Diagnosing the Conditional Probability of Tornado Damage Rating Using Environmental and Radar Attributes

2015 ◽  
Vol 30 (4) ◽  
pp. 914-932 ◽  
Author(s):  
Bryan T. Smith ◽  
Richard L. Thompson ◽  
Andrew R. Dean ◽  
Patrick T. Marsh
Keyword(s):  
Conditional Probability ◽  
Situational Awareness ◽  
Rotational Velocity ◽  
Environmental Data ◽  
Doppler Data ◽  
Awareness Tool ◽  
Tornado Event ◽  
Tornado Damage ◽  
Segment Data ◽  
Horizontal Grid

Abstract Radar-identified convective modes, peak low-level rotational velocities, and near-storm environmental data were assigned to a sample of tornadoes reported in the contiguous United States during 2009–13. The tornado segment data were filtered by the maximum enhanced Fujita (EF)-scale tornado event per hour using a 40-km horizontal grid. Convective mode was assigned to each tornado event by examining full volumetric Weather Surveillance Radar-1988 Doppler data at the beginning time of each event, and 0.5° peak rotational velocity (Vrot) data were identified manually during the life span of each tornado event. Environmental information accompanied each grid-hour event, consisting primarily of supercell-related convective parameters from the hourly objective mesoscale analyses calculated and archived at the Storm Prediction Center. Results from examining environmental and radar attributes, featuring the significant tornado parameter (STP) and 0.5° peak Vrot data, suggest an increasing conditional probability for greater EF-scale damage as both STP and 0.5° peak Vrot increase, especially with supercells. Possible applications of these findings include using the conditional probability of tornado intensity as a real-time situational awareness tool.

scholarly journals Tornado Damage Rating Probabilities Derived from WSR-88D Data

2017 ◽  
Vol 32 (4) ◽  
pp. 1509-1528 ◽  
Author(s):  
Richard L. Thompson ◽  
Bryan T. Smith ◽  
Jeremy S. Grams ◽  
Andrew R. Dean ◽  
Joseph C. Picca ◽  
...  
Keyword(s):  
United States ◽  
Great Plains ◽  
Probability Distributions ◽  
Rotational Velocity ◽  
The United States ◽  
Low Level ◽  
Convective Systems ◽  
Severe Thunderstorms ◽  
Damage Indicators ◽  
Tornado Damage

Abstract Previous work with observations from the NEXRAD (WSR-88D) network in the United States has shown that the probability of damage from a tornado, as represented by EF-scale ratings, increases as low-level rotational velocity increases. This work expands on previous studies by including reported tornadoes from 2014 to 2015, as well as a robust sample of nontornadic severe thunderstorms [≥1-in.- (2.54 cm) diameter hail, thunderstorm wind gusts ≥ 50 kt (25 m s−1), or reported wind damage] with low-level cyclonic rotation. The addition of the nontornadic sample allows the computation of tornado damage rating probabilities across a spectrum of organized severe thunderstorms represented by right-moving supercells and quasi-linear convective systems. Dual-polarization variables are used to ensure proper use of velocity data in the identification of tornadic and nontornadic cases. Tornado damage rating probabilities increase as low-level rotational velocity Vrot increases and circulation diameter decreases. The influence of height above radar level (or range from radar) is less obvious, with a muted tendency for tornado damage rating probabilities to increase as rotation (of the same Vrot magnitude) is observed closer to the ground. Consistent with previous work on gate-to-gate shear signatures such as the tornadic vortex signature, easily identifiable rotation poses a greater tornado risk compared to more nebulous areas of cyclonic azimuthal shear. Additionally, tornado probability distributions vary substantially (for similar sample sizes) when comparing the southeast United States, which has a high density of damage indicators, to the Great Plains, where damage indicators are more sparse.

scholarly journals Operationally-Relevant Cyber Situational Awareness Tool Development.

2016 ◽  
Author(s):  
Robert G. Abbott ◽  
Benjamin Robert Anderson ◽  
Kevin S. Nauer ◽  
James C. Forsythe
Keyword(s):  
Situational Awareness ◽  
Tool Development ◽  
Awareness Tool

scholarly journals The Impact on Geographic Location Accuracy due to Different Satellite Orbit Ephemerides

2009 ◽  
Vol 2009 ◽  
pp. 1-9
Author(s):  
Claudia C. Celestino ◽  
Cristina T. Sousa ◽  
Wilson Yamaguti ◽  
Helio Koiti Kuga
Keyword(s):  
Data Collection ◽  
Geographic Location ◽  
Satellite Orbit ◽  
Environmental Data ◽  
Location Accuracy ◽  
Doppler Shifts ◽  
Precise Ephemeris ◽  
Doppler Data ◽  
Important Input ◽  
The Impact

The current Brazilian System of Environmental Data Collection is composed of several satellites (SCD-1 and 2, CBERS-2 and 2B), Data Collection Platforms (DCPs) spread mostly over the Brazilian territory, and ground reception stations located in Cuiabá and Alcântara. An essential functionality offered to the users is the geographic location of these DCPs. The location is computed by the in-house developed “GEOLOC” program which processes the onboard measured Doppler shifts suffered by the signal transmitted by the DCPs. These data are relayed and stored on ground when the satellite passes over the receiving stations. Another important input data to GEOLOC are the orbit ephemeris of the satellite corresponding to the Doppler data. In this work, the impact on the geographic location accuracy when using orbit ephemeris which can be obtained through several sources is assessed. First, this evaluation is performed by computer simulation of the Doppler data, corresponding to real existing satellite passes. Then real Doppler data are used to assess the performance of the location system. The results indicate that the use of precise ephemeris can improve the performance of GEOLOC by reducing the location errors, and such conclusion can then be extended to similar location systems.

scholarly journals Inferring the Presence of Freezing Drizzle Using Archived Data from the Automated Surface Observing System (ASOS)

2020 ◽  
Vol 37 (12) ◽  
pp. 2239-2250
Author(s):  
Scott D. Landolt ◽  
Andrew Gaydos ◽  
Daniel Porter ◽  
Stephanie DiVito ◽  
Darcy Jacobson ◽  
...  
Keyword(s):  
United States ◽  
Numerical Modeling ◽  
Situational Awareness ◽  
Doppler Radar ◽  
Transportation Safety ◽  
Classification Algorithms ◽  
Current Form ◽  
Modeling Methods ◽  
Awareness Tool ◽  
Precipitation Type

AbstractIn its current form, the Automated Surface Observing System (ASOS) provides automated precipitation type reports of rain, snow, and freezing rain. Unknown precipitation can also be reported when the system recognizes precipitation is occurring but cannot classify it. A new method has been developed that can reprocess the raw ASOS 1-min-observation (OMO) data to infer the presence of freezing drizzle. This freezing drizzle derivation algorithm (FDDA) was designed to identify past freezing drizzle events that could be used for aviation product development and evaluation (e.g., Doppler radar hydrometeor classification algorithms, and improved numerical modeling methods) and impact studies that utilize archived datasets [e.g., National Transportation Safety Board (NTSB) investigations of transportation accidents in which freezing drizzle may have played a role]. Ten years of archived OMO data (2005–14) from all ASOS sites across the conterminous United States were reprocessed using the FDDA. Aviation routine weather reports (METARs) from human-augmented ASOS observations were used to evaluate and quantify the FDDA’s ability to infer freezing drizzle conditions. Advantages and drawbacks to the method are discussed. This method is not intended to be used as a real-time situational awareness tool for detecting freezing drizzle conditions at the ASOS but rather to determine periods for which freezing drizzle may have impacted transportation, with an emphasis on aviation, and to highlight the need for improved observations from the ASOS.

scholarly journals Demonstration of a GOES-R Satellite Convective Toolkit to “Bridge the Gap” between Severe Weather Watches and Warnings: An Example from the 20 May 2013 Moore, Oklahoma, Tornado Outbreak

2016 ◽  
Vol 97 (1) ◽  
pp. 69-84 ◽  
Author(s):  
Chad M. Gravelle ◽  
John R. Mecikalski ◽  
William E. Line ◽  
Kristopher M. Bedka ◽  
Ralph A. Petersen ◽  
...  
Keyword(s):  
Situational Awareness ◽  
Deep Convection ◽  
Convective Cloud ◽  
Western Hemisphere ◽  
Atmospheric Conditions ◽  
Environment Analysis ◽  
Convective Initiation ◽  
Radar Echo ◽  
Tornado Event ◽  
Total Lightning

Abstract With the launch of the Geostationary Operational Environmental Satellite–R (GOES-R) series in 2016, there will be continuity of observations for the current GOES system operating over the Western Hemisphere. The GOES-R Proving Ground was established in 2008 to help prepare satellite user communities for the enhanced capabilities of GOES-R, including new instruments, imagery, and products that will have increased spectral, spatial, and temporal resolution. This is accomplished through demonstration and evaluation of proxy products that use current GOES data, higher-resolution data provided by polar-orbiting satellites, and model-derived synthetic satellite imagery. The GOES-R demonstration products presented here, made available to forecasters in near–real time (within 20 min) via the GOES-R Proving Ground, include the 0–9-h NearCast model, 0–1-h convective initiation probabilities, convective cloud-top cooling, overshooting top detection, and a pseudo–Geostationary Lightning Mapper total lightning tendency diagnostic. These products are designed to assist in identifying areas of increasing convective instability, pre-radar echo cumulus cloud growth preceding thunderstorm formation, storm updraft intensity, and potential storm severity derived from lightning trends. In turn, they provide the warning forecaster with improved situational awareness and short-term predictive information that enhance their ability to monitor atmospheric conditions preceding and associated with the development of deep convection, a time period that typically occurs between the issuance of National Weather Service (NWS) Storm Prediction Center convective watches and convective storm warnings issued by NWS forecast offices. This paper will focus on how this GOES-R satellite convective toolkit could have been used by warning forecasters to enhance near-storm environment analysis and the warning-decision-making process prior to and during the 20 May 2013 Moore, Oklahoma, tornado event.

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