Modeling and Integration of Severe Weather Advisories for Situational Awareness

2006 ◽  
Author(s):  
Renato Iannella
Keyword(s):  
Situational Awareness ◽  
Severe Weather

scholarly journals The Advantages of a Mixed-Band Radar Network for Severe Weather Operations: A Case Study of 13 May 2009

2014 ◽  
Vol 29 (1) ◽  
pp. 78-98 ◽  
Author(s):  
Vivek N. Mahale ◽  
Jerald A. Brotzge ◽  
Howard B. Bluestein
Keyword(s):  
Situational Awareness ◽  
Severe Weather ◽  
Combined System ◽  
Low Level ◽  
Dynamic Events ◽  
Temporal Sampling ◽  
Radar Network ◽  
Upper Level ◽  
Using Data

Abstract Adding a mix of X- or C-band radars to the current Weather Surveillance Radar-1988 Doppler (WSR-88D) network could address several limitations of the network, including improvements to spatial gaps in low-level coverage and temporal sampling of volume scans. These limitations can result in missing critical information in highly dynamic events, such as tornadoes and severe straight-line wind episodes. To evaluate the potential value of a mixed-band radar network for severe weather operations, a case study is examined using data from X- and S-band radars. On 13 May 2009, a thunderstorm complex associated with a cold front moved southward into southwest Oklahoma. A tornado rapidly developed from an embedded supercell within the complex. The life cycle of the tornado and subsequent wind event was sampled by the experimental Collaborative Adaptive Sensing of the Atmosphere (CASA) radar testbed of four X-band radars as well as two operational WSR-88Ds. In this study, the advantages of a mixed-band radar network are demonstrated through a chronological analysis of the event. The two radar networks provided enhanced overall situational awareness. Data from the WSR-88Ds provided 1) clear-air sensitivity, 2) a broad overview of the storm complex, 3) a large maximum unambiguous range, and 4) upper-level scans up to 19.5°. Data from the CASA radars provided 1) high-temporal, 1-min updates; 2) overlapping coverage for dual-Doppler analysis; and 3) dense low-level coverage. The combined system allowed for detailed, dual- and single-Doppler observations of a wind surge, a mesocyclone contraction, and a downburst.

scholarly journals An Overview of the Performance and Operational Applications of the MRMS and FLASH Systems in Recent Significant Urban Flash Flood Events

2021 ◽  
pp. 1-29
Author(s):  
ALAN GERARD ◽  
STEVEN M. MARTINAITIS ◽  
JONATHAN J. GOURLEY ◽  
KENNETH W. HOWARD ◽  
JIAN ZHANG
Keyword(s):  
Urban Areas ◽  
Situational Awareness ◽  
Prediction Models ◽  
Flash Flood ◽  
Spatial Scales ◽  
Weather Prediction ◽  
Small Time ◽  
Severe Weather ◽  
Flood Events ◽  
The Public

AbstractThe Multi-Radar Multi-Sensor (MRMS) system is an operational, state-of-the-science hydrometeorological data analysis and nowcasting framework that combines data from multiple radar networks, satellites, surface observational systems, and numerical weather prediction models to produce a suite of real-time, decision-support products every two minutes over the contiguous United States and southern Canada. The Flooded Locations and Simulated Hydrograph (FLASH) component of the MRMS system was designed for the monitoring and prediction of flash floods across small time and spatial scales required for urban areas given their rapid hydrologic response to precipitation. Developed at the National Severe Storms Laboratory in collaboration with the Cooperative Institute for Mesoscale Meteorological Studies (CIMMS) and other research entities, the objective for MRMS and FLASH is to be the world’s most advanced system for severe weather and storm-scale hydrometeorology, leveraging the latest science and observation systems to produce the most accurate and reliable hydrometeorological and severe weather analyses. NWS forecasters, the public and the private sector utilize a variety of products from the MRMS and FLASH systems for hydrometeorological situational awareness and to provide warnings to the public and other users about potential impacts from flash flooding. This article will examine the performance of hydrometeorological products from MRMS and FLASH, and provide perspectives on how NWS forecasters use these products in the prediction of flash flood events with an emphasis on the urban environment.

scholarly journals Supercells in Environments with Atypical Hodographs

2011 ◽  
Vol 26 (6) ◽  
pp. 1075-1083 ◽  
Author(s):  
David O. Blanchard
Keyword(s):  
Situational Awareness ◽  
Warm Season ◽  
Severe Weather ◽  
North American Monsoon ◽  
Correct Interpretation ◽  
Weather Event ◽  
Northern Arizona ◽  
Upper Level ◽  
Cold Core ◽  
Severe Weather Event

Abstract An unusual severe weather event with supercell thunderstorms developed across portions of northern Arizona in the midst of the warm-season North American monsoon—a regime characteristically dominated by a subtropical upper-level high over the southwestern United States. The approach of a midlatitude, cold-core, upper-level low brought an environment of enhanced shear and increased instability supportive of supercells. This atypical system is described and how a correct interpretation of the winds and hodograph would allow a forecaster to maintain situational awareness is discussed.

scholarly journals Comparison between GOES-12 Overshooting-Top Detections, WSR-88D Radar Reflectivity, and Severe Storm Reports

2012 ◽  
Vol 27 (3) ◽  
pp. 684-699 ◽  
Author(s):  
Richard Dworak ◽  
Kristopher Bedka ◽  
Jason Brunner ◽  
Wayne Feltz
Keyword(s):  
United States ◽  
Heavy Rainfall ◽  
Situational Awareness ◽  
Warm Season ◽  
Heavy Precipitation ◽  
Severe Weather ◽  
High Temporal Resolution ◽  
Eastern United States ◽  
Severe Storm ◽  
Convective Storms

Abstract Studies have found that convective storms with overshooting-top (OT) signatures in weather satellite imagery are often associated with hazardous weather, such as heavy rainfall, tornadoes, damaging winds, and large hail. An objective satellite-based OT detection product has been developed using 11-μm infrared window (IRW) channel brightness temperatures (BTs) for the upcoming R series of the Geostationary Operational Environmental Satellite (GOES-R) Advanced Baseline Imager. In this study, this method is applied to GOES-12 IRW data and the OT detections are compared with radar data, severe storm reports, and severe weather warnings over the eastern United States. The goals of this study are to 1) improve forecaster understanding of satellite OT signatures relative to commonly available radar products, 2) assess OT detection product accuracy, and 3) evaluate the utility of an OT detection product for diagnosing hazardous convective storms. The coevolution of radar-derived products and satellite OT signatures indicates that an OT often corresponds with the highest radar echo top and reflectivity maximum aloft. Validation of OT detections relative to composite reflectivity indicates an algorithm false-alarm ratio of 16%, with OTs within the coldest IRW BT range (<200 K) being the most accurate. A significant IRW BT minimum typically present with an OT is more often associated with heavy precipitation than a region with a spatially uniform BT. Severe weather was often associated with OT detections during the warm season (April–September) and over the southern United States. The severe weather to OT relationship increased by 15% when GOES operated in rapid-scan mode, showing the importance of high temporal resolution for observing and detecting rapidly evolving cloud-top features. Comparison of the earliest OT detection associated with a severe weather report showed that 75% of the cases occur before severe weather and that 42% of collocated severe weather reports had either an OT detected before a severe weather warning or no warning issued at all. The relationships between satellite OT signatures, severe weather, and heavy rainfall shown in this paper suggest that 1) when an OT is detected, the particular storm is likely producing heavy rainfall and/or possibly severe weather; 2) an objective OT detection product can be used to increase situational awareness and forecaster confidence that a given storm is severe; and 3) this product may be particularly useful in regions with insufficient radar coverage.

Maintaining Situational Awareness: The Role of Visual Attention

1999 ◽  
Author(s):  
Alex Chaparro ◽  
Loren Groff ◽  
Kamala Tabor ◽  
Kathy Sifrit ◽  
Leo J. Gugerty
Keyword(s):  
Visual Attention ◽  
Situational Awareness

Emergency management decision-making during severe weather

2006 ◽  
Author(s):  
Leigh A. Baumgart ◽  
Ellen J. Bass ◽  
Brenda Philips ◽  
Kevin Kloesel
Keyword(s):  
Decision Making ◽  
Emergency Management ◽  
Severe Weather ◽  
Management Decision ◽  
Management Decision Making

Review on Fog Based Spectrum Sensing for Artificial Intelligence

2018 ◽  
pp. 66-70
Author(s):  
A. Rethina Palin ◽  
I. Jeena Jacob
Keyword(s):  
Bandwidth Allocation ◽  
Situational Awareness ◽  
Cluster Head ◽  
Mesh Network ◽  
Network Systems ◽  
Markov Logic ◽  
Wireless Mesh ◽  
Local Environments ◽  
Reactive Routing ◽  
The Hierarchical Structure

Wireless Mesh Network (MWN) could be divided into proactive routing, reactive routing and hybrid routing, which must satisfy the requirements related to scalability, reliability, flexibility, throughput, load balancing, congestion control and efficiency. DMN (Directional Mesh Network) become more adaptive to the local environments and robust to spectrum changes. The existing computing units in the mesh network systems are Fog nodes, the DMN architecture is more economic and efficient since it doesn’t require architecture- level changes from existing systems. The cluster head (CH) manages a group of nodes such that the network has the hierarchical structure for the channel access, routing and bandwidth allocation. The feature extraction and situational awareness is conducted, each Fog node sends the information regarding the current situation to the cluster head in the contextual format. A Markov logic network (MLN) based reasoning engine is utilized for the final routing table updating regarding the system uncertainty and complexity.

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