36 research outputs found

    Program for integrating multizonal photographs of the Earth, taken by MKF-6 camera, in a computer

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    An algorithm and program are described, for integrating up to 6 simultaneously exposed photographs in different spectral ranges of the surface of the Earth, taken by MKF-6 cameras aboard Soyuz-22. Three of the reference marks are identified on 1 photograph and then are used to integrate the other photographs with the first. The program was compiled for the ES-1040 computer, as a standard subprogram in a system for computer processing of data of study of the Earth from space

    Interactions Between Convective Storms and Their Environment

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    The ways in which intense convective storms interact with their environment are considered for a number of specific severe storm situations. A physical model of subcloud wind fields and vertical wind profiles was developed to explain the often observed intensification of convective storms that move along or across thermal boundaries. A number of special, unusually dense, data sets were used to substantiate features of the model. GOES imagery was used in conjunction with objectively analyzed surface wind data to develop a nowcast technique that might be used to identify specific storm cells likely to become tornadic. It was shown that circulations associated with organized meso-alpha and meso-beta scale storm complexes may, on occasion, strongly modify tropospheric thermodynamic patterns and flow fields

    Perceptions and Expected Immediate Reactions to Severe Storm Displays

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    The National Weather Service has adopted warning polygons that more specifically indicate the risk area than its previous county-wide warnings. However, these polygons are not defined in terms of numerical strike probabilities (ps). To better understand people’s interpretations of warning polygons, 167 participants were shown 23 hypothetical scenarios in one of three information conditions—polygon-only (Condition A), polygon + tornadic storm cell (Condition B), and polygon + tornadic storm cell + flanking nontornadic storm cells (Condition C). Participants judged each polygon’s ps and reported the likelihood of taking nine different response actions. The polygon-only condition replicated the results of previous studies; ps was highest at the polygon’s centroid and declined in all directions from there. The two conditions displaying storm cells differed from the polygon-only condition only in having ps just as high at the polygon’s edge nearest the storm cell as at its centroid. Overall, ps values were positively correlated with expectations of continuing normal activities, seeking information from social sources, seeking shelter, and evacuating by car. These results indicate that participants make more appropriate ps judgments when polygons are presented in their natural context of radar displays than when they are presented in isolation. However, the fact that ps judgments had moderately positive correlations with both sheltering (a generally appropriate response) and evacuation (a generally inappropriate response) suggests that experiment participants experience the same ambivalence about these two protective actions as people threatened by actual tornadoes

    Observations and Laboratory Simulations of Tornadoes in Complex Topographical Regions

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    Aerial photos taken along the damage paths of the Joplin, MO, and Tuscaloosa-Birmingham, AL, tornadoes of 2011 captured and preserved several unique patterns of damage. In particular, a few distinct tree-fall patterns were noted along the Tuscaloosa-Birmingham tornado track that appeared highly influenced by the underlying topography. One such region was the focus of a damage survey and motivated laboratory vortex simulations with a 3-D foam representation of the underlying topography, in addition to simulations performed with idealized 2D topographic features, using Iowa State University\u27s tornado simulator. The purpose of this dissertation is to explore various aspects related to the interaction of a tornado or a tornado-like vortex with its underlying topography. Three topics are examined: 1) Analysis of tornado-induced tree-fall using aerial photography from the Joplin, MO, and Tuscaloosa-Birmingham, AL, tornadoes of 2011, 2) Laboratory investigation of topographical influences on a simulated tornado-like vortex, and 3) On the use of non-standard EF-scale damage indicators to categorize tornadoes

    Integration of graphical, physics-based, and machine learning methods for assessment of impact and recovery of the built environment from wind hazards

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    2019 Summer.Includes bibliographical references.The interaction between a natural hazard and a community has the potential to result in a natural disaster with substantial socio-economic losses. In order to minimize disaster impacts, researchers have been improving building codes and exploring further concepts of community resilience. Community resilience refers to a community's ability to absorb a hazard (minimize impacts) and "bounce back" afterwards (quick recovery time). Therefore, the two main components in modeling resilience are: the initial impact and subsequent recovery time. With respect to a community's building stock, this entails the building damage state sustained and how long it takes to repair and reoccupy that building. In modeling these concepts, probabilistic and physics-based methods have been the traditional approach. With advancements in artificial intelligence and machine learning, as well as data availability, it may be possible to model impact and recovery differently. Most current methods are highly constrained by their topic area, for example a damage state focuses on structural loading and resistance, while social vulnerability independently focus on certain social demographics. These models currently perform independently and are then aggregated together, but with the complex connectivity available through machine learning, structural and social characteristics may be combined simultaneously in one network model. The popularity of machine learning predictive modeling across multiple different applications has risen due to the benefit of modeling complex networks and perhaps identifying critical variables that were previously unknown, or the mechanism behind how these variables interacted within the predictive problem being modeled. The research presented herein outlines a method of using artificial neural networks to model building damage and recovery times. The incorporation of graph theory to analyze the resulting models also provides insight into the "black box" of artificial intelligence and the interaction of socio-technical parameters within the concept of community resilience. The subsequent neural network models are then verified through hindcasting the 2011 Joplin tornado for individual building damage and the time it took to repair and reoccupy each building. The results of this research show viability for using these methods to model damage, but more research work may be needed to model recovery at the same level of accuracy as damage. It is therefore recommended that artificial neural networks be primarily used for problems where the variables are well known but their interactions are not as easily understood or modeled. The graphical analysis also reveals an importance of social parameters across all points in the resilience process, while the structural components remain mostly important in determining the initial impact. Final importance factors are determined for each of the variables evaluated herein. It is suggested moving forward, that modeling approaches consider integrating how a community interacts with its infrastructure, since the human components are what make a natural hazard a disaster, and tracing artificial neural network connections may provide a starting point for such integration into current traditional modeling approaches

    The Tornado Warning Process: A Review of Current Research, Challenges, and Opportunities

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    With the unusually violent tornado season of 2011, there has been a renewed national interest, through such programs as NOAA\u27s Weather Ready Nation initiative, to reevaluate and improve our tornado warning process. This literature review provides an interdisciplinary, end-to-end examination of the tornado warning process. Following the steps outlined by the Integrated Warning System, current research in tornado prediction and detection, the warning decision process, warning dissemination, and public response are reviewed, and some of the major challenges for improving each stage are highlighted. The progress and challenges in multi-day to short-term tornado prediction are discussed, followed by an examination of tornado detection, focused primarily upon the contributions made by weather radar and storm spotters. Next is a review of the warning decision process and the challenges associated with dissemination of the warning, followed by a discussion of the complexities associated with understanding public response. Finally, several research opportunities are considered, with emphases on understanding acceptable risk, greater community and personal preparation, and personalization of the hazard risk

    Examining Student Severe Weather Behavior During a Hypothetical Tornado Scenario

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    Destructive severe weather events are an unfortunate reality of the United States’ unique geography. Each spring, the central and southeastern states are subjected to nature’s most violent localized storms, tornadoes. The 2011 tornado season received nationwide attention for two events: the April 27 outbreak was responsible for 348 fatalities from 292 confirmed tornadoes across 16 states, while the May 22 Joplin, Missouri, tornado was responsible for an additional 162 fatalities (Storm Prediction Center, 2011; Paul & Stimers, 2012). One of the most devastating single tornadoes of that outbreak was the Tuscaloosa, Alabama, tornado that claimed 64 lives along its 130-km track and ranked as an Enhanced Fujita scale category 4 (EF4). Urban sprawl in the United States exposes people to more events similar to what occurred in Tuscaloosa as developments increase in area and population size. The National Weather Service (NWS) continues to modernize its warning systems because inadequate warning for at risk populations is a major contributing factor to fatalities and death. However, there is little information available about the public’s response to these warnings in seeking adequate shelter. For this analysis, a survey was constructed to simulate a tornado event, similar to that of the April 27, 2011, tornado. Participants were provided with three levels of warning information (high, medium, and low), while only half were able to view a map of the tornado’s progress towards their location. Participants were asked to evaluate the situation by answering a series of questions at multiple intervals of the storm. The collected survey data will be used to analyze risk perception and shelter seeking behavior in relation to available warning information

    The Northern Tornadoes Project - Uncovering Canada’s True Tornado Climatology

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    Canada is a vast country with most of its population located along its southern border. Large areas are sparsely populated and/or heavily forested, and severe weather reports are rare when thunderstorms occur there. Thus, it has been difficult to accurately assess the true tornado climatology and risk. It is also important to establish a reliable baseline for tornado-related climate change studies. The Northern Tornadoes Project (NTP), led by Western University, is an ambitious multidisciplinary initiative aimed at detecting and documenting every tornado that occurs across Canada. A team of meteorologists and wind engineers collects research-quality data during each damage investigation, via thorough ground surveys and high-resolution satellite, aircraft and drone imaging. Crowdsourcing through social media is also key to tracking down events. In addition, NTP conducts research to improve our ability to detect and accurately assess tornadoes that affect forests, cropland and grassland. An open data website allows sharing of resulting data sets and analyses. Pilot investigations were carried out during the warm seasons of 2017 and 2018, with the scope expanding from the detection of any tornadoes in heavily forested regions of central Canada in 2017 to the detection of all EF1+ tornadoes in Ontario plus all significant events outside of Ontario in 2018. The 2019 season was the first full campaign, systemically collecting research-quality tornado data across the entire country. To date, the Project has found 89 tornadoes that otherwise would not have been identified, and increased the national tornado count in 2019 by 78%. © 2020 American Meteorological Societ

    Northern Tornadoes Project. Annual Report 2020

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    NORTHERN TORNADOES PROJECT: IMPACT AT A GLANCE Entered into working partnerships with University of Manitoba, York University and The Weather Network Acquired cutting-edge drone technology, allowing us to obtain high-quality, highly accurate damage survey data and images Obtained an advanced drone licence, allowing us to fly drones longer distances without keeping the drone in sight Conducted 409 NTP investigations, 292 Planet satellite surveys, 31 ground surveys, 24 drone surveys and 4 aircraft surveys Verified the occurrence of 77 tornadoes across Canada in 2020. NTP investigations increased the verified tornado count by 166% Created a more useful, user-friendly Dashboard and Open Data Site Published an NTP overview article in the high-impact journal Bulletin of the American Meteorological Society Began documenting the human cost of tornadoes and highlighting stories of loss and resiliency following the Angus, ON (2014) and Dunrobin, ON (2018) tornadoes Became 100% carbon-neutral through a partnership with Tree Canada Increased Twitter and Facebook follows and user engagement throughout the year, including the tornado off-season Held successful, well-attended live-casts and online classroom sessions with researchers, journalists, citizen scientists and schoolchildren Covered by 59 media outlets including CBC, Canadian Geographic, TV and radi

    Computer Modeling of Close-to-Ground Tornado Wind-Fields for Different Tornado Widths

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    Tornadoes induce different wind forces on buildings than straight-line (SL) winds. The tangential velocity (Vθ) of tornados is the main parameter that causes damage to the buildings. In-field tornado measurements cannot evaluate the tornado’s Vθ at less than 20m above ground level (AGL). The laboratory tornado simulators suggest that the Swirl ratio (S) and the radius (ro) are the most influential factors affecting Vθ. However, due to scaling problems, laboratory simulators cannot report the Vθ for elevations less than 10m AGL. Well refined computational fluid dynamics (CFD) models can evaluate the Vθ at less than 10m AGL. However, the CFD models are limited to tornado radius ro=1.0km whereas observation of actual tornados by National Weather Service (NWS) shows that significant tornados in USA have width in the range of 0.7km to 2.3km. Thus, effect of ro on the Vθ is not investigated. Therefore, the aim of this study is to investigate the maximum Vθ (Vθ,max) for different tornado radii at elevations above and below 10m AGL. Simulation results show that by increasing the ro, the S parameter producing the Vθ,max will increase accordingly. In addition, results show that by increasing ro, the Vθ,max gradually reduces with respect to reference radial velocity Vr∞. In this respect, for 0.7km≤ ro ≤2.3km the Vθ,max is in the range of 6.5Vr∞ to 3.0Vr∞. Moreover, by increasing ro, the elevation of occurrence (zmax) of the Vθ,max will increase; However for all tornado radii, the zmax is always between 21m to 64m AGL. In addition, simulations show that for ro≤1.6km the radial Vθ profiles above 10m of the ground resemble the Rankine Combined Vortex Model (RCVM) flows, whereas at less than 10m of the ground the profile has two peaks for S greater than the touchdown S. Similarly, for ro≥1.8km the radial Vθ profiles below and above z=10m have two peaks for the S greater than the touchdown S
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