Pub Date : 2024-03-05DOI: 10.1175/bams-d-23-0131.1
E. Katragkou, S. P. Sobolowski, C. Teichmann, F. Solmon, V. Pavlidis, D. Rechid, P. Hoffmann, J. Fernandez, G. Nikulin, D. Jacob
Abstract CORDEX (Coordinated Regional Downscaling EXperiment) is a coordinated international activity that has produced ensembles of regional climate simulations with domains that cover all land areas of the world. These ensembles are used by a wide range of practitioners that include the scientific community, policy makers, stakeholders from the public and private sector. They also provide the scientific basis for the Intergovernmental Panel on Climate Change-Assessment Reports. As its next phase now launches, the CMIP6-CORDEX datasets are expected to populate community repositories over the next couple of years, with updated state-of-the-art regional climate data that will further support national and regional communities and inform their climate adaptation and mitigation strategies. The protocol presented here focuses on the European domain (EURO-CORDEX). It takes the international CORDEX protocol covering all fourteen global domains as its template. However, it expands on the international protocol in specific areas; Incorporates historical and projected aerosol trends into the regional models in a consistent way with CMIP6-Global Climate Models, to allow for a better comparison of global vs. regional trends; Produces more climate variables to better support sectorial climate impact assessments; Takes into account the recent scientific developments addressed in the CORDEX Flagship Pilot Studies, enabling a better assessment of processes and phenomena relevant to regional climate (e.g. land use change, aerosol, convection, urban environment). Here, we summarize the scientific analysis which led to the new simulation protocol and highlight the improvements we expect in the new generation regional climate ensemble.
{"title":"Delivering an improved framework for the new generation of CMIP6-driven EURO-CORDEX regional climate simulations","authors":"E. Katragkou, S. P. Sobolowski, C. Teichmann, F. Solmon, V. Pavlidis, D. Rechid, P. Hoffmann, J. Fernandez, G. Nikulin, D. Jacob","doi":"10.1175/bams-d-23-0131.1","DOIUrl":"https://doi.org/10.1175/bams-d-23-0131.1","url":null,"abstract":"Abstract CORDEX (Coordinated Regional Downscaling EXperiment) is a coordinated international activity that has produced ensembles of regional climate simulations with domains that cover all land areas of the world. These ensembles are used by a wide range of practitioners that include the scientific community, policy makers, stakeholders from the public and private sector. They also provide the scientific basis for the Intergovernmental Panel on Climate Change-Assessment Reports. As its next phase now launches, the CMIP6-CORDEX datasets are expected to populate community repositories over the next couple of years, with updated state-of-the-art regional climate data that will further support national and regional communities and inform their climate adaptation and mitigation strategies. The protocol presented here focuses on the European domain (EURO-CORDEX). It takes the international CORDEX protocol covering all fourteen global domains as its template. However, it expands on the international protocol in specific areas; Incorporates historical and projected aerosol trends into the regional models in a consistent way with CMIP6-Global Climate Models, to allow for a better comparison of global vs. regional trends; Produces more climate variables to better support sectorial climate impact assessments; Takes into account the recent scientific developments addressed in the CORDEX Flagship Pilot Studies, enabling a better assessment of processes and phenomena relevant to regional climate (e.g. land use change, aerosol, convection, urban environment). Here, we summarize the scientific analysis which led to the new simulation protocol and highlight the improvements we expect in the new generation regional climate ensemble.","PeriodicalId":9464,"journal":{"name":"Bulletin of the American Meteorological Society","volume":null,"pages":null},"PeriodicalIF":8.0,"publicationDate":"2024-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140036541","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 : 2024-03-01DOI: 10.1175/bams-d-22-0221.1
Xin-Zhong Liang, Drew Gower, Jennifer A. Kennedy, Melissa Kenney, Michael C. Maddox, Michael Gerst, Guillermo Balboa, Talon Becker, Ximing Cai, Roger Elmore, Wei Gao, Yufeng He, Kang Liang, Shane Lotton, Leena Malayil, Megan L. Matthews, Alison M. Meadow, Christopher M. U. Neale, Greg Newman, Amy Rebecca Sapkota, Sanghoon Shin, Jonathan Straube, Chao Sun, You Wu, Yun Yang, Xuesong Zhang
Abstract Climate change presents huge challenges to the already-complex decisions faced by U.S. agricultural producers, as seasonal weather patterns increasingly deviate from historical tendencies. Under USDA funding, a transdisciplinary team of researchers, extension experts, educators, and stakeholders is developing a climate decision support Dashboard for Agricultural Water use and Nutrient management (DAWN) to provide Corn Belt farmers with better predictive information. DAWN’s goal is to provide credible, usable information to support decisions by creating infrastructure to make subseasonal-to-seasonal forecasts accessible. DAWN uses an integrated approach to 1) engage stakeholders to coproduce a decision support and information delivery system; 2) build a coupled modeling system to represent and transfer holistic systems knowledge into effective tools; 3) produce reliable forecasts to help stakeholders optimize crop productivity and environmental quality; and 4) integrate research and extension into experiential, transdisciplinary education. This article presents DAWN’s framework for integrating climate–agriculture research, extension, and education to bridge science and service. We also present key challenges to the creation and delivery of decision support, specifically in infrastructure development, coproduction and trust building with stakeholders, product design, effective communication, and moving tools toward use.
{"title":"DAWN: Dashboard for Agricultural Water Use and Nutrient Management—A Predictive Decision Support System to Improve Crop Production in a Changing Climate","authors":"Xin-Zhong Liang, Drew Gower, Jennifer A. Kennedy, Melissa Kenney, Michael C. Maddox, Michael Gerst, Guillermo Balboa, Talon Becker, Ximing Cai, Roger Elmore, Wei Gao, Yufeng He, Kang Liang, Shane Lotton, Leena Malayil, Megan L. Matthews, Alison M. Meadow, Christopher M. U. Neale, Greg Newman, Amy Rebecca Sapkota, Sanghoon Shin, Jonathan Straube, Chao Sun, You Wu, Yun Yang, Xuesong Zhang","doi":"10.1175/bams-d-22-0221.1","DOIUrl":"https://doi.org/10.1175/bams-d-22-0221.1","url":null,"abstract":"Abstract Climate change presents huge challenges to the already-complex decisions faced by U.S. agricultural producers, as seasonal weather patterns increasingly deviate from historical tendencies. Under USDA funding, a transdisciplinary team of researchers, extension experts, educators, and stakeholders is developing a climate decision support Dashboard for Agricultural Water use and Nutrient management (DAWN) to provide Corn Belt farmers with better predictive information. DAWN’s goal is to provide credible, usable information to support decisions by creating infrastructure to make subseasonal-to-seasonal forecasts accessible. DAWN uses an integrated approach to 1) engage stakeholders to coproduce a decision support and information delivery system; 2) build a coupled modeling system to represent and transfer holistic systems knowledge into effective tools; 3) produce reliable forecasts to help stakeholders optimize crop productivity and environmental quality; and 4) integrate research and extension into experiential, transdisciplinary education. This article presents DAWN’s framework for integrating climate–agriculture research, extension, and education to bridge science and service. We also present key challenges to the creation and delivery of decision support, specifically in infrastructure development, coproduction and trust building with stakeholders, product design, effective communication, and moving tools toward use.","PeriodicalId":9464,"journal":{"name":"Bulletin of the American Meteorological Society","volume":null,"pages":null},"PeriodicalIF":8.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140019485","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 : 2024-02-29DOI: 10.1175/bams-d-23-0162.1
Zied Ben Bouallègue, Mariana C A Clare, Linus Magnusson, Estibaliz Gascón, Michael Maier-Gerber, Martin Janoušek, Mark Rodwell, Florian Pinault, Jesper S Dramsch, Simon T K Lang, Baudouin Raoult, Florence Rabier, Matthieu Chevallier, Irina Sandu, Peter Dueben, Matthew Chantry, Florian Pappenberger
Abstract Data-driven modeling based on machine learning (ML) is showing enormous potential for weather forecasting. Rapid progress has been made with impressive results for some applications. The uptake of ML methods could be a game-changer for the incremental progress in traditional numerical weather prediction (NWP) known as the “quiet revolution” of weather forecasting. The computational cost of running a forecast with standard NWP systems greatly hinders the improvements that can be made from increasing model resolution and ensemble sizes. An emerging new generation of ML models, developed using high-quality reanalysis datasets like ERA5 for training, allow forecasts that require much lower computational costs and that are highly-competitive in terms of accuracy. Here, we compare for the first time ML-generated forecasts with standard NWP-based forecasts in an operational-like context, initialized from the same initial conditions. Focusing on deterministic forecasts, we apply common forecast verification tools to assess to what extent a data-driven forecast produced with one of the recently developed ML models (PanguWeather) matches the quality and attributes of a forecast from one of the leading global NWP systems (the ECMWF IFS). The results are very promising, with comparable accuracy for both global metrics and extreme events, when verified against both the operational IFS analysis and synoptic observations. Overly smooth forecasts, increasing bias with forecast lead time, and poor performance in predicting tropical cyclone intensity are identified as current drawbacks of ML-based forecasts. A new NWP paradigm is emerging relying on inference from ML models and state-of-the-art analysis and reanalysis datasets for forecast initialization and model training.
摘要 基于机器学习（ML）的数据驱动建模在天气预报方面显示出巨大的潜力。在某些应用领域，已经取得了快速进展和令人印象深刻的成果。对于被称为天气预报 "静悄悄的革命 "的传统数值天气预报（NWP）而言，采用 ML 方法可能会改变其渐进式发展。使用标准 NWP 系统进行预报的计算成本极大地阻碍了提高模式分辨率和集合规模所能带来的改进。利用高质量再分析数据集（如ERA5）进行训练开发的新一代 ML 模式，可使预报所需的计算成本大大降低，而且在准确性方面具有很强的竞争力。在这里，我们首次将 ML 生成的预报与基于标准 NWP 的预报进行了类似业务化的比较，这些预报是在相同的初始条件下初始化的。以确定性预报为重点，我们应用常用的预报验证工具，评估使用最近开发的一种 ML 模型（盘古天气）生成的数据驱动预报在多大程度上与全球领先的 NWP 系统（ECMWF IFS）的预报质量和属性相匹配。结果很有希望，在与运行中的 IFS 分析和同步观测进行验证时，全球指标和极端事件的准确性都相当高。基于 ML 的预报目前存在的缺点是预报过于平滑、预报偏差随着预报准备时间的延长而增大以及热带气旋强度预报性能不佳。一种新的 NWP 模式正在出现，它依赖于 ML 模式的推断以及用于预报初始化和模式训练的最新分析和再分析数据集。
{"title":"The rise of data-driven weather forecasting: A first statistical assessment of machine learning-based weather forecasts in an operational-like context","authors":"Zied Ben Bouallègue, Mariana C A Clare, Linus Magnusson, Estibaliz Gascón, Michael Maier-Gerber, Martin Janoušek, Mark Rodwell, Florian Pinault, Jesper S Dramsch, Simon T K Lang, Baudouin Raoult, Florence Rabier, Matthieu Chevallier, Irina Sandu, Peter Dueben, Matthew Chantry, Florian Pappenberger","doi":"10.1175/bams-d-23-0162.1","DOIUrl":"https://doi.org/10.1175/bams-d-23-0162.1","url":null,"abstract":"Abstract Data-driven modeling based on machine learning (ML) is showing enormous potential for weather forecasting. Rapid progress has been made with impressive results for some applications. The uptake of ML methods could be a game-changer for the incremental progress in traditional numerical weather prediction (NWP) known as the “quiet revolution” of weather forecasting. The computational cost of running a forecast with standard NWP systems greatly hinders the improvements that can be made from increasing model resolution and ensemble sizes. An emerging new generation of ML models, developed using high-quality reanalysis datasets like ERA5 for training, allow forecasts that require much lower computational costs and that are highly-competitive in terms of accuracy. Here, we compare for the first time ML-generated forecasts with standard NWP-based forecasts in an operational-like context, initialized from the same initial conditions. Focusing on deterministic forecasts, we apply common forecast verification tools to assess to what extent a data-driven forecast produced with one of the recently developed ML models (PanguWeather) matches the quality and attributes of a forecast from one of the leading global NWP systems (the ECMWF IFS). The results are very promising, with comparable accuracy for both global metrics and extreme events, when verified against both the operational IFS analysis and synoptic observations. Overly smooth forecasts, increasing bias with forecast lead time, and poor performance in predicting tropical cyclone intensity are identified as current drawbacks of ML-based forecasts. A new NWP paradigm is emerging relying on inference from ML models and state-of-the-art analysis and reanalysis datasets for forecast initialization and model training.","PeriodicalId":9464,"journal":{"name":"Bulletin of the American Meteorological Society","volume":null,"pages":null},"PeriodicalIF":8.0,"publicationDate":"2024-02-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140019414","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 : 2024-02-28DOI: 10.1175/bams-d-22-0284.1
M. Timofeyeva-Livezey, Jenna Meyers, Stephen Baxter, Margaret Hurwitz, James Zdrojewski, Keith White, David Ross, Barbara Mayes Boustead, Viviane Silva, Christopher Stachelski, Audra Bruschi, Victor Murphy, Andrea Bair, David DeWitt, Richard Thoman, Fiona Horsfall, Brian Brettschneider, Elizabeth Vickery, Ray Wolf, Bill Ward
Abstract National Oceanic and Atmospheric Administration (NOAA) National Weather Service (NWS) has been providing national, regional, and local climate services for more than 20 years. The NWS climate services building blocks consist of service provision infrastructure, partnership and outreach, discovery of user needs and requirements, and service delivery at national, regional, local, and tribal levels. To improve services, the NWS climate services program accelerated user engagement through customer surveys, workshops, and collaborations. Since 2002, the annual Climate Prediction Applications Science Workshop has developed a community of climate information producers and users through sharing of climate science applications, decision support tools, and effective communication practices. Although NWS had been producing operational climate monitoring and prediction products for several decades, the Weather Research and Forecasting Innovation Act of 2017 (US Public Law 115-25, 2017) specifically mandated that NWS deliver services at subseasonal to seasonal (S2S) time scales, including periods from two weeks to two years. Looking ahead, both the Department of Commerce (DOC) and NOAA have included climate services in their new 2022-2026 strategic plans, including DOC’s goal to address the climate crisis through mitigation, adaptation, and resilience efforts and NOAA’s initiatives to build a Climate Ready Nation (CRN). The NWS Climate Services Program supports these strategic goals and CRN initiatives through integrating climate information into Impact-based Decision Support Services, the most critical element for implementation of the NWS strategy for a Weather-Ready Nation. This includes application of state-of-the-art climate monitoring and prediction products to the most societally relevant impacts while empowering regional and local climate delivery of enhanced services.
{"title":"NWS Regional and Local Climate Services: Past 20 years, Present, and Future","authors":"M. Timofeyeva-Livezey, Jenna Meyers, Stephen Baxter, Margaret Hurwitz, James Zdrojewski, Keith White, David Ross, Barbara Mayes Boustead, Viviane Silva, Christopher Stachelski, Audra Bruschi, Victor Murphy, Andrea Bair, David DeWitt, Richard Thoman, Fiona Horsfall, Brian Brettschneider, Elizabeth Vickery, Ray Wolf, Bill Ward","doi":"10.1175/bams-d-22-0284.1","DOIUrl":"https://doi.org/10.1175/bams-d-22-0284.1","url":null,"abstract":"Abstract National Oceanic and Atmospheric Administration (NOAA) National Weather Service (NWS) has been providing national, regional, and local climate services for more than 20 years. The NWS climate services building blocks consist of service provision infrastructure, partnership and outreach, discovery of user needs and requirements, and service delivery at national, regional, local, and tribal levels. To improve services, the NWS climate services program accelerated user engagement through customer surveys, workshops, and collaborations. Since 2002, the annual Climate Prediction Applications Science Workshop has developed a community of climate information producers and users through sharing of climate science applications, decision support tools, and effective communication practices. Although NWS had been producing operational climate monitoring and prediction products for several decades, the Weather Research and Forecasting Innovation Act of 2017 (US Public Law 115-25, 2017) specifically mandated that NWS deliver services at subseasonal to seasonal (S2S) time scales, including periods from two weeks to two years. Looking ahead, both the Department of Commerce (DOC) and NOAA have included climate services in their new 2022-2026 strategic plans, including DOC’s goal to address the climate crisis through mitigation, adaptation, and resilience efforts and NOAA’s initiatives to build a Climate Ready Nation (CRN). The NWS Climate Services Program supports these strategic goals and CRN initiatives through integrating climate information into Impact-based Decision Support Services, the most critical element for implementation of the NWS strategy for a Weather-Ready Nation. This includes application of state-of-the-art climate monitoring and prediction products to the most societally relevant impacts while empowering regional and local climate delivery of enhanced services.","PeriodicalId":9464,"journal":{"name":"Bulletin of the American Meteorological Society","volume":null,"pages":null},"PeriodicalIF":8.0,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140002113","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 : 2024-02-28DOI: 10.1175/bams-d-22-0116.1
Diana Bernstein
"A Path to Gender Equity in the Geosciences: Empowering Women Postdocs" published on 28 Feb 2024 by American Meteorological Society.
"地球科学领域的性别平等之路：美国气象学会于 2024 年 2 月 28 日发表了 "A Path to Gender Equity in the Geosciences: Empowering Women Postdocs "一文。
{"title":"A Path to Gender Equity in the Geosciences: Empowering Women Postdocs","authors":"Diana Bernstein","doi":"10.1175/bams-d-22-0116.1","DOIUrl":"https://doi.org/10.1175/bams-d-22-0116.1","url":null,"abstract":"\"A Path to Gender Equity in the Geosciences: Empowering Women Postdocs\" published on 28 Feb 2024 by American Meteorological Society.","PeriodicalId":9464,"journal":{"name":"Bulletin of the American Meteorological Society","volume":null,"pages":null},"PeriodicalIF":8.0,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140002103","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 : 2024-02-28DOI: 10.1175/bams-d-23-0114.1
Gabor Vali, Russell C. Schnell
Abstract An overview is given of the path of research that led from asking how hailstones originate to the discovery that ice nucleation can be initiated by bacteria and other microorganisms at temperatures as high as −2°C. The major steps along that path were finding exceptionally effective ice nucleators in soils of high content of decayed vegetative matter, then in decaying tree leaves, then in plankton-laden ocean water. Eventually, it was shown that Pseudomonas syringae bacteria were responsible for the most of the observed activity. That identification coincided with the demonstration that the same bacteria cause frost damage on plants. Ice nucleation by bacteria meant an unexpected turn in the understanding of ice nucleation and of ice formation in the atmosphere. Subsequent research confirmed the unique effectiveness of ice nucleating particles of biological origin, referred to as bio-INPs, so that bio-INPs are now considered to be important elements of lower-tropospheric cloud processes. Nonetheless, some of the questions which originally motivated the research are still unresolved, so that revisiting the early work may be helpful to current endeavors. Part 1 of this manuscript summarizes how the discovery progressed. Part 2, (Schnell and Vali, 2024; SV24) shows the relationship between bio-INPs in soils and in precipitation with climate, and other findings. The online Supplemental Material contains a bibliography of recent work about bio-INPs.
{"title":"Looking back: An account of how ice nucleation by bacteria was discovered; 1963 to about mid-1980s. Part 1. The basics","authors":"Gabor Vali, Russell C. Schnell","doi":"10.1175/bams-d-23-0114.1","DOIUrl":"https://doi.org/10.1175/bams-d-23-0114.1","url":null,"abstract":"Abstract An overview is given of the path of research that led from asking how hailstones originate to the discovery that ice nucleation can be initiated by bacteria and other microorganisms at temperatures as high as −2°C. The major steps along that path were finding exceptionally effective ice nucleators in soils of high content of decayed vegetative matter, then in decaying tree leaves, then in plankton-laden ocean water. Eventually, it was shown that Pseudomonas syringae bacteria were responsible for the most of the observed activity. That identification coincided with the demonstration that the same bacteria cause frost damage on plants. Ice nucleation by bacteria meant an unexpected turn in the understanding of ice nucleation and of ice formation in the atmosphere. Subsequent research confirmed the unique effectiveness of ice nucleating particles of biological origin, referred to as bio-INPs, so that bio-INPs are now considered to be important elements of lower-tropospheric cloud processes. Nonetheless, some of the questions which originally motivated the research are still unresolved, so that revisiting the early work may be helpful to current endeavors. Part 1 of this manuscript summarizes how the discovery progressed. Part 2, (Schnell and Vali, 2024; SV24) shows the relationship between bio-INPs in soils and in precipitation with climate, and other findings. The online Supplemental Material contains a bibliography of recent work about bio-INPs.","PeriodicalId":9464,"journal":{"name":"Bulletin of the American Meteorological Society","volume":null,"pages":null},"PeriodicalIF":8.0,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140001911","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 : 2024-02-28DOI: 10.1175/bams-d-23-0115.1
Russell C. Schnell, Gabor Vali
Abstract In Part 1 (Vali and Schnell, 2024; VS24) we described the discoveries we and our associates made in the 1960s and 1970s about biological ice nucleators (bio-INPs). Bio-INPs are far more effective than mineral INPs at temperatures above −10°C. The bio-INPs were found in decayed vegetation and in ocean water, then bacteria were identified as being the most active source for this remarkable activity. In this Part 2, we recount how, within a few years, the worldwide distribution of bio-INP sources was shown to correlate with climate zones, as was the abundance of INPs in precipitation. Oceanic sources were further studied and the presence of bio-INPs in fog diagnosed. The potential for release of bio-INPs from to the atmosphere was demonstrated. Bacterial INPs were found to play a crucial role in a plant’s frost resistance. These and other early developments of biological INPs are described. A bibliography of related recent literature is presented in the online Part 1 Supplemental Material.
{"title":"Looking back: An account of how ice nucleation by bacteria was discovered; 1963 to about mid-1980s. Part 2. Broadening the scope","authors":"Russell C. Schnell, Gabor Vali","doi":"10.1175/bams-d-23-0115.1","DOIUrl":"https://doi.org/10.1175/bams-d-23-0115.1","url":null,"abstract":"Abstract In Part 1 (Vali and Schnell, 2024; VS24) we described the discoveries we and our associates made in the 1960s and 1970s about biological ice nucleators (bio-INPs). Bio-INPs are far more effective than mineral INPs at temperatures above −10°C. The bio-INPs were found in decayed vegetation and in ocean water, then bacteria were identified as being the most active source for this remarkable activity. In this Part 2, we recount how, within a few years, the worldwide distribution of bio-INP sources was shown to correlate with climate zones, as was the abundance of INPs in precipitation. Oceanic sources were further studied and the presence of bio-INPs in fog diagnosed. The potential for release of bio-INPs from to the atmosphere was demonstrated. Bacterial INPs were found to play a crucial role in a plant’s frost resistance. These and other early developments of biological INPs are described. A bibliography of related recent literature is presented in the online Part 1 Supplemental Material.","PeriodicalId":9464,"journal":{"name":"Bulletin of the American Meteorological Society","volume":null,"pages":null},"PeriodicalIF":8.0,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140001908","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 : 2024-02-27DOI: 10.1175/bams-d-23-0034.1
Travis Griggs, James Flynn, Yuxuan Wang, Sergio Alvarez, Michael Comas, Paul Walter
Abstract Photochemical modeling outputs showing high ozone concentrations over the Gulf of Mexico and Galveston Bay during ozone episodes in the Houston-Galveston-Brazoria (HGB) region have not been previously verified using in-situ observations. Such data was collected systematically, for the first time, from July-October 2021 from three boats deployed for the Galveston Offshore Ozone Observations (GO3) and Tracking Aerosol Convection Interactions ExpeRiment - Air Quality (TRACER-AQ) field campaigns. A pontoon boat and a commercial vessel operated in Galveston Bay, while another commercial vessel operated in the Gulf of Mexico offshore of Galveston. All three boats had continuously operating sampling systems that included ozone analyzers and weather stations, and the two boats operating in Galveston Bay had a ceilometer. The sampling systems operated autonomously on the two commercial boats as they traveled their daily routes. Thirty-seven ozonesondes were launched over water on forecast high ozone days in Galveston Bay and the Gulf of Mexico. During the campaigns, multiple periods of ozone exceeding 100 ppbv were observed over water in Galveston Bay and the Gulf of Mexico. These events included previously identified conditions for high ozone events in the HGB region, such as the bay/sea breeze recirculation and post-frontal environments, as well as a localized coastal high ozone event after the passing of a tropical system (Hurricane Nicholas) that was not well forecast.
{"title":"Characterizing Over Water High Ozone Events in the Houston-Galveston-Brazoria Region During the 2021 GO3 and TRACER-AQ Campaigns","authors":"Travis Griggs, James Flynn, Yuxuan Wang, Sergio Alvarez, Michael Comas, Paul Walter","doi":"10.1175/bams-d-23-0034.1","DOIUrl":"https://doi.org/10.1175/bams-d-23-0034.1","url":null,"abstract":"Abstract Photochemical modeling outputs showing high ozone concentrations over the Gulf of Mexico and Galveston Bay during ozone episodes in the Houston-Galveston-Brazoria (HGB) region have not been previously verified using in-situ observations. Such data was collected systematically, for the first time, from July-October 2021 from three boats deployed for the Galveston Offshore Ozone Observations (GO3) and Tracking Aerosol Convection Interactions ExpeRiment - Air Quality (TRACER-AQ) field campaigns. A pontoon boat and a commercial vessel operated in Galveston Bay, while another commercial vessel operated in the Gulf of Mexico offshore of Galveston. All three boats had continuously operating sampling systems that included ozone analyzers and weather stations, and the two boats operating in Galveston Bay had a ceilometer. The sampling systems operated autonomously on the two commercial boats as they traveled their daily routes. Thirty-seven ozonesondes were launched over water on forecast high ozone days in Galveston Bay and the Gulf of Mexico. During the campaigns, multiple periods of ozone exceeding 100 ppbv were observed over water in Galveston Bay and the Gulf of Mexico. These events included previously identified conditions for high ozone events in the HGB region, such as the bay/sea breeze recirculation and post-frontal environments, as well as a localized coastal high ozone event after the passing of a tropical system (Hurricane Nicholas) that was not well forecast.","PeriodicalId":9464,"journal":{"name":"Bulletin of the American Meteorological Society","volume":null,"pages":null},"PeriodicalIF":8.0,"publicationDate":"2024-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140002009","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 : 2024-02-27DOI: 10.1175/bams-d-22-0241.1
Chris Vagasky, Ronald L. Holle, Martin J. Murphy, John A. Cramer, Ryan K. Said, Mitchell Guthrie, Jesse Hietanen
Abstract The number of cloud-to-ground (CG) flashes over the contiguous U.S. (CONUS) has been estimated to be from as small as 25 million per year to as many as 40 million. In addition, many CG flashes contact the ground in more than one place. To clarify these values, recent data from the National Lightning Detection Network (NLDN) have been examined since the network is performing well enough to make precise updates to the number of CG flashes and their associated ground contact points. The average number of CG flashes is calculated to be about 23.4 million per year over CONUS, and the average number of ground contact points is calculated as 36.8 million per year. Knowledge of these two parameters is critical to lightning protection standards, as well as better understanding of the effects of lightning on forest fire initiation, geophysical interactions, human safety, and applications that benefit from knowing that a single flash may transfer charge to ground in multiple, widely-spaced locations. Sensitivity tests to assess the effects of misclassification of CG and in-cloud (IC) lightning are also made to place bounds on these estimates; and the likely uncertainty is a few percent.
{"title":"How Much Lightning Actually Strikes the United States?","authors":"Chris Vagasky, Ronald L. Holle, Martin J. Murphy, John A. Cramer, Ryan K. Said, Mitchell Guthrie, Jesse Hietanen","doi":"10.1175/bams-d-22-0241.1","DOIUrl":"https://doi.org/10.1175/bams-d-22-0241.1","url":null,"abstract":"Abstract The number of cloud-to-ground (CG) flashes over the contiguous U.S. (CONUS) has been estimated to be from as small as 25 million per year to as many as 40 million. In addition, many CG flashes contact the ground in more than one place. To clarify these values, recent data from the National Lightning Detection Network (NLDN) have been examined since the network is performing well enough to make precise updates to the number of CG flashes and their associated ground contact points. The average number of CG flashes is calculated to be about 23.4 million per year over CONUS, and the average number of ground contact points is calculated as 36.8 million per year. Knowledge of these two parameters is critical to lightning protection standards, as well as better understanding of the effects of lightning on forest fire initiation, geophysical interactions, human safety, and applications that benefit from knowing that a single flash may transfer charge to ground in multiple, widely-spaced locations. Sensitivity tests to assess the effects of misclassification of CG and in-cloud (IC) lightning are also made to place bounds on these estimates; and the likely uncertainty is a few percent.","PeriodicalId":9464,"journal":{"name":"Bulletin of the American Meteorological Society","volume":null,"pages":null},"PeriodicalIF":8.0,"publicationDate":"2024-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139981534","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-23-0137.1
Bosi Sheng, Buwen Dong, Haolin Wang, Mingming Zhang, Shuheng Lin, Peng Si, Fraser C. Lott, Qingxiang Li
Abstract Precipitation in southern China during April–June 2022 was the highest since 1961. Anthropogenic forcing has reduced the probability of 2022-like Rx30day precipitation by about 45% based on CMIP6 simulations.
{"title":"Anthropogenic Influences on Extremely Persistent Seasonal Precipitation in Southern China during May–June 2022","authors":"Bosi Sheng, Buwen Dong, Haolin Wang, Mingming Zhang, Shuheng Lin, Peng Si, Fraser C. Lott, Qingxiang Li","doi":"10.1175/bams-d-23-0137.1","DOIUrl":"https://doi.org/10.1175/bams-d-23-0137.1","url":null,"abstract":"Abstract Precipitation in southern China during April–June 2022 was the highest since 1961. Anthropogenic forcing has reduced the probability of 2022-like Rx30day precipitation by about 45% based on CMIP6 simulations.","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":"139968966","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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