71-80 of 189 results
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Adding Tropical Cyclone Verification Capabilities to the Model Evaluation Tools – Tropical Cyclone (MET-TC) Software
PI Daniel Halperin
Producing reliable tropical cyclone (TC) genesis forecasts is an operational priority. The National Hurricane Center uses several TC genesis guidance products for their Tropical Weather Outlook. Furthermore, global model output is used in many TC genesis guidance products and is considered an important source of deterministic TC genesis forecast guidance. This project creates a standard framework for verifying deterministic and probabilistic TC genesis forecasts using the TC-Gen tool in the Model Evaluation Tools software package.
Accurately predicting tropical cyclone (TC) genesis is an important component of providing the public with the information they need to protect life and property in the event of a landfalling storm. The National Hurricane Center (NHC) issues probabilistic forecasts of TC genesis within two and five days in their Tropical Weather Outlook product. There are several guidance products available to the forecasters, many of which rely at least in part on global forecast models. It is important to understand how accurate these guidance products and the global models are at forecasting TC genesis.
This project seeks to create a standard framework for verifying TC genesis forecasts from various sources. This will allow a straightforward comparison between official forecasts, the experimental guidance products, and global model forecasts. These verification capabilities are developed within the existing Model Evaluation Tools (MET) software package from the National Center for Atmospheric Research (NCAR). As such, the user will have considerable flexibility when setting up a verification task. They will also be provided numerous output statistics for deterministic and probabilistic forecasts.
Categories: Faculty-Staff
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Demonstration of an Electrostatic Dust Shield on the Lunar Surface
PI Troy Henderson
This project will demonstrate the capability of an electrostatic dust shield, developed by NASA/KSC engineers, to remove dust from the lens of a camera after impact on the lunar surface.
This project, which is funded by NASA Kennedy Space Center, will demonstrate the capability of an electrostatic dust shield, developed by NASA/KSC engineers, to remove dust from the lens of a camera after impact on the lunar surface. Laboratory tests will confirm the experiment design, followed by a flight to the lunar surface in early 2022
Categories: Faculty-Staff
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Hazard Detection and Avoidance for Lunar Landing
PI Troy Henderson
This project develops and demonstrates algorithms for detecting and avoiding areas of large rocks and high slopes for a lunar lander
This project, funded by Intuitive Machines, develops and demonstrates algorithms for detecting and avoiding areas of large rocks and high slopes for a lunar lander. Preliminary work uses an optical camera and future work will include a lidar sensor. These algorithms will be tested in simulation, tested in laboratory experiments and demonstrated on a lunar lander flight mission.
Categories: Faculty-Staff
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Improved Image Processing for Orbit Estimation
PI Troy Henderson
This project seeks to improve orbit estimation methods using advanced image processing techniques applied to images from ground and space-based telescopes.
This project, funded by Air Force Research Laboratory, seeks to improve orbit estimation methods using advanced image processing techniques applied to images from ground and space-based telescopes. Additional work uses RF signals to estimate orbits of transmitting spacecraft.
Categories: Faculty-Staff
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Using Machine Learning to Improve Forecasting of Deep Convection
PI Christopher Hennon
CO-I Ronny Schroeder
CO-I Curtis James
CO-I Abd AlRahman AlMomani
We are working to train a neural network to forecast the initiation time, location, and intensity of thunderstorms. These results will support operations during the proposed CONVECT project and could ultimately aid operational forecasting during the North American Monsoon (NAM).
This research, funded through an NSF EAGER grant, seeks to improve forecast accuracy of monsoon thunderstorm activity and precipitation amounts in the Southwest. The project creates an innovative machine learning tool trained using regional numerical weather model output and satellite remote sensing data (the predictors) with respect to known thunderstorm cell locations and intensities detected by radar (the targets). The tool will be designed to extract important fundamental relationships between the predictors and targets that help explain the development and evolution of thunderstorms. After an intense training, validation and testing phase, the relationships will then be leveraged to generate better forecasts of the timing, severity and location of future thunderstorm events in the Southwest. The tool will be shared with the National Weather Service tto help forecasters predict thunderstorm-related hazards such as large hail, flash flooding or wildfire ignition. This innovative approach will also provide a framework for improving operational meteorological and geophysical prediction systems and for guiding scientific field studies.
The project develops a probabilistic model to predict convective initiation, rain rates, and convective cell tracks during the wet phase of the North American Monsoon (NAM). Predictors of convection (e.g., relative humidity, convective available potential energy, precipitable water) will be collected from dynamic mesoscale model (High Resolution Rapid Refresh, University of Arizona-Weather Research Forecast model) analyses and forecasts and combined with new satellite-derived observations of soil moisture and surface temperature to produce a unique prediction tool. A novel machine learning approach – causality informed learning – will be applied to identify the most suitable predictors for further training in a neural network and to gain insight into the processes governing convective initiation and evolution. Hourly forecasts of precipitation occurrence, nature, and categorical rain rates will be produced operationally to guide forecasters and field research.
Categories: Faculty-Staff
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Data-enabled Science & Engineering Education (IUSE 1626602, 2016-2020),
PI Matthew Ikle
CO-I Hong Liu
CO-I Michael Wolyniak
CO-I Raphael Isokpehi
This project will develop a virtual department across four partner campuses to provide computer science education to students at campuses that are individually too small to support this kind of department. The new department will focus on the analysis of "big data" - large sets of computational and observational data - that are becoming increasingly prevalent in STEM. Cyber-learning techniques such as recorded lectures, archived materials, blog participation, and active learning approaches will be combined to offer a set of classes in big data science spanning meteorology, environmental science, biology and chemistry. By combining students from different campuses into the same courses, problems with minimal resources and limited potential enrollments on the individual campuses can be overcome. In particular, the project will focus on developing courses in biology and earth science, areas where students are not attracted by traditional computer science classes.
The project will develop a flexible, blended learning model and effective learning assessment tools that can be implemented across multiple disciplines and institutions. The major goals and corresponding objectives of the project are to:
1) Develop and implement high quality and relevant Computational and Data-Enabled Science and Engineering (CDSE) courses in mathematical modeling, data mining, genomics and bioinformatics, and problems in atmospheric and hydrospheric science using active learning and research-based teaching methodologies that promote inter-institutional and interdisciplinary collaboration.
2) Use innovative web-based technologies, to develop and implement learning assessment tools to gauge achievement of students from diverse backgrounds and contexts.
3) Develop, implement, and test an expanded CDSE pedagogical network in which resource sharing allows institutions of all sizes and types to consistently and sustainably offer CDSE coursework.
Instructors from different campuses will be paired in a peer teaching/peer review model for course design and implementation. Including pairs of instructors from different institutions ensures that (1) each instructor will gain the knowledge and experience to teach a new course that is originally developed by the other instructor; and (2) the courses are thoroughly reviewed and revised by peers. The coalition will share its discoveries in building inter-institutional teaching efficiency, undergraduate research opportunities, and learning assessment via online networks, new coalition partners, conferences, and publications.Categories: Faculty-Staff
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The Covariant Stone-von Neumann Theorem for Actions of Abelian Groups on C*-algebras of Compact Operators
PI Lara Ismert
This is a collaborative work with Leonard Huang, Ph.D. at the University of Nevada, Reno.
In this paper, we formulate and prove a version of the Stone-von Neumann Theorem for every C*-dynamical system of the form (G,K(H),α), where G is a locally compact Hausdorff abelian group and H is a Hilbert space. The novelty of our work stems from our representation of the Weyl Commutation Relation on Hilbert K(H)-modules, instead of just Hilbert spaces, and our introduction of two additional commutation relations, which are necessary to obtain a uniqueness theorem. Along the way, we apply one of our basic results on Hilbert C*-modules to significantly shorten the length of Iain Raeburn's well-known proof of Takai-Takesaki Duality.Categories: Faculty-Staff
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CONVECT (Convective Organization aNd Venting Experiment in Complex Terrain)
PI Curtis James
CO-I Ronny Schroeder
CONVECT is a major meteorological field research project being proposed for July - August 2025 in north-central Arizona. The project is aimed at improving our understanding and ability to predict the convective development and organization of boundary layer thermals, thunderstorms, and mesoscale convective systems during the North American Monsoon (NAM).
The proposed field project is the Convective Organization aNd Venting Experiment in Complex Terrain (CONVECT), focused in north-central Arizona near the city of Prescott. This targeted region, encompassing the Black Hills, Verde and Prescott Valleys, and Mogollon Rim, provides an ideal laboratory for investigating processes connecting complex terrain to boundary-layer and convective processes. During the summer monsoon season, this region experiences frequent deep, precipitating convection. These storms typically initiate over the most prominent terrain features in this region and then may propagate into the populated lower lying areas or send out density currents or buoyancy bores that subsequently initiate new convection. The thunderstorms are generally spatially localized, forming over a deep convective boundary layer, but are often associated with pulse severe conditions (damaging wind gusts or large hail). Some cells may become terrain-locked or exhibit back-building behavior, leading to intense rainfall and flash flooding.
The multi-scale approach proposed for CONVECT will, for the first time, capture the complete physical chain of land-atmosphere processes that drive water vapor transport and monsoonal precipitation over complex terrain at meso- to micro-scales. This diurnally cycling chain includes energy and moisture exchange over a heterogeneous, sloping surface, thermally-driven planetary boundary layer (PBL) circulations, the venting of PBL air into the free troposphere, and the initiation, upscale growth, and propagation of deep convection. The proposed deployment includes a dense network of surface flux and energy balance probes, lower-tropospheric thermodynamic and kinematic profiling systems, mobile radars, and crewed and uncrewed aircraft with in-situ and remote sensors. The campaign will be carefully guided by multi-scale modeling, and in turn, experimental observations will be assimilated to evaluate their impact on multi-scale predictability and the validity of surface layer and PBL parameterizations in complex terrain. The CONVECT science team of instrument scientists and numerical modelers contains the necessary, complementary expertise in the surface layer, the boundary layer, and deep convection to substantially advance understanding of mountain exchange between the surface and free troposphere, as well as extreme precipitation, through a multi-scale lens.
Categories: Faculty-Staff
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Peer Review within a Learning Management System (LMS) in a Face-to-Face (F2F) Course
PI Wendi Kappers
The purpose of this research study is to investigate student collaboration and the effectiveness of peer review on the part of the reviewer to increase understanding of information literacy. Focus upon the Learning Management System (LMS) to support automation of peer review activities is a secondary purpose.
This research paper describes the use of peer review to improve information literacy. Peer-reviewed assignments for learning have been seen favorably within the literature. The articulated benefits range from students feeling more engaged, having expressed less anxiety, or found to be better equipped to perform in unfamiliar areas outside their current learning environments. However, minimal research examines the benefits specifically for the feedback provider (reviewer) when a more modern tool, such as the Canvas Learning Management System (LMS) is used. During the fall 2015 semester, a study was conducted to examine the peer review process from the vantage point of the reviewer when mitigated by an LMS. Since peer review is seen as a social activity, this study is guided by a social constructivism teaching framework to investigate peer review activities for (a) linear relationships to that of a perceived social element inclusion, (b) changes in learning from the perspective of the reviewer rather than the receiver of feedback, and (c) improvement in perceived information literacy. Additionally, this research examines Canvas attributes as identified by Sondergaard & Mulder(1) (2012) of (a) Automation, (b) Simplicity, (c) Customizability, and (d) Accessibility, which support statements from the literature that indicate a lack of investigation of more modern peer review tools. Survey results, both qualitative and quantitative, were analyzed across three different peer-reviewed assignments for this examination. Of the 91 respondents, representing a 32 percent response rate, descriptive analysis revealed themes ranging from Changes in Student Efforts to Valued New Perspectives; whereas, expected Active Learning and Social Benefits slightly contradicted the positive tone that was originally found in the thematic review. Overwhelming positive ratings were collected regarding the use of the LMS to support and implement a peer-reviewed assignment. Perceived affects upon the peer reviewer, and how these types of assignments can support the proposed Accreditation Board for Engineering and Technology, Inc. (ABET) General Criterion 3 Student Outcomes and General Criterion 5 Curriculum currently under revision are discussed. Lastly, these data are represented for use as an evaluation baseline for future planned investigations and for other faculty and course developers, who are considering implementation of peer-reviewed activities within first-year program courses
Categories: Faculty-Staff
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Cyber hygiene and cyber insurance current practice research
PI Wendi Kappers
CO-I Aaron Glassman
CO-I Michael Wills
Identify the market uptake and applicability of cyber hygiene models, particularly within small to medium enterprises, and relate this to current market practices in the use of cyber insurance policies and mechanisms as part of risk mitigation and management.
The cybersecurity and information risk management marketplace abounds in "top ten" lists of risks, recommended strategies and tactics, and advice; yet the uptake and successful implementation of these measures across SMB / SME (less than 250-500 person) organizations is lackluster. Cyber insurance underwriting, too, is showing strains, especially in light of 2020-2021's ransomware and related siruption attacks reacinc pandemic-seeming proportions. This research forms the first part of a process to develop, calibrate, and use models of risk avoidance, management, and acceptance behaviors.Categories: Faculty-Staff
71-80 of 189 results