Analysis of lightning detection network data for selected areas in Canada

2012 ◽  
Author(s):  
Volodymyr Shostak ◽  
Olexandr Bormotov ◽  
Davide Pavanello ◽  
Wasyl Janischewskyj ◽  
Farhad Rachidi
Keyword(s):  
Network Data ◽  
Lightning Detection

scholarly journals Validation of the METEOSAT storm detection and nowcasting system Cb-TRAM with lightning network data – Europe and South Africa

2013 ◽  
Vol 6 (1) ◽  
pp. 1269-1310 ◽  
Author(s):  
T. Zinner ◽  
C. Forster ◽  
E. de Coning ◽  
H.-D. Betz
Keyword(s):  
South Africa ◽  
Thermal Emission ◽  
Climatic Conditions ◽  
Probability Of Detection ◽  
Lightning Activity ◽  
Network Data ◽  
Detection Scheme ◽  
Lightning Detection ◽  
Skill Scores ◽  
Physical Limitations

Abstract. In this manuscript, recent changes to the DLR METEOSAT thunderstorm TRacking And Monitoring algorithm (Cb-TRAM) are presented as well as a validation of Cb-TRAM against the European ground-based LIghtning NETwork data (LINET) of Nowcast GmbH and Lightning Detection Network (LDN) data of the South African Weather Service (SAWS). The validation is conducted along the well known skill scores probability of detection (POD) and false alarm ratio (FAR) on the basis of METEOSAT/SEVIRI pixels as well as on the basis of thunderstorm objects. The values obtained demonstrate the limits of Cb-TRAM in specific as well as the limits of satellite methods in general which are based on thermal emission and solar reflectivity information from thunderstorm tops. Although the climatic conditions and the occurence of thunderstorms is quite different for Europe and South Africa, the quality score values are similar. Our conclusion is that Cb-TRAM provides robust results of well-defined quality for very different climatic regimes. The POD for a thunderstorm with intense lightning is about 80% during the day. The FAR for a Cb-TRAM detected thunderstorm which is not at least close to intense lightning activity is about 50%; if the proximity to any lightning activity is evaluated the FAR is even much lower at about 15%. Pixel-based analysis shows that the detected thunderstorm object size is not indiscriminately large, but well within the physical limitations of the method. Nighttime POD and FAR are somewhat worse as the detection scheme can not use high resolution visible information. Nowcasting scores show useful values up to approximatelly 30 min.

INVESTIGATION OF RADAR AND ELECTRICAL CHARACTERISTICS OF THUNDERCLOUDS SEEDED WITH A GLACIOGENIC REAGENT IN KARNATAKA, INDIA

2021 ◽  
pp. 112-122
Author(s):  
A.A. SIN'KEVICH ◽  
◽  
B. BOE ◽  
S. PAWAR ◽  
YU. P. MIKHAILOVSKII ◽  
...  
Keyword(s):  
Network Data ◽  
Lightning Flash ◽  
Electrical Characteristics ◽  
North Caucasus ◽  
Flash Rate ◽  
Convective Clouds ◽  
The North ◽  
Lightning Detection ◽  
Karnataka State ◽  
Order Of Magnitude

Characteristics of developing convective clouds (Cu) in Karnataka state (India) during the thunderstorm formation are analyzed using weather radar and lightning detection network data. It is noted that radar characteristics of Cu which produced lightning, exceed those where lightning does not form. The study has shown that the number of negative cloud-to-ground strokes exceeds the number of positive ones by an order of magnitude. The radar characteristics of clouds in India and the North Caucasus are compared. Significant differences in lightning flash rates over the mentioned regions are registered. A low correlation is found between the supercooled volume and the flash rate of negative lightning. The paper also presents the results of studying the dynamic characteristics of four Cu seeded with a glaciogenic reagent. The thunderstorm risk is estimated for the clouds. It is shown that the seeding increases a probability of lightning events.

scholarly journals Lightning environment in the vicinity of the CN tower during major storms

2021 ◽  
Author(s):  
Norhan Mansour
Keyword(s):  
Measurement System ◽  
North American ◽  
Electromagnetic Interference ◽  
Current Measurement ◽  
Average Multiplicity ◽  
Network Data ◽  
Return Stroke ◽  
The North ◽  
Lightning Detection ◽  
Intense Storm

Based on the North American Lightning Detection Network data and the return-stroke currents recorded at the CN Tower, the lightning environment within 100 km from the CN Tower is thoroughly investigated, especially while the tower was struck with major storms in 2011 and 2005. On Aug 24, 2011, video records showed that the tower was struck with 52 flashes within about 84 minutes, pointing out to the most intense storm that has ever been observed at the tower. During this most intense storm, the tower’s current measurement system recorded 32 flashes, containing 161 return strokes, resulting in an average flash multiplicity of 5, which is 80% higher than the average multiplicity of flashes occurring in the vicinity of the tower. Since the tower is repeatedly hit by lightning and its flashes produce markedly higher number of strokes, then it definitely poses an electromagnetic interference risk to nearby sensitive installations, including those in downtown Toronto.

Experiments on Lightning Detection Network Data Assimilation

2020 ◽  
Vol 33 (2) ◽  
pp. 219-228
Author(s):  
K. G. Rubinstein ◽  
I. M. Gubenko ◽  
R. Yu. Ignatov ◽  
N. D. Tikhonenko ◽  
Yu. I. Yusupov
Keyword(s):  
Data Assimilation ◽  
Network Data ◽  
Lightning Detection

scholarly journals Lightning environment in the vicinity of the CN tower during major storms

2021 ◽  
Author(s):  
Norhan Mansour
Keyword(s):  
Measurement System ◽  
North American ◽  
Electromagnetic Interference ◽  
Current Measurement ◽  
Average Multiplicity ◽  
Network Data ◽  
Return Stroke ◽  
The North ◽  
Lightning Detection ◽  
Intense Storm

Based on the North American Lightning Detection Network data and the return-stroke currents recorded at the CN Tower, the lightning environment within 100 km from the CN Tower is thoroughly investigated, especially while the tower was struck with major storms in 2011 and 2005. On Aug 24, 2011, video records showed that the tower was struck with 52 flashes within about 84 minutes, pointing out to the most intense storm that has ever been observed at the tower. During this most intense storm, the tower’s current measurement system recorded 32 flashes, containing 161 return strokes, resulting in an average flash multiplicity of 5, which is 80% higher than the average multiplicity of flashes occurring in the vicinity of the tower. Since the tower is repeatedly hit by lightning and its flashes produce markedly higher number of strokes, then it definitely poses an electromagnetic interference risk to nearby sensitive installations, including those in downtown Toronto.

Abstract #1249: Novel Use of Physician Social Network Data to Map the Gap in the Endocrinology Workforce

2015 ◽  
Vol 21 ◽  
pp. 301
Author(s):  
Armand Krikorian ◽  
Lily Peng ◽  
Zubair Ilyas ◽  
Joumana Chaiban
Keyword(s):  
Social Network ◽  
Network Data ◽  
Social Network Data

The Multilevel p2 Model

Methodology ◽  
2006 ◽  
Vol 2 (1) ◽  
pp. 42-47 ◽  
Author(s):  
Bonne J. H. Zijlstra ◽  
Marijtje A. J. van Duijn ◽  
Tom A. B. Snijders
Keyword(s):  
Social Network ◽  
Network Data ◽  
Mcmc Algorithm ◽  
Independent Observation ◽  
Single Observation ◽  
Multiple Networks ◽  
Social Network Data ◽  
Multiple Observations ◽  
Practical Support ◽  
The Social

The p 2 model is a random effects model with covariates for the analysis of binary directed social network data coming from a single observation of a social network. Here, a multilevel variant of the p 2 model is proposed for the case of multiple observations of social networks, for example, in a sample of schools. The multilevel p 2 model defines an identical p 2 model for each independent observation of the social network, where parameters are allowed to vary across the multiple networks. The multilevel p 2 model is estimated with a Bayesian Markov Chain Monte Carlo (MCMC) algorithm that was implemented in free software for the statistical analysis of complete social network data, called StOCNET. The new model is illustrated with a study on the received practical support by Dutch high school pupils of different ethnic backgrounds.

Collection of Ego-Centered Network Data with Computer-Assisted Interviews

Methodology ◽  
2006 ◽  
Vol 2 (1) ◽  
pp. 7-15 ◽  
Author(s):  
Joachim Gerich ◽  
Roland Lehner
Keyword(s):  
Survey Methods ◽  
A Priori ◽  
Dynamic Structure ◽  
Computer Assisted ◽  
Network Data ◽  
Great Effort ◽  
Self Administration ◽  
Face To Face ◽  
Full Network

Although ego-centered network data provide information that is limited in various ways as compared with full network data, an ego-centered design can be used without the need for a priori and researcher-defined network borders. Moreover, ego-centered network data can be obtained with traditional survey methods. However, due to the dynamic structure of the questionnaires involved, a great effort is required on the part of either respondents (with self-administration) or interviewers (with face-to-face interviews). As an alternative, we will show the advantages of using CASI (computer-assisted self-administered interview) methods for the collection of ego-centered network data as applied in a study on the role of social networks in substance use among college students.

Sign in / Sign up

Export Citation Format

Share Document