Pub Date : 2024-03-15DOI: 10.1175/bams-d-24-0077.1
Erica K. Dolinar, Jason E. Nachamkin
"Perspectives on Cloud Prediction, Post-Processing, and Verification for DoD Applications" published on 15 Mar 2024 by American Meteorological Society.
"美国气象学会于 2024 年 3 月 15 日出版的《国防部应用的云预测、后处理和验证展望》。
{"title":"Perspectives on Cloud Prediction, Post-Processing, and Verification for DoD Applications","authors":"Erica K. Dolinar, Jason E. Nachamkin","doi":"10.1175/bams-d-24-0077.1","DOIUrl":"https://doi.org/10.1175/bams-d-24-0077.1","url":null,"abstract":"\"Perspectives on Cloud Prediction, Post-Processing, and Verification for DoD Applications\" published on 15 Mar 2024 by American Meteorological Society.","PeriodicalId":9464,"journal":{"name":"Bulletin of the American Meteorological Society","volume":"87 1","pages":""},"PeriodicalIF":8.0,"publicationDate":"2024-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140153612","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 : 2024-03-13DOI: 10.1175/bams-d-23-0139.1
Ghassan J. Alaka, Jason A. Sippel, Zhan Zhang, Hyun-Sook Kim, Frank D. Marks, Vijay Tallapragada, Avichal Mehra, Xuejin Zhang, Aaron Poyer, Sundararaman G. Gopalakrishnan
Abstract The Hurricane Weather Research and Forecasting (HWRF) model was the flagship hurricane model at NOAA’s National Centers for Environmental Prediction for sixteen years and a state-of-the-art tool for tropical cyclone (TC) intensity prediction at the National Weather Service and across the globe. HWRF was a joint development between NOAA research and operations, specifically the Environmental Modeling Center and the Atlantic Oceanographic and Meteorological Laboratory. Significant support also came from the National Hurricane Center, Developmental Testbed Center, University Corporation for Atmospheric Research, universities, cooperative institutes, and the TC community. In the North Atlantic basin, where most improvement efforts focused, HWRF intensity forecast errors decreased by 45-50% at many lead times between 2007 and 2022. These large improvements resulted from increases in horizontal and vertical resolution as well as advances in model physics and data assimilation. HWRF intensity forecasts performed particularly well over the Gulf of Mexico in recent years, providing useful guidance for a large number of impactful landfalling hurricanes. Such advances were made possible not only by significant gains in computing, but also through substantial investment from the Hurricane Forecast Improvement Program.
{"title":"Lifetime Performance of the Operational Hurricane Weather Research and Forecasting (HWRF) Model for North Atlantic Tropical Cyclones","authors":"Ghassan J. Alaka, Jason A. Sippel, Zhan Zhang, Hyun-Sook Kim, Frank D. Marks, Vijay Tallapragada, Avichal Mehra, Xuejin Zhang, Aaron Poyer, Sundararaman G. Gopalakrishnan","doi":"10.1175/bams-d-23-0139.1","DOIUrl":"https://doi.org/10.1175/bams-d-23-0139.1","url":null,"abstract":"Abstract The Hurricane Weather Research and Forecasting (HWRF) model was the flagship hurricane model at NOAA’s National Centers for Environmental Prediction for sixteen years and a state-of-the-art tool for tropical cyclone (TC) intensity prediction at the National Weather Service and across the globe. HWRF was a joint development between NOAA research and operations, specifically the Environmental Modeling Center and the Atlantic Oceanographic and Meteorological Laboratory. Significant support also came from the National Hurricane Center, Developmental Testbed Center, University Corporation for Atmospheric Research, universities, cooperative institutes, and the TC community. In the North Atlantic basin, where most improvement efforts focused, HWRF intensity forecast errors decreased by 45-50% at many lead times between 2007 and 2022. These large improvements resulted from increases in horizontal and vertical resolution as well as advances in model physics and data assimilation. HWRF intensity forecasts performed particularly well over the Gulf of Mexico in recent years, providing useful guidance for a large number of impactful landfalling hurricanes. Such advances were made possible not only by significant gains in computing, but also through substantial investment from the Hurricane Forecast Improvement Program.","PeriodicalId":9464,"journal":{"name":"Bulletin of the American Meteorological Society","volume":"53 1","pages":""},"PeriodicalIF":8.0,"publicationDate":"2024-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140125444","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 : 2024-03-08DOI: 10.1175/bams-d-24-0062.1
Sergey Frolov, Kevin Garrett, Isidora Jankov, Daryl Kleist, Jebb Q. Stewart, John Ten Hoeve
"Integration of emerging data-driven models into the NOAA research to operation pipeline for numerical weather prediction" published on 08 Mar 2024 by American Meteorological Society.
{"title":"Integration of emerging data-driven models into the NOAA research to operation pipeline for numerical weather prediction","authors":"Sergey Frolov, Kevin Garrett, Isidora Jankov, Daryl Kleist, Jebb Q. Stewart, John Ten Hoeve","doi":"10.1175/bams-d-24-0062.1","DOIUrl":"https://doi.org/10.1175/bams-d-24-0062.1","url":null,"abstract":"\"Integration of emerging data-driven models into the NOAA research to operation pipeline for numerical weather prediction\" published on 08 Mar 2024 by American Meteorological Society.","PeriodicalId":9464,"journal":{"name":"Bulletin of the American Meteorological Society","volume":"276 1","pages":""},"PeriodicalIF":8.0,"publicationDate":"2024-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140070241","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 : 2024-03-07DOI: 10.1175/bams-d-24-0039.1
A.R. Siders, Dana Veron
"Advancing Climate Education through Integrated Activities to Promote Inclusion, Creativity, and Mental Health" published on 07 Mar 2024 by American Meteorological Society.
{"title":"Advancing Climate Education through Integrated Activities to Promote Inclusion, Creativity, and Mental Health","authors":"A.R. Siders, Dana Veron","doi":"10.1175/bams-d-24-0039.1","DOIUrl":"https://doi.org/10.1175/bams-d-24-0039.1","url":null,"abstract":"\"Advancing Climate Education through Integrated Activities to Promote Inclusion, Creativity, and Mental Health\" published on 07 Mar 2024 by American Meteorological Society.","PeriodicalId":9464,"journal":{"name":"Bulletin of the American Meteorological Society","volume":"26 1","pages":""},"PeriodicalIF":8.0,"publicationDate":"2024-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140054394","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}
Abstract The long-range transport of bioaerosols by dust events significantly impacts ecological and meteorological networks of the atmosphere, biosphere, and anthroposphere. Bioaerosols not only cause significant public health risks, but also act as efficient ice nuclei for inducing cloud formation and precipitation in the hydrological cycle. To establish risk management for bioaerosol impacts on the Earth system, a large-scale investigation of bioaerosols must be performed under different environmental conditions. For this purpose, a Dust–Bioaerosol (DuBi) field campaign was conducted to investigate the distribution of bioaerosols by collecting ∼950 samples at 39 sites across East Asia from 2016 to 2021. Concentrations and community structures of bioaerosols were further analyzed using fluorescence microscopic observations and high-throughput DNA sequencing, and these factors were compared to environmental factors, such as PM10 and aridity. The results indicated that microbial concentrations at dryland sites were statistically higher than those at humid sites, while the microbe-to-total-particle ratio was statistically lower in drylands than in humid regions. Microbial cells per microgram of PM10 decreased when PM10 increased. The proportion of airborne particles at each site did not vary substantially with season. The richness and diversity of airborne bacteria were significantly higher in drylands than in semiarid regions, while the community structures were stable among all sampling sites. The DuBi field campaign improves our understanding of bioaerosol characteristic variations along the dust transport pathway in East Asia and the changes of bioaerosols under the trend of climate warming, supporting the efforts to reduce public health risks.
{"title":"Large-Scale Dust–Bioaerosol Field Observations in East Asia","authors":"Zhongwei Huang, Qing Dong, Fanli Xue, Jing Qi, Xinrong Yu, Teruya Maki, Pengyue Du, Qianqing Gu, Shihan Tang, Jinsen Shi, Jianrong Bi, Tian Zhou, Jianping Huang","doi":"10.1175/bams-d-23-0108.1","DOIUrl":"https://doi.org/10.1175/bams-d-23-0108.1","url":null,"abstract":"Abstract The long-range transport of bioaerosols by dust events significantly impacts ecological and meteorological networks of the atmosphere, biosphere, and anthroposphere. Bioaerosols not only cause significant public health risks, but also act as efficient ice nuclei for inducing cloud formation and precipitation in the hydrological cycle. To establish risk management for bioaerosol impacts on the Earth system, a large-scale investigation of bioaerosols must be performed under different environmental conditions. For this purpose, a Dust–Bioaerosol (DuBi) field campaign was conducted to investigate the distribution of bioaerosols by collecting ∼950 samples at 39 sites across East Asia from 2016 to 2021. Concentrations and community structures of bioaerosols were further analyzed using fluorescence microscopic observations and high-throughput DNA sequencing, and these factors were compared to environmental factors, such as PM10 and aridity. The results indicated that microbial concentrations at dryland sites were statistically higher than those at humid sites, while the microbe-to-total-particle ratio was statistically lower in drylands than in humid regions. Microbial cells per microgram of PM10 decreased when PM10 increased. The proportion of airborne particles at each site did not vary substantially with season. The richness and diversity of airborne bacteria were significantly higher in drylands than in semiarid regions, while the community structures were stable among all sampling sites. The DuBi field campaign improves our understanding of bioaerosol characteristic variations along the dust transport pathway in East Asia and the changes of bioaerosols under the trend of climate warming, supporting the efforts to reduce public health risks.","PeriodicalId":9464,"journal":{"name":"Bulletin of the American Meteorological Society","volume":"42 1","pages":""},"PeriodicalIF":8.0,"publicationDate":"2024-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140054288","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 : 2024-03-06DOI: 10.1175/bams-d-22-0257.1
Thomas C. Pagano, Barbara Casati, Stephanie Landman, Nicholas Loveday, Robert Taggart, Elizabeth E. Ebert, Mohammadreza Khanarmuei, Tara L. Jensen, Marion Mittermaier, Helen Roberts, Steve Willington, Nigel Roberts, Mike Sowko, Gordon Strassberg, Charles Kluepfel, Timothy A. Bullock, David D. Turner, Florian Pappenberger, Neal Osborne, Chris Noble
Abstract Operational agencies face significant challenges related to the verification and evaluation of weather forecasts. These challenges were investigated in a series of online workshops and polls engaging operational personnel from six countries. Five key themes emerged: inadequate verification approaches for both existing and emerging products; incomplete and uncertain observations; difficulties in accurately capturing users' real-world experiences using simplified metrics; poor communication and understanding of forecasts and complex verification information; and institutional factors such as limited resources, evolving meteorologist roles, and concerns over reputational damage. We identify nearly fifty operationally relevant scientific questions and suggest calls to action. Addressing these needs includes designing forecast systems with verification as a central consideration, enhancing the availability of observations, and developing and adopting community software systems. Additionally, we propose the establishment of an international community comprising environmental and social science researchers, statisticians, verification practitioners, and users to provide sustained support for this collective endeavor.
{"title":"Challenges of Operational Weather Forecast Verification and Evaluation","authors":"Thomas C. Pagano, Barbara Casati, Stephanie Landman, Nicholas Loveday, Robert Taggart, Elizabeth E. Ebert, Mohammadreza Khanarmuei, Tara L. Jensen, Marion Mittermaier, Helen Roberts, Steve Willington, Nigel Roberts, Mike Sowko, Gordon Strassberg, Charles Kluepfel, Timothy A. Bullock, David D. Turner, Florian Pappenberger, Neal Osborne, Chris Noble","doi":"10.1175/bams-d-22-0257.1","DOIUrl":"https://doi.org/10.1175/bams-d-22-0257.1","url":null,"abstract":"Abstract Operational agencies face significant challenges related to the verification and evaluation of weather forecasts. These challenges were investigated in a series of online workshops and polls engaging operational personnel from six countries. Five key themes emerged: inadequate verification approaches for both existing and emerging products; incomplete and uncertain observations; difficulties in accurately capturing users' real-world experiences using simplified metrics; poor communication and understanding of forecasts and complex verification information; and institutional factors such as limited resources, evolving meteorologist roles, and concerns over reputational damage. We identify nearly fifty operationally relevant scientific questions and suggest calls to action. Addressing these needs includes designing forecast systems with verification as a central consideration, enhancing the availability of observations, and developing and adopting community software systems. Additionally, we propose the establishment of an international community comprising environmental and social science researchers, statisticians, verification practitioners, and users to provide sustained support for this collective endeavor.","PeriodicalId":9464,"journal":{"name":"Bulletin of the American Meteorological Society","volume":"293 1","pages":""},"PeriodicalIF":8.0,"publicationDate":"2024-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140057530","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 : 2024-03-05DOI: 10.1175/bams-d-23-0060.1
Lidia Cucurull, Richard A. Anthes, Sean P. F. Casey, Michael J. Mueller, Andres Vidal
Abstract Between 2014 and 2018 the National Oceanic and Atmospheric Administration conducted the NOAA Satellite Observing System Architecture (NSOSA) study to plan for the next generation of operational environmental satellites. The study generated some important questions that could be addressed by Observing System Simulation Experiments (OSSEs). This paper describes a series of OSSEs in which benefits to numerical weather prediction from existing observing systems are combined with enhancements from potential future capabilities. Assessments include the relative value of the quantity of different types of thermodynamic soundings for global numerical weather applications. We compare the relative impact of several sounding configuration scenarios for infrared (IR), microwave (MW), and radio occultation (RO) observing capabilities. The main results are: (1) increasing the revisit rate for satellite radiance soundings produces the largest benefits, but at a significant cost by requiring an increase of the number of polar orbiting satellites from two to twelve; (2) a large positive impact is found when the number of RO soundings/day is increased well beyond current values and other observations are held at current levels of performance; (3) RO can be used as a mitigation strategy for lower MW/IR sounding revisit rates, particularly in the tropics; and (4) smaller benefits result from increasing the horizontal resolution along the track of the satellites of MW/IR satellite radiances. Furthermore, disaggregating IR and MW instruments into six evenly distributed sun-synchronous orbits is slightly more beneficial than when the same instruments are combined and collocated on three separate orbits.
{"title":"Observing System Simulation Experiments (OSSEs) in Support of Next-Generation NOAA Satellite Constellation","authors":"Lidia Cucurull, Richard A. Anthes, Sean P. F. Casey, Michael J. Mueller, Andres Vidal","doi":"10.1175/bams-d-23-0060.1","DOIUrl":"https://doi.org/10.1175/bams-d-23-0060.1","url":null,"abstract":"Abstract Between 2014 and 2018 the National Oceanic and Atmospheric Administration conducted the NOAA Satellite Observing System Architecture (NSOSA) study to plan for the next generation of operational environmental satellites. The study generated some important questions that could be addressed by Observing System Simulation Experiments (OSSEs). This paper describes a series of OSSEs in which benefits to numerical weather prediction from existing observing systems are combined with enhancements from potential future capabilities. Assessments include the relative value of the quantity of different types of thermodynamic soundings for global numerical weather applications. We compare the relative impact of several sounding configuration scenarios for infrared (IR), microwave (MW), and radio occultation (RO) observing capabilities. The main results are: (1) increasing the revisit rate for satellite radiance soundings produces the largest benefits, but at a significant cost by requiring an increase of the number of polar orbiting satellites from two to twelve; (2) a large positive impact is found when the number of RO soundings/day is increased well beyond current values and other observations are held at current levels of performance; (3) RO can be used as a mitigation strategy for lower MW/IR sounding revisit rates, particularly in the tropics; and (4) smaller benefits result from increasing the horizontal resolution along the track of the satellites of MW/IR satellite radiances. Furthermore, disaggregating IR and MW instruments into six evenly distributed sun-synchronous orbits is slightly more beneficial than when the same instruments are combined and collocated on three separate orbits.","PeriodicalId":9464,"journal":{"name":"Bulletin of the American Meteorological Society","volume":"85 1","pages":""},"PeriodicalIF":8.0,"publicationDate":"2024-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140036458","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 : 2024-03-05DOI: 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":"72 1","pages":""},"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":"102 1","pages":""},"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。
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