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Abstract
The National Weather Service (NWS) Office of Science and Technology Integration commissioned a report to assess the status of artificial intelligence (AI) and machine learning (ML) activity within the agency with a view towards identifying existing obstacles and recommending future directions. The purpose of this essay is to communicate the steps that the NWS plans to take to realize the potential benefits of AI in operations.
AI activities are growing rapidly within atmospheric sciences, and the NWS is part of this growth. However, the activity is fragmented and lacks the needed infrastructure for improved coordination of effort. Current obstacles to progress include insufficient workforce training in AI/ML, a lack of curated datasets and software that can be used for development and evaluation of these approaches, the absence of a centralized clearing house available to NWS personnel for technical expertise and consultation, limited operational compute resources, and a lack of a clear end-to-end project pathway that encompasses exploration, development, testbed/proving ground and operational implementation.
These limitations are addressable. Training materials specific to NWS interests can be developed through collaboration with existing NOAA centers. Establishing a reference library staffed with AI/ML consultants tasked with collaborating with operational units would reduce siloed efforts and enhance productivity. Establishing funding vehicles for theme-based projects with a sustainable pathway through operational implementation would help bridge the research-to-operations “valley of death.” Given the growth of AI/ML across the US Weather Enterprise and the already substantial involvement of academic and private sector entities, these developments within the NWS will be of interest to the atmospheric science field.
Abstract
The National Weather Service (NWS) Office of Science and Technology Integration commissioned a report to assess the status of artificial intelligence (AI) and machine learning (ML) activity within the agency with a view towards identifying existing obstacles and recommending future directions. The purpose of this essay is to communicate the steps that the NWS plans to take to realize the potential benefits of AI in operations.
AI activities are growing rapidly within atmospheric sciences, and the NWS is part of this growth. However, the activity is fragmented and lacks the needed infrastructure for improved coordination of effort. Current obstacles to progress include insufficient workforce training in AI/ML, a lack of curated datasets and software that can be used for development and evaluation of these approaches, the absence of a centralized clearing house available to NWS personnel for technical expertise and consultation, limited operational compute resources, and a lack of a clear end-to-end project pathway that encompasses exploration, development, testbed/proving ground and operational implementation.
These limitations are addressable. Training materials specific to NWS interests can be developed through collaboration with existing NOAA centers. Establishing a reference library staffed with AI/ML consultants tasked with collaborating with operational units would reduce siloed efforts and enhance productivity. Establishing funding vehicles for theme-based projects with a sustainable pathway through operational implementation would help bridge the research-to-operations “valley of death.” Given the growth of AI/ML across the US Weather Enterprise and the already substantial involvement of academic and private sector entities, these developments within the NWS will be of interest to the atmospheric science field.
Abstract
6th WGNE workshop on systematic errors in weather and climate models What: Scientists, ranging from early career to highly experienced, involved in the development of weather and climate models and in the diagnosis of model errors, held an international workshop to discuss the nature, causes and remedies of systematic errors across timescales and across Earth system modeling components. When: 31 Oct - 04 Nov 2022 Where: Reading, UK and online
Abstract
6th WGNE workshop on systematic errors in weather and climate models What: Scientists, ranging from early career to highly experienced, involved in the development of weather and climate models and in the diagnosis of model errors, held an international workshop to discuss the nature, causes and remedies of systematic errors across timescales and across Earth system modeling components. When: 31 Oct - 04 Nov 2022 Where: Reading, UK and online
Abstract
The planetary boundary layer (PBL) is central to the exchange of heat and moisture between Earth’s surface and the atmosphere, to the turbulent transport of aerosol and chemical pollutants affecting air quality, and to near- and long-term climate prediction. Consequently, the PBL has become a major focus of atmospheric and climate science, particularly after its designation as a “targeted observable” by the 2018 National Academies of Science, Engineering, and Medicine Earth Science Decadal Survey. Information about the height of the PBL that is global in scope allows for wide geographical analysis of connections to seasonality, to latitude, proximity to oceans, and synoptic variability. Information about the PBL height at hourly resolution allows for the analysis of diurnal cycles and PBL height growth rates, both of which are critical to the study of near-surface transport processes. This manuscript describes the release of a new global dataset of PBL height estimates retrieved from radar wind profilers (RWPs), called Global Radar Wind Profiler Planetary Boundary Layer Height (GRWP-PBLH). Hourly PBL height estimates are retrieved using an existing algorithm applied to archived signal-to-noise ratio data from a series of networks located around the globe, specifically in Australia, Europe, and Japan. Information about the source data, details of data processing, and production of PBL height estimates are discussed here along with a description of supplementary data and the available software. The GRWP-PBLH dataset is now accessible to the community for ongoing and future research.
Abstract
The planetary boundary layer (PBL) is central to the exchange of heat and moisture between Earth’s surface and the atmosphere, to the turbulent transport of aerosol and chemical pollutants affecting air quality, and to near- and long-term climate prediction. Consequently, the PBL has become a major focus of atmospheric and climate science, particularly after its designation as a “targeted observable” by the 2018 National Academies of Science, Engineering, and Medicine Earth Science Decadal Survey. Information about the height of the PBL that is global in scope allows for wide geographical analysis of connections to seasonality, to latitude, proximity to oceans, and synoptic variability. Information about the PBL height at hourly resolution allows for the analysis of diurnal cycles and PBL height growth rates, both of which are critical to the study of near-surface transport processes. This manuscript describes the release of a new global dataset of PBL height estimates retrieved from radar wind profilers (RWPs), called Global Radar Wind Profiler Planetary Boundary Layer Height (GRWP-PBLH). Hourly PBL height estimates are retrieved using an existing algorithm applied to archived signal-to-noise ratio data from a series of networks located around the globe, specifically in Australia, Europe, and Japan. Information about the source data, details of data processing, and production of PBL height estimates are discussed here along with a description of supplementary data and the available software. The GRWP-PBLH dataset is now accessible to the community for ongoing and future research.
Abstract
Windblown dust events, including dust storms and smaller blowing dust events, pose severe risks to public health and transportation safety. In the United States, the statistics of fatalities caused by dust events remains elusive. We developed a new dataset by merging dust fatality data from NOAA Storm Events Database and the Department of Transportation Fatality Analysis Reporting System (FARS). There was a total of 232 deaths from windblown dust events from 2007 to 2017. This number is much larger than that reported by the NOAA Natural Hazard Statistics, which assigns some dust fatalities to high winds and thunderstorms (∼45%) and does not include many events in FARS. Dust fatalities are most frequent over the Southwest, consistent with the spatial distribution of dust storm occurrences. Other high-risk regions include the Colorado Plateau, Columbia Plateau in Washington and Oregon, the High Plains where the disastrous “Dust Bowl” occurred, and the Corn Belt where blowing dust from croplands presents a driving hazard. All six most deadly dust wrecks (three deaths or more) involved semi trucks and five of them were caused by dust storms along Interstate 10. There exist two “hotspots” for dust fatalities: 1) the “Deadliest 10 Miles” between Phoenix and Tucson, Arizona, and 2) Lordsburg Playa in New Mexico, where active dust mitigation projects have been managed by state transportation agencies. In most years, dust events caused comparable life losses to that from other weather hazards such as hurricanes, thunderstorms, lightning, and wildfires. This work presents new evidence that dust is an underappreciated weather hazard.
Abstract
Windblown dust events, including dust storms and smaller blowing dust events, pose severe risks to public health and transportation safety. In the United States, the statistics of fatalities caused by dust events remains elusive. We developed a new dataset by merging dust fatality data from NOAA Storm Events Database and the Department of Transportation Fatality Analysis Reporting System (FARS). There was a total of 232 deaths from windblown dust events from 2007 to 2017. This number is much larger than that reported by the NOAA Natural Hazard Statistics, which assigns some dust fatalities to high winds and thunderstorms (∼45%) and does not include many events in FARS. Dust fatalities are most frequent over the Southwest, consistent with the spatial distribution of dust storm occurrences. Other high-risk regions include the Colorado Plateau, Columbia Plateau in Washington and Oregon, the High Plains where the disastrous “Dust Bowl” occurred, and the Corn Belt where blowing dust from croplands presents a driving hazard. All six most deadly dust wrecks (three deaths or more) involved semi trucks and five of them were caused by dust storms along Interstate 10. There exist two “hotspots” for dust fatalities: 1) the “Deadliest 10 Miles” between Phoenix and Tucson, Arizona, and 2) Lordsburg Playa in New Mexico, where active dust mitigation projects have been managed by state transportation agencies. In most years, dust events caused comparable life losses to that from other weather hazards such as hurricanes, thunderstorms, lightning, and wildfires. This work presents new evidence that dust is an underappreciated weather hazard.
Abstract
Projections of changes in tropical cyclone (TC) characteristics under climate change are of great interest to those affected by TCs. In a recent paper, Knutson et al. combined a large number of previous results to produce projections consisting of distributions of possible future TC frequencies, intensities, and rainfall rates. These distributions provide a great resource for users of TC information. However, to apply the distributions to impacts models may require the user to solve a number of technical challenges including modeling correlations, fitting distributions, interpolation, and converting the projections to properties at landfall. I consider the frequency and intensity changes, and implement solutions for each of these challenges using a combination of recently published research and a new methodology. This allows me to produce a dataset of TC projections that give frequency changes as a function of landfall region and intensity. Mean intensity changes can then be derived from frequency changes. The projections are presented in a format that allows them to be used in impacts models without further processing. The interpolation and landfall adjustments tend to increase the frequency changes. As a result, my projections give increasing mean frequencies of hurricane-strength landfalling TCs in four out of six global basins, with mean frequencies increasing by up to 16% for a 2°C increase in global mean surface temperature. My projections are highly uncertain, but include uncertainty estimates. They are designed to be a useful resource for anyone interested in possible future TC frequencies and intensities.
Abstract
Projections of changes in tropical cyclone (TC) characteristics under climate change are of great interest to those affected by TCs. In a recent paper, Knutson et al. combined a large number of previous results to produce projections consisting of distributions of possible future TC frequencies, intensities, and rainfall rates. These distributions provide a great resource for users of TC information. However, to apply the distributions to impacts models may require the user to solve a number of technical challenges including modeling correlations, fitting distributions, interpolation, and converting the projections to properties at landfall. I consider the frequency and intensity changes, and implement solutions for each of these challenges using a combination of recently published research and a new methodology. This allows me to produce a dataset of TC projections that give frequency changes as a function of landfall region and intensity. Mean intensity changes can then be derived from frequency changes. The projections are presented in a format that allows them to be used in impacts models without further processing. The interpolation and landfall adjustments tend to increase the frequency changes. As a result, my projections give increasing mean frequencies of hurricane-strength landfalling TCs in four out of six global basins, with mean frequencies increasing by up to 16% for a 2°C increase in global mean surface temperature. My projections are highly uncertain, but include uncertainty estimates. They are designed to be a useful resource for anyone interested in possible future TC frequencies and intensities.
Abstract
In 2021, people of Hispanic and Latinx origin made up 6% of the atmospheric and Earth sciences workforce of the United States, yet they represent 20% of the population. Motivated by this disparity in Hispanic and Latinx representation in the atmospheric and Earth science workforce, this manuscript documents the lack of representation through existing limited demographic data. The analysis presents a clear gap in participation by Hispanic and Latinx people in academic settings, with a widening gap through each education and career stage. Several factors and challenges impacting the representation disparity include the lack of funding for and collaboration with Hispanic Serving Institutions, limited opportunities due to immigration status, and limited support for international research collaborations. We highlight the need for actionable steps to address the lack of representation and provide targeted recommendations to federal funding agencies, educational institutions, faculty, and potential employers. While we wait for system cultural change from our scientific institutions, grassroots initiatives like those proudly led by the AMS Committee for Hispanic and Latinx Advancement will emerge to address the needs of the Hispanic and Latinx scientific and broader community. We briefly highlight some of those achievements. Lasting cultural change can only happen if our leaders are active allies in the creation of a more diverse, equitable, and inclusive future. Alongside our active allies we will continue to champion for change in our weather, water, and climate enterprise.
Abstract
In 2021, people of Hispanic and Latinx origin made up 6% of the atmospheric and Earth sciences workforce of the United States, yet they represent 20% of the population. Motivated by this disparity in Hispanic and Latinx representation in the atmospheric and Earth science workforce, this manuscript documents the lack of representation through existing limited demographic data. The analysis presents a clear gap in participation by Hispanic and Latinx people in academic settings, with a widening gap through each education and career stage. Several factors and challenges impacting the representation disparity include the lack of funding for and collaboration with Hispanic Serving Institutions, limited opportunities due to immigration status, and limited support for international research collaborations. We highlight the need for actionable steps to address the lack of representation and provide targeted recommendations to federal funding agencies, educational institutions, faculty, and potential employers. While we wait for system cultural change from our scientific institutions, grassroots initiatives like those proudly led by the AMS Committee for Hispanic and Latinx Advancement will emerge to address the needs of the Hispanic and Latinx scientific and broader community. We briefly highlight some of those achievements. Lasting cultural change can only happen if our leaders are active allies in the creation of a more diverse, equitable, and inclusive future. Alongside our active allies we will continue to champion for change in our weather, water, and climate enterprise.
Abstract
Weather balloon payloads are commonly used by atmospheric researchers and enthusiasts to gain insight about the upper atmosphere. Balloon payloads are often unstable during flight due to high wind speeds that are experienced in both the troposphere and lower stratosphere. High Altitude Visual Orientation Control (HAVOC) is a platform of cold gas thrusters designed to control the azimuth of high-altitude balloon payloads to counteract high wind conditions. HAVOC’s active control scheme uses valves that direct the flow of pressurized gas into two sets of nozzles that can generate torque in either a clockwise or counterclockwise direction. This counteracts the rotation induced by wind and other forces encountered during a high-altitude balloon flight. The HAVOC design is discussed including its methods of measuring and controlling balloon payload rotation. Data from preliminary flights is presented, demonstrating the system’s ability to reduce payload rotation to a user defined ±40 deg s-1 for a duration of 1 hour and 49 minutes, and to maintain a fixed payload azimuth within ±30 deg for 1 hour. In addition, we present possible uses for the HAVOC system tailored to the type of user, including atmospheric researchers, videographers, and students.
Abstract
Weather balloon payloads are commonly used by atmospheric researchers and enthusiasts to gain insight about the upper atmosphere. Balloon payloads are often unstable during flight due to high wind speeds that are experienced in both the troposphere and lower stratosphere. High Altitude Visual Orientation Control (HAVOC) is a platform of cold gas thrusters designed to control the azimuth of high-altitude balloon payloads to counteract high wind conditions. HAVOC’s active control scheme uses valves that direct the flow of pressurized gas into two sets of nozzles that can generate torque in either a clockwise or counterclockwise direction. This counteracts the rotation induced by wind and other forces encountered during a high-altitude balloon flight. The HAVOC design is discussed including its methods of measuring and controlling balloon payload rotation. Data from preliminary flights is presented, demonstrating the system’s ability to reduce payload rotation to a user defined ±40 deg s-1 for a duration of 1 hour and 49 minutes, and to maintain a fixed payload azimuth within ±30 deg for 1 hour. In addition, we present possible uses for the HAVOC system tailored to the type of user, including atmospheric researchers, videographers, and students.
Abstract
Process-oriented diagnostics (PODs) aim to provide feedback for model developers through model analysis based on physical hypotheses. However, the step from a diagnostic based on relationships among variables, even when hypothesis-driven, to specific guidance for revising model formulation or parameterizations can be substantial. The POD may provide more information than a purely performance-based metric, but a gap between POD principles and providing actionable information for specific model revisions can remain. Furthermore, in coordinating diagnostics development, there is a trade-off between freedom for the developer, aiming to capture innovation, and near-term utility to the modeling center. Best practices that allow for the former, while conforming to specifications that aid the latter, are important for community diagnostics development that leads to tangible model improvements. Promising directions to close the gap between principles and practice include the interaction of PODs with perturbed physics experiments and with more quantitative process models as well as the inclusion of personnel from modeling centers in diagnostics development groups for immediate feedback during climate model revisions. Examples are provided, along with best-practice recommendations, based on practical experience from the NOAA Model Diagnostics Task Force (MDTF). Common standards for metrics and diagnostics that have arisen from a collaboration between the MDTF and the Department of Energy’s Coordinated Model Evaluation Capability are advocated as a means of uniting community diagnostics efforts.
Abstract
Process-oriented diagnostics (PODs) aim to provide feedback for model developers through model analysis based on physical hypotheses. However, the step from a diagnostic based on relationships among variables, even when hypothesis-driven, to specific guidance for revising model formulation or parameterizations can be substantial. The POD may provide more information than a purely performance-based metric, but a gap between POD principles and providing actionable information for specific model revisions can remain. Furthermore, in coordinating diagnostics development, there is a trade-off between freedom for the developer, aiming to capture innovation, and near-term utility to the modeling center. Best practices that allow for the former, while conforming to specifications that aid the latter, are important for community diagnostics development that leads to tangible model improvements. Promising directions to close the gap between principles and practice include the interaction of PODs with perturbed physics experiments and with more quantitative process models as well as the inclusion of personnel from modeling centers in diagnostics development groups for immediate feedback during climate model revisions. Examples are provided, along with best-practice recommendations, based on practical experience from the NOAA Model Diagnostics Task Force (MDTF). Common standards for metrics and diagnostics that have arisen from a collaboration between the MDTF and the Department of Energy’s Coordinated Model Evaluation Capability are advocated as a means of uniting community diagnostics efforts.
Abstract
The goal of the Sea2Cloud project is to study the interplay between surface ocean biogeochemical and physical properties, fluxes to the atmosphere, and ultimately their impact on cloud formation under minimal direct anthropogenic influence. Here we present an interdisciplinary approach, combining atmospheric physics and chemistry with marine biogeochemistry, during a voyage between 41° and 47°S in March 2020. In parallel to ambient measurements of atmospheric composition and seawater biogeochemical properties, we describe semicontrolled experiments to characterize nascent sea spray properties and nucleation from gas-phase biogenic emissions. The experimental framework for studying the impact of the predicted evolution of ozone concentration in the Southern Hemisphere is also detailed. After describing the experimental strategy, we present the oceanic and meteorological context including provisional results on atmospheric thermodynamics, composition, and flux measurements. In situ measurements and flux studies were carried out on different biological communities by sampling surface seawater from subantarctic, subtropical, and frontal water masses. Air–Sea-Interface Tanks (ASIT) were used to quantify biogenic emissions of trace gases under realistic environmental conditions, with nucleation observed in association with biogenic seawater emissions. Sea spray continuously generated produced sea spray fluxes of 34% of organic matter by mass, of which 4% particles had fluorescent properties, and which size distribution resembled the one found in clean sectors of the Southern Ocean. The goal of Sea2Cloud is to generate realistic parameterizations of emission flux dependences of trace gases and nucleation precursors, sea spray, cloud condensation nuclei, and ice nuclei using seawater biogeochemistry, for implementation in regional atmospheric models.
Abstract
The goal of the Sea2Cloud project is to study the interplay between surface ocean biogeochemical and physical properties, fluxes to the atmosphere, and ultimately their impact on cloud formation under minimal direct anthropogenic influence. Here we present an interdisciplinary approach, combining atmospheric physics and chemistry with marine biogeochemistry, during a voyage between 41° and 47°S in March 2020. In parallel to ambient measurements of atmospheric composition and seawater biogeochemical properties, we describe semicontrolled experiments to characterize nascent sea spray properties and nucleation from gas-phase biogenic emissions. The experimental framework for studying the impact of the predicted evolution of ozone concentration in the Southern Hemisphere is also detailed. After describing the experimental strategy, we present the oceanic and meteorological context including provisional results on atmospheric thermodynamics, composition, and flux measurements. In situ measurements and flux studies were carried out on different biological communities by sampling surface seawater from subantarctic, subtropical, and frontal water masses. Air–Sea-Interface Tanks (ASIT) were used to quantify biogenic emissions of trace gases under realistic environmental conditions, with nucleation observed in association with biogenic seawater emissions. Sea spray continuously generated produced sea spray fluxes of 34% of organic matter by mass, of which 4% particles had fluorescent properties, and which size distribution resembled the one found in clean sectors of the Southern Ocean. The goal of Sea2Cloud is to generate realistic parameterizations of emission flux dependences of trace gases and nucleation precursors, sea spray, cloud condensation nuclei, and ice nuclei using seawater biogeochemistry, for implementation in regional atmospheric models.
