Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=1385&sort=-approved
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This lack of knowledge puts scientists at the beginning of their careers at a severe disadvantage and reduces the overall national research productivity. The project based at the University of Wisconsin-Milwaukee and partner institutes aims to address this major issue by presenting a project-based summer school in cyberinfrastructure for diverse research domains, called CIberCATSS. The participants in the school gain comprehensive knowledge of computing and its application to state-of-the-art research questions. The participants become superbly trained members of the national STEM workforce, advancing the national welfare and ensuring future prosperity. By training students with diverse backgrounds, the project will also improve the diversity and quality of the STEM workforce.The CIberCATSS program trains a competitively-selected group of summer students in advanced computation and cyberinfrastructure in the context of specific research problems. The seven-week summer school starts with three weeks of formal instruction in computing that ranges from basic programming to machine learning. The material is delivered in person and is captured for later dissemination on the web. This instruction is followed by four weeks of a short-term computational project where students apply the gained knowledge in a research context. In doing so, the learned material is placed in a disciplinary context and gaps in knowledge of how to apply the learned material to research problems are addressed. Progress both in learning and in the execution of the project is ensured by a combination of one-on-one time with domain experts and a low student-to-teaching assistant ratio. Participants in the program develop a rock-solid foundation in computing and cyberinfrastructure and apply this knowledge in a research context during the summer workshop and in their future research careers.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "12556", "attributes": { "award_id": "2225960", "title": "RUI: Imaging and Spectra at the 2023 and 2024 Total Solar Eclipses", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Geosciences (GEO)", "PLANETARY ASTRONOMY" ], "program_reference_codes": [], "program_officials": [], "start_date": "2023-01-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 28484, "first_name": "Jay", "last_name": "Pasachoff", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 2113, "ror": "https://ror.org/04avkmd49", "name": "Williams College", "address": "", "city": "", "state": "MA", "zip": "", "country": "United States", "approved": true }, "abstract": "A thorough understanding of the solar corona remains one of the most important areas in solar research. Studies of the solar corona during solar eclipses have been a major part of the success of the Williams College astronomy department in attracting and training students. This project continues solar eclipse studies with unique, ground-based measurements in concert with space-based and ground-based radio observations from NSF-funded telescopes. Undergraduate students, including students from under-represented groups in STEM, will be trained and participating in observations of two solar eclipses.The solar eclipse observations include exquisitely high-resolution observations with a Fabry-Perot and with Lyot/Halle filters. Observations also include high-frequency (> 1 Hz) power spectra of narrow-band coronal loops to compare mechanisms of coronal heating. Spectrographic observations continue to study changes of the spectral-line ratios over the solar-activity cycle through the predicted 2024 low maximum, comparing with previous solar-minimum observations from this group. Additionally, radio observations will be make with the NSF-funded Jansky Very Large Array, to provide the best-ever mapping of active regions to pinpoint the different locations of radio and EUV eruption origins, and Owens Valley Radio Observatory and a radiotelescope in the Apple Valley. The aim is to study differences in the corona during solar minimum and solar maximum.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "12557", "attributes": { "award_id": "2219614", "title": "Collaborative Research: CISE-MSI: DP: IIS: Event Detection and Knowledge Extraction via Learning and Causality Analysis for Resilience Emergency Response", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)", "Special Projects - CNS" ], "program_reference_codes": [], "program_officials": [], "start_date": "2023-01-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 28485, "first_name": "Hoang Long", "last_name": "Nguyen", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 938, "ror": "https://ror.org/00k63dq23", "name": "Meharry Medical College", "address": "", "city": "", "state": "TN", "zip": "", "country": "United States", "approved": true }, "abstract": "This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).This project utilizes information gleaned from social media about upcoming events to inform designated authorities in a timely manner so they can prepare mitigating action plans in case of emergency. Besides the extracted events themselves, harvested information may include (but is not limited to) images, posted messages, people’s sentiments and other surrounding context which will improve relevancy and trust of the information in understanding emergency situations. Extracted events become the source for investigating and analyzing spatial-temporal influences between events and cross-domain events to derive further insights. Potential applications are real-time tracking and monitoring of events for disaster relief, and forecasting of events for mitigation plans. Project outcomes will benefit researchers in information extraction and integration with interests in graph models and transfer learning; in addition to providing practical studying materials in areas such as deep learning, spatio-temporal data causality and analysis for students about disaster resilience and progressing towards community resilience in the long term. Moreover, the work will increase research capacity and collaborations to generate new research opportunities for students from underrepresented communities to pursue advanced degrees in computer science. Social media data provides a means to identify happening events prior, during, and post disasters. It provides signals for designed authorities for reactions and mitigation planning. This research will use social media posts, machine learning, and transfer learning techniques in three thrusts: 1) Extract local and global events; 2) Embed surrounding context such as relevance and trust; 3) Analyze spatial-temporal relationship between events and cross-domain events for further insights. This project puts forth a novel approach to events analysis under the umbrella of graph neural network and transfer learning, leveraging recent advances and opportunities in deep learning. The resulting data-driven algorithms will be modelled emphasizing the socio-economic aspects of the consequences and cascading losses by allowing the system to adapt according to the community-based variables and the dynamics of the disasters. The findings will be disseminated via publications, source code, and data to reach diverse communities of researchers and students.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "12558", "attributes": { "award_id": "2218197", "title": "AGS-PRF: Physics-Constrained Machine Learning-Based Models for Climate Simulations with Data Assimilation and Uncertainty Quantification", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Geosciences (GEO)", "Postdoctoral Fellowships" ], "program_reference_codes": [], "program_officials": [], "start_date": "2023-01-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 28486, "first_name": "Mohamed Aziz", "last_name": "Bhouri", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 2114, "ror": "", "name": "Bhouri, Mohamed Aziz", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true }, "abstract": "Despite recent computational advancements, climate models still cannot explicitly resolve key physical processes like turbulence, convection, and clouds. These unresolved processes must be accounted for by parameterization schemes. The inability of these schemes to mimic reality has hindered the ability of model simulations to capture various observed phenomena, leading model biases and uncertainties. Recently, machine learning-based techniques have greatly improved these schemes. Nevertheless, the standard machine learning-based methods are built solely on idealized computational models without considering the wealth of observational data available. Thus, inherent inaccuracies of the parameterization schemes continue to undermine the performance of climate models. This project aims to improve machine-learning-based parameterization schemes and, thereby, enhance the performance of existing climate models. The proposer will incorporate observations and physical laws into machine learning techniques to make parameterization schemes more accurate and provide uncertainty estimates to capture the chaotic nature of the climate system. The proposed work will train a young postdoctoral scholar. Specifically, the proposal will utilize data from satellite observations and high-resolution simulations to improve machine learning schemes. Physics-constraints will be imposed either through the incorporation of a physical loss term or by considering specific machine learning tools such as Neural Networks with fixed output layer that strongly imposes the known physical constraint. Uncertainty quantification in machine learning schemes will be implemented using ensemble learning or Bayesian approaches such as Hamiltonian Monte Carlo sampling schemes. The knowledge gained in this project could have an impact across climate science, including the advancement of global climate models’ development and of machine learning application to Big Data in climate science, as well as the development of novel computational probabilistic methods for complex multi-scale and multi-physics real-world systems.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "12559", "attributes": { "award_id": "2222586", "title": "Modeling Tree-Thinking: Measuring Evolutionary Relatedness Understanding and Examining the Interaction of Factors that Influence Tree-Thinking", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Directorate for STEM Education (EDU)", "Postdoctoral Fellowships" ], "program_reference_codes": [], "program_officials": [], "start_date": "2023-01-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 28487, "first_name": "Tina", "last_name": "Marcroft", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 204, "ror": "", "name": "Texas State University - San Marcos", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true }, "abstract": "This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).Phylogenetic trees are common diagrams in biology that depict organisms and their evolutionary history. As evolution is considered a fundamental topic that underpins much of biology, understanding these tree diagrams is critical. The ability to read such trees is often referred to as \"tree-thinking\" in the literature. Undergraduate students often find tree-thinking very difficult. This study is designed to examine factors that influence tree-thinking such as mental rotation and understanding of evolution. At present, little is known about the ways these factors interact and the ways in which students' everyday ideas about grouping organisms affect tree-thinking. This project aims to develop assessments of student thinking. Researchers and educators can use the knowledge gained from this study to design better interventions.The goals of this study are to (A) create an evidence-based assessment measuring multiple conceptions of evolutionary relatedness, (B) model the different factors that may influence tree-thinking, and (C) explore the ways in which undergraduate students scan tree diagrams. The assessment will be created based on the findings of the primary investigator's dissertation work. Data fit will be determined with data from over 200 participants at two undergraduate institutions. Validity will be determined using expert judgement, confirmatory factor analysis, and response process testing. Reliability will be determined by examining internal consistency and test-retest reliability. Modeling will be conducted at the same institutions with the aforementioned instrument as well as other instruments measuring factors such as spatial reasoning, evolution acceptance, evolution understanding, and tree-reading and manipulation abilities. Several models will be evaluated using structural equation modeling and later compared with model evaluation metrics such as Akaike Information Criterion. The final phase of this study will be exploratory where students from the aforementioned courses are grouped using latent profile analysis. Representatives from each group will conduct tree-thinking tasks while being monitored by eye-tracking equipment. Students' scanpaths will be examined with respect to their tree-thinking abilities. This study is designed to develop assessments of student thinking that relate to evolutionary relatedness that can be used for research purposes or in educational settings that can better explain the factors that influence tree-thinking.The project responds to the STEM Education Postdoctoral Research Fellowship (STEM Ed PRF) program that aims to enhance the research knowledge, skills, and practices of recent doctorates in STEM, STEM education, education, and related disciplines to advance their preparation to engage in fundamental and applied research that advances knowledge within the field.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "12560", "attributes": { "award_id": "2149909", "title": "Collaborative Research: REU Site: The Socio-Ecological Role of Greenways in Urban Systems - An Interdisciplinary Approach", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)", "RSCH EXPER FOR UNDERGRAD SITES" ], "program_reference_codes": [], "program_officials": [], "start_date": "2023-01-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 28488, "first_name": "DeAnna", "last_name": "Beasley", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 894, "ror": "", "name": "University of Tennessee Chattanooga", "address": "", "city": "", "state": "TN", "zip": "", "country": "United States", "approved": true }, "abstract": "This project is funded from the Research Experiences for Undergraduates (REU) Sites program in the Directorate for Social, Behavioral and Economic Sciences (SBE). The REU program has both scientific and societal benefits integrating research and education. This REU Site award to University of Tennessee Chattanooga, located in Chattanooga, TN, and Southern Illinois University Edwardsville in Edwardsville, IL, will support the training of 10 students for 10 weeks for three years. Research is conducted at Chattanooga, TN, Edwardsville, IL, and Cleveland, TN. It is anticipated that a total of 30 students, primarily from schools with limited research opportunities or from an under-represented group, will be trained in the program. Students will learn how policy decisions are made and how interdisciplinary research is conducted, with many presenting the results of their work at scientific conferences and to local policymakers and community stakeholders. The research will integrate greenway networks into the broader field of urban science and improve our understanding of the environmental and human associated impacts of greenway networks in urban areas. Assessment of the program will be done through the online SALG URSSA tool. Students will be tracked after the program in order to determine their career paths.The theme for this 3-year research experience focuses on enhancing environmental resilience and sustainability by examining the interaction between human and natural systems within urban greenway networks. The research is grounded in three fundamental questions: 1) What are the human and ecological drivers of microclimate? 2) What are the human usage patterns in urban greenway networks? 3) How can empirical evidence on urban greenway dynamics inform the broader scientific community and local community stakeholders on ways to mitigate environmental impacts and social disparities in cities? Students will work in interdisciplinary teams and assess how greenways vary based on social and ecological characteristics of the greenway in each respective city. Results from each team will be combined into a larger dataset which will be used to assess greenway characteristics in varying city sizes. The findings will be shared with community leaders and stakeholders to inform them of the current impacts of their greenway system and potential opportunities for expanding the greenway network. Students will learn and apply skills related to: biodiversity; data collection, analysis, and visualization; geographic information systems (GIS); and, evidence-based policymaking. These skills will then be used to better understand the human and biological drivers of microclimate variation within greenway networks and inform policymakers as to the best ways to mitigate environmental impacts and social disparities in order to create more resilient cities.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "12561", "attributes": { "award_id": "2336852", "title": "I-Corps: A Low-cost and Non-contact Respiration Monitoring Method for COVID-19 Screening and Prognosis", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Technology, Innovation and Partnerships (TIP)", "I-Corps" ], "program_reference_codes": [], "program_officials": [], "start_date": "2023-01-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 1763, "first_name": "Sabit", "last_name": "Ekin", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 387, "ror": "https://ror.org/01g9vbr38", "name": "Oklahoma State University", "address": "", "city": "", "state": "OK", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 282, "ror": "", "name": "Texas A&M Engineering Experiment Station", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true }, "abstract": "The broader impact/commercial potential of this I-Corps project is the ability to monitor respiration using a low-cost and non-contact sensing method. Studies show that real-time health monitoring devices will reach a market value of over $65 billion by 2022. Given the high prevalence of lifestyle-associated disorders, long-term continuous monitoring of physiological parameters becomes important for many healthcare cases such as apnea and for human-computer-interaction applications. The anticipated benefits of the technology in the current COVID-19 outbreak include, but are not limited to, helping to reduce the load of current (expensive and limited) respiration monitoring medical equipment, being deployable in open-spaces and being highly desirable for the drastically increasing numbers of COVID-19 patients. Further, since respiration monitoring is a ubiquitous element of medicine, this work may also impact the entire health care community, from patients in their homes, to doctor’s offices, to large medical institutions and industries. This I-Corps project involves the technological advancement required to enable the proposed low-cost and non-contact respiration sensing method. This method represents a substantial departure from traditional approaches to wireless respiration monitoring and is poised to make significant contributions in this area. The proposed technology is timely given the critical worldwide impact of COVID-19. The proposed solution is adequately deployable in home environments (e.g. living rooms) and hospitals, etc. to remotely monitor respiration for COVID-19 screening and prognosis. The proposed approach allows very low-cost, safe, easy, continuous, and non-obtrusive gathering of respiration data — a critical input for cost-effective and proactive treatment and management of subjects with COVID-19 and other chronic respiratory conditions. This project will allow the team to better understand the unmet needs by conducting customer discoveries and interviews, develop a viable business model, and learn the desired features for developing a compelling minimum viable product.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "12562", "attributes": { "award_id": "2229100", "title": "Collaborative Research: SHINE: Observational and Theoretical Studies of the Parametric Decay Instability in the Lower Solar Atmosphere", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Geosciences (GEO)", "PLANETARY ASTRONOMY" ], "program_reference_codes": [], "program_officials": [], "start_date": "2023-01-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 28489, "first_name": "Michael", "last_name": "Hahn", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 196, "ror": "https://ror.org/00hj8s172", "name": "Columbia University", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true }, "abstract": "It is unclear how the outer layer of the Sun, the corona, is heated to millions of degrees. One potential mechanism is through the parametric decay instability (PDI). This project addresses the Solar, Heliospheric, and Interplanetary Environment (SHINE) goal of understanding the solar corona through numerical simulations and analysis of space-based and NSF-funded ground based solar observations. The project also supports outreach activities in New York City and New Mexico. Two post-doctoral researchers will be supported, along with undergraduate students at Columbia University.The project will determine whether PDI is an important process in the solar atmosphere. The team will analyze observations from the Coronal Multichannel Polarimeter, the Extreme Ultraviolet Imaging Spectrometer, the Interface Region Imaging Spectrometer, and the NSF-funded Daniel K. Inouye Solar Telescope. Numerical modeling will be used to better understand the behavior of PDI in the presence of gradients in the plasma properties of the transition region and the corona. Magnetohydrodynamic simulations will be carried out to study Alfven waves and PDI growth in the lower solar atmosphere, where the scale length of gradients is smaller than the wavelength. This is critical to understand how density fluctuations are generated very close to the Sun. Moreover, ion heating associated with acoustic waves produced by PDI will be investigated using hybrid simulations, to estimate what fraction of the Alfven wave power can be dissipated in the lower atmosphere.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "12563", "attributes": { "award_id": "2312006", "title": "CRII: CNS: Integrating Security Tasks into Multicore Real-Time Systems", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)", "CSR-Computer Systems Research" ], "program_reference_codes": [], "program_officials": [], "start_date": "2023-01-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 28490, "first_name": "Monowar", "last_name": "Hasan", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 306, "ror": "https://ror.org/05dk0ce17", "name": "Washington State University", "address": "", "city": "", "state": "WA", "zip": "", "country": "United States", "approved": true }, "abstract": "This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).Many critical systems of modern society (e.g., engine control units in automobiles, logic controllers in manufacturing and power plants, aircraft control and navigation systems, industrial control systems, sensing and perception systems in robot-aided healthcare) have \"real-time\" (i.e., strict timing and safety) requirements. Emerging Internet-of-things-specific applications (e.g., connected autonomous cars, unmanned aerial vehicles), the trending use of off-the-shelf components, and widespread Internet/network connectivity expand the possibility of security breaches in those critical systems, as revealed by recent real-world attacks. The key innovation of this research is the development of a unified framework to integrate monitoring and detection mechanisms as first-class elements within the design of real-time systems, especially those built with multicore chips. This project will (a) devise novel algorithms, scheduling models, and frameworks to integrate security into multicore platforms that are cognizant of real-time requirements, (b) build design-time tools and system-level plugins to incorporate the proposed techniques into off-the-shelf systems, and (c) develop metrics to carefully trade-off two contending requirements: timeliness and security. The ideas will be validated through experimentation and testing on two off-the-shelf platforms: a multi-terrain rover and a six-degree-of-freedom robotic arm.This research will advance the field by enabling system designers to better understand how to integrate security concerns, with a focus on revealing security trade-offs to ensure minimal (or no) perturbations on real-time properties. Techniques developed as part of this project will make safety-critical, real-time systems more secure and applicable to various domains (e.g., automobiles, avionics, drones, space rovers, power grids, manufacturing plants, medical devices, industrial control systems). The proposed research and educational plans will enhance the knowledge of the next-generation technological workforce in cyber-physical systems and cyber-security. This award supports the training of graduate and undergraduate students, the development of a new security course at Wichita State University, and the integration of research findings into educational materials. All hardware, software, and system implementations (including documentation and tutorials) will be freely available in a public repository (https://github.com/CPS2RL) for educators, scientists, industry personnel, and hobbyists to access and use.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "12564", "attributes": { "award_id": "2230482", "title": "Modeling Dynamics and Impacts of a new class of Kelvin-Helmholtz Instabilities that Drive Enhanced Turbulence and Mixing in the MLT", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Geosciences (GEO)", "AERONOMY" ], "program_reference_codes": [], "program_officials": [], "start_date": "2023-01-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 28491, "first_name": "Tyler", "last_name": "Mixa", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 2115, "ror": "", "name": "GLOBAL ATMOSPHERIC TECHNOLOGIES AND SCIENCES, INC.", "address": "", "city": "", "state": "VA", "zip": "", "country": "United States", "approved": true }, "abstract": "State-of-the-art general circulation models (GCMs) used for weather and climate prediction underestimate the amount of turbulent mixing in the middle atmosphere by up to a factor of 2, and as a result, mischaracterize the transport and global distributions of CO2 and other primary atmospheric constituents. GCMs attribute mixing to a single dynamical source that neglects newly discovered, small-scale turbulent processes thought to be widespread, and perhaps even ubiquitous, in the middle atmosphere and beyond. This project will identify these unique “tube and knot” (T&K) instability dynamics and their implications for mixing through observationally guided, high-resolution modeling studies. Sophisticated turbulence and chemical analysis capabilities will be employed to address scientific goals among a diverse range of atmospheric research communities. The resulting knowledge of T&K-driven momentum transport and deposition will aid the development of improved mixing parameterizations in GCMs and yield higher accuracy weather and climate forecasting to address a critical societal need. It will also support the education of a University of Colorado Boulder graduate student and a Utah State University undergraduate student while facilitating outreach events that promote climate science education to under-represented pre-college students in the surrounding communities.This project will identify and quantify Kelvin Helmholtz instability (KHI) T&K dynamics and implications for mixing in the MLT via high-resolution modeling, utilizing the unique capabilities of in-house models CGCAM and SAM to characterize instability dynamics extending to turbulence scales and mixing in deep domains with realistic environments. The results will improve mixing parameterization schemes in weather and climate models by addressing GCM underestimation of the eddy diffusion coefficient Kzz. The goals of this research are to identify and quantify the large-scale (mean and tidal) and GW environments that enable KHI T&K dynamics, and account for their spatial scales and intensities; to quantify the diversity of KHI T&K dynamics, and their implications for energy dissipation, mixing, and influences in the MLT via high-resolution modeling; and to employ our KHI T&K modeling to assess their enhancements of energy dissipation rates, mixing, and implied Kzz relative to those expected for GW breaking. The analysis and modeling approach addressing these research goals will employ KHI T&K observations by USU Advanced Mesospheric Temperature Mapper (AMTM) OH airglow imaging and GATS SAAMER radar and lidar profiling of winds, temperatures, and Na densities in Tierra del Fuego, Chile, and Poker Flat, Alaska, to guide representative modeling environments (e.g., GW and tidal shears, multi-scale superpositions). Informed by these observations, a wide range of KHI T&K simulations will be performed to capture the diversity of responses for varying environmental conditions and evaluate KHI T&K mixing, enabling definition of the parameters dictating a KHI Kzz for representative shear layer scales and Richardson and Reynolds numbers. This research will directly result in a better understanding of unresolved mixing dynamics in the MLT and how they impact constituent particles and energy transport to higher levels of the atmosphere.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } } ], "meta": { "pagination": { "page": 1385, "pages": 1397, "count": 13961 } } }