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":"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}
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}
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