrument designed for detection of harmful algal blooms, phytoplankton, chlorophyll-a, and other constituents. The third geostationary spacecraft will be placed in the center of the U.S. and will carry a hyperspectral infrared sounder, an atmospheric composition instrument, and potentially a partner payload. Radiances from the sounder will be assimilated into numerical weather prediction models to improve forecasts, and sounder-derived retrievals of vertical profiles of temperature and water vapor will allow forecasters to detect and track areas of enhanced instability. Retrievals of pollutants such as nitrogen dioxide and ozone from the new atmospheric composition instrument along with trace gas measurements from the sounder will be used to improve air quality monitoring, forecasts, and warnings in addition to climate monitoring. Once complete, the GeoXO constellation will contribute to an international “geo ring” of satellites that will be used for worldwide weather, oceans, climate, and air quality monitoring. This revolutionary new geostationary satellite constellation will provide critical observations for a changing Earth system.","PeriodicalId":9464,"journal":{"name":"Bulletin of the American Meteorological Society","volume":"170 1","pages":""},"PeriodicalIF":8.0,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139589540","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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