Optimizing the real-time ground level enhancement alert system based on neutron monitor measurements: IntroducingGLE Alert Plus

Space Weather ◽  
2014 ◽  
Vol 12 (11) ◽  
pp. 633-649 ◽  
Author(s):  
G. Souvatzoglou ◽  
A. Papaioannou ◽  
H. Mavromichalaki ◽  
J. Dimitroulakos ◽  
C. Sarlanis
Keyword(s):  
Real Time ◽  
Neutron Monitor ◽  
Ground Level ◽  
Alert System ◽  
Ground Level Enhancement ◽  
The Real

ALERT SYSTEM FOR GROUND LEVEL COSMIC-RAY ENHANCEMENTS PREDICTION AT THE ATHENS NEUTRON MONITOR NETWORK IN REAL-TIME

2005 ◽  
Vol 20 (29) ◽  
pp. 6711-6713 ◽  
Author(s):  
G. MARIATOS ◽  
H. MAVROMICHALAKI ◽  
C. SARLANIS ◽  
G. SOUVATZOGLOU ◽  
A. BELOV ◽  
...  
Keyword(s):  
Real Time ◽  
Neutron Monitor ◽  
High Frequency ◽  
Power Transmission ◽  
Cosmic Ray ◽  
Ground Level ◽  
Alert System ◽  
Radio Communications ◽  
Cosmic Particle ◽  
Ground Level Enhancements

The prediction of solar activity is important for various technologies, including operation of low-Earth orbiting satellites, electric power transmission grids, high-frequency radio-communications etc. The Athens Neutron Monitor Network in Real Time, initiated in December 2003, provides data from twenty-one real-time neutron monitor stations, useful for real-time monitoring of cosmic particle fluxes. Recently a program for forecasting the arrival of dangerous middle energy particles on the Earth's surface has started. These program processes the data taken from the Neutron Monitor Network and informs us about the onset of ground level enhancements. In this way enough time to protect technological systems will be given.

The First Ground-Level Enhancement of Solar Cycle 24 on 17 May 2012 and Its Real-Time Detection

Solar Physics ◽  
2013 ◽  
Vol 289 (1) ◽  
pp. 423-436 ◽  
Author(s):  
A. Papaioannou ◽  
G. Souvatzoglou ◽  
P. Paschalis ◽  
M. Gerontidou ◽  
H. Mavromichalaki
Keyword(s):  
Solar Cycle ◽  
Real Time ◽  
Ground Level ◽  
Ground Level Enhancement ◽  
Solar Cycle 24 ◽  
Real Time Detection ◽  
Cycle 24

scholarly journals Applications and usage of the real-time Neutron Monitor Database

2011 ◽  
Vol 47 (12) ◽  
pp. 2210-2222 ◽  
Author(s):  
H. Mavromichalaki ◽  
A. Papaioannou ◽  
C. Plainaki ◽  
C. Sarlanis ◽  
G. Souvatzoglou ◽  
...  
Keyword(s):  
Real Time ◽  
Neutron Monitor ◽  
The Real

scholarly journals Real-Time Detection of the Ground Level Enhancement on 10 September 2017 by A.Ne.Mo.S.: System Report

Space Weather ◽  
2018 ◽  
Vol 16 (11) ◽  
pp. 1797-1805 ◽  
Author(s):  
H. Mavromichalaki ◽  
M. Gerontidou ◽  
P. Paschalis ◽  
E. Paouris ◽  
A. Tezari ◽  
...  
Keyword(s):  
Real Time ◽  
Ground Level ◽  
Ground Level Enhancement ◽  
Real Time Detection

scholarly journals GLE # 67 Event on 2 November 2003: An Analysis of the Spectral and Anisotropy Characteristics Using Verified Yield Function and Detrended Neutron Monitor Data

Solar Physics ◽  
2021 ◽  
Vol 296 (5) ◽  
Author(s):  
Alexander L. Mishev ◽  
Sergey A. Koldobskiy ◽  
Leon G. Kocharov ◽  
Ilya G. Usoskin
Keyword(s):  
Neutron Monitor ◽  
Dynamical Evolution ◽  
Ground Level ◽  
High Energy ◽  
Yield Function ◽  
Angle Distribution ◽  
Ground Level Enhancement ◽  
Solar Particles ◽  
Monitor Data

AbstractDuring Solar Cycle 23 16 ground-level enhancement events were registered by the global neutron monitor network. In this work we focus on the period with increased solar activity during late October – early November 2003 producing a sequence of three events, specifically on ground-level enhancement GLE 67 on 2 November 2003. On the basis of an analysis of neutron monitor and space-borne data we derived the spectra and pitch-angle distribution of high-energy solar particles with their dynamical evolution throughout the event. According to our analysis, the best fit of the spectral and angular properties of solar particles was obtained by a modified power-law rigidity spectrum and a double Gaussian, respectively. The derived angular distribution is consistent with the observations where an early count rate increase at Oulu neutron monitor with asymptotic viewing direction in the anti-Sun direction was registered. The quality of the fit and model constraints were assessed by a forward modeling. The event integrated particle fluence was derived using two different methods. The derived results are briefly discussed.

Managing the humanitarian relief chain: the Uttarakhand disaster issues

2016 ◽  
Vol 13 (1) ◽  
Author(s):  
Kunal K Ganguly ◽  
Siddharth Shankar Rai
Keyword(s):  
Supply Chain ◽  
Real Time ◽  
Inventory Management ◽  
Design Methodology ◽  
Relevant Literature ◽  
Ground Level ◽  
Historical Record ◽  
Humanitarian Relief ◽  
The Real ◽  
Practical Implications

Purpose The purpose of this paper is to identify various issues and challenges humanitarian supply chain faces on the ground level. The paper aims at defining the major problems and searching for their possible solutions with the help of available literature and interaction of ground level experience. Design/methodology/approach The authors focus on building a theory and propose solutions for the real time problems faced in the disaster environment. To solve its purpose, besides taking the insights from available and relevant literature, paper has followed ground level experience of the concerned people in supply chain as well. Authors have quoted the real time examples and statements in order to make the situation more clear and precise. Findings Humanitarian relief supply chain in today’s environment often confronts the challenges such as information availability, inventory management, collaboration, logistics related issues, preparedness etc. The study explores various challenges. After searching roots of these challenges in literature it proposes some considerable solutions which can be further studied for their validity, acceptability and implementation. Research limitations/implications The research has considered only the natural disasters and not the manmade ones. It has also taken examples from Indian conditions. However, the conditions in other countries and their practices for the disaster management may vary to certain extent. There are many disasters such as volcanos which do not have a historical record of striking in Indian conditions which confine the authorities to disaster specific practices. Practical implications The research initiates further work in this area on various issues explored in the study. There is scope of separate attention on each of the issues discussed. The study will help the humanitarian relief practitioners to understand the insights of disaster situations. It will convince the practitioners and future researchers on focusing and analyzing the proposed frameworks. Originality/value The paper presents issues from real time situations at the ground level in humanitarian relief environment. With the help of literature authors have tried to conceptualize these issues and their “can be” solutions.

scholarly journals REAL-TIME AND SEAMLESS MONITORING OF GROUND-LEVEL PM<sub>2.5</sub> USING SATELLITE REMOTE SENSING

2018 ◽  
Vol IV-3 ◽  
pp. 143-147
Author(s):  
Tongwen Li ◽  
Chengyue Zhang ◽  
Huanfeng Shen ◽  
Qiangqiang Yuan ◽  
Liangpei Zhang
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Real Time ◽  
Satellite Remote Sensing ◽  
Ground Level ◽  
Fusion Model ◽  
The Real ◽  
Data Gaps ◽  
Spatio Temporal ◽  
Polar Orbiting Satellite

Satellite remote sensing has been reported to be a promising approach for the monitoring of atmospheric PM<sub>2.5</sub>. However, the satellite-based monitoring of ground-level PM<sub>2.5</sub> is still challenging. First, the previously used polar-orbiting satellite observations, which can be usually acquired only once per day, are hard to monitor PM<sub>2.5</sub> in real time. Second, many data gaps exist in satellitederived PM<sub>2.5</sub> due to the cloud contamination. In this paper, the hourly geostationary satellite (i.e., Harawari-8) observations were adopted for the real-time monitoring of PM<sub>2.5</sub> in a deep learning architecture. On this basis, the satellite-derived PM<sub>2.5</sub> in conjunction with ground PM<sub>2.5</sub> measurements are incorporated into a spatio-temporal fusion model to fill the data gaps. Using Wuhan Urban Agglomeration as an example, we have successfully derived the real-time and seamless PM<sub>2.5</sub> distributions. The results demonstrate that Harawari-8 satellite-based deep learning model achieves a satisfactory performance (out-of-sample cross-validation R<sup>2</sup>&amp;thinsp;=&amp;thinsp;0.80, RMSE&amp;thinsp;=&amp;thinsp;17.49&amp;thinsp;&amp;mu;g/m<sup>3</sup>) for the estimation of PM<sub>2.5</sub>. The missing data in satellite-derive PM<sub>2.5</sub> are accurately recovered, with R<sup>2</sup> between recoveries and ground measurements of 0.75. Overall, this study has inherently provided an effective strategy for the realtime and seamless monitoring of ground-level PM<sub>2.5</sub>.

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