scholarly journals Early Warning Systems with Real-Time Data

2017 ◽  
Author(s):  
Tjeerd M. Boonman ◽  
Jan P. A. M. Jacobs ◽  
Gerard H. Kuper ◽  
Alberto Romero
Keyword(s):  
Real Time ◽  
Early Warning ◽  
Early Warning Systems ◽  
Warning Systems ◽  
Time Data ◽  
Real Time Data

scholarly journals Early Warning Systems for Currency Crises with Real-Time Data

2019 ◽  
Vol 30 (4) ◽  
pp. 813-835 ◽  
Author(s):  
Tjeerd M. Boonman ◽  
Jan P. A. M. Jacobs ◽  
Gerard H. Kuper ◽  
Alberto Romero
Keyword(s):  
Real Time ◽  
Early Warning ◽  
Currency Crises ◽  
Early Warning Systems ◽  
Warning Systems ◽  
Time Data ◽  
Real Time Data

scholarly journals Early Warning Systems with Real-Time Data

2017 ◽  
Author(s):  
Tjeerd M. Boonman ◽  
Gerard H. Kuper ◽  
Jan P.A.M. Jacobs ◽  
Alberto Romero
Keyword(s):  
Real Time ◽  
Early Warning ◽  
Early Warning Systems ◽  
Warning Systems ◽  
Time Data ◽  
Real Time Data

Early Warning of Power Plant Equipment Based on Massive Real-Time Data Mining Technology

2014 ◽  
Vol 599-601 ◽  
pp. 1487-1490 ◽  
Author(s):  
Li Kun Zheng ◽  
Kun Feng ◽  
Xiao Qing Xiao ◽  
Wei Qiao Song
Keyword(s):  
Data Mining ◽  
Power Plant ◽  
Real Time ◽  
Early Warning ◽  
Time Data ◽  
Mining Technology ◽  
Equipment Safety ◽  
State Evaluation ◽  
Power Plant Equipment ◽  
Real Time Data

This paper mainly discusses the application of the mass real-time data mining technology in equipment safety state evaluation in the power plant and the realization of the equipment comprehensive quantitative assessment and early warning of potential failure by mining analysis and modeling massive amounts of real-time data the power equipment. In addition to the foundational technology introduced in this paper, the technology is also verified by the application case in the power supply side remote diagnosis center of Guangdong electric institute.

scholarly journals Near real-time GPS applications for tsunami early warning systems

2010 ◽  
Vol 10 (2) ◽  
pp. 181-189 ◽  
Author(s):  
C. Falck ◽  
M. Ramatschi ◽  
C. Subarya ◽  
M. Bartsch ◽  
A. Merx ◽  
...  
Keyword(s):  
Real Time ◽  
Early Warning ◽  
Early Warning System ◽  
Tide Gauge ◽  
Warning System ◽  
Early Warning Systems ◽  
Time Data ◽  
Essential Information ◽  
Tsunami Early Warning ◽  
Land Mass

Abstract. GPS (Global Positioning System) technology is widely used for positioning applications. Many of them have high requirements with respect to precision, reliability or fast product delivery, but usually not all at the same time as it is the case for early warning applications. The tasks for the GPS-based components within the GITEWS project (German Indonesian Tsunami Early Warning System, Rudloff et al., 2009) are to support the determination of sea levels (measured onshore and offshore) and to detect co-seismic land mass displacements with the lowest possible latency (design goal: first reliable results after 5 min). The completed system was designed to fulfil these tasks in near real-time, rather than for scientific research requirements. The obtained data products (movements of GPS antennas) are supporting the warning process in different ways. The measurements from GPS instruments on buoys allow the earliest possible detection or confirmation of tsunami waves on the ocean. Onshore GPS measurements are made collocated with tide gauges or seismological stations and give information about co-seismic land mass movements as recorded, e.g., during the great Sumatra-Andaman earthquake of 2004 (Subarya et al., 2006). This information is important to separate tsunami-caused sea height movements from apparent sea height changes at tide gauge locations (sensor station movement) and also as additional information about earthquakes' mechanisms, as this is an essential information to predict a tsunami (Sobolev et al., 2007). This article gives an end-to-end overview of the GITEWS GPS-component system, from the GPS sensors (GPS receiver with GPS antenna and auxiliary systems, either onshore or offshore) to the early warning centre displays. We describe how the GPS sensors have been installed, how they are operated and the methods used to collect, transfer and process the GPS data in near real-time. This includes the sensor system design, the communication system layout with real-time data streaming, the data processing strategy and the final products of the GPS-based early warning system components.

scholarly journals P-wave picking for earthquake early warning: Refinement of a T pd method

2021 ◽  
Author(s):  
Masumi Yamada ◽  
Jim Mori
Keyword(s):  
Real Time ◽  
Early Warning ◽  
Arrival Time ◽  
Early Warning Systems ◽  
Earthquake Early Warning ◽  
P Wave ◽  
Warning Systems ◽  
Wave Detection ◽  
Earthquake Early Warning Systems ◽  
Wave Arrival

Summary Detecting P-wave onsets for on-line processing is an important component for real-time seismology. As earthquake early warning systems around the world come into operation, the importance of reliable P-wave detection has increased, since the accuracy of the earthquake information depends primarily on the quality of the detection. In addition to the accuracy of arrival time determination, the robustness in the presence of noise and the speed of detection are important factors in the methods used for the earthquake early warning. In this paper, we tried to improve the P-wave detection method designed for real-time processing of continuous waveforms. We used the new Tpd method, and proposed a refinement algorithm to determine the P-wave arrival time. Applying the refinement process substantially decreases the errors of the P-wave arrival time. Using 606 strong motion records of the 2011 Tohoku earthquake sequence to test the refinement methods, the median of the error was decreased from 0.15 s to 0.04 s. Only three P-wave arrivals were missed by the best threshold. Our results show that the Tpd method provides better accuracy for estimating the P-wave arrival time compared to the STA/LTA method. The Tpd method also shows better performance in detecting the P-wave arrivals of the target earthquakes in the presence of noise and coda of previous earthquakes. The Tpd method can be computed quickly so it would be suitable for the implementation in earthquake early warning systems.

scholarly journals Communicating Complex Forecasts for Enhanced Early Warning in Nepal

2019 ◽  
Author(s):  
Mirianna Budimir ◽  
Amy Donovan ◽  
Sarah Brown ◽  
Puja Shakya ◽  
Dilip Gautam ◽  
...  
Keyword(s):  
Early Warning ◽  
Lead Time ◽  
Warning System ◽  
Early Warning Systems ◽  
Warning Systems ◽  
Time Data ◽  
Deterministic Models ◽  
Early Action ◽  
Probabilistic Forecasts ◽  
Barriers And Challenges

Abstract. Early warning systems have the potential to save lives and improve resilience. Simple early warning systems rely on real-time data and deterministic models to generate evacuation warnings; these simple deterministic models enable life-saving action, but provide limited lead time for resilience-building early action. More complex early warning systems supported by forecasts, including probabilistic forecasts, can provide additional lead time for preparation. However, barriers and challenges remain in disseminating and communicating these more complex warnings to community members and individuals at risk. Research was undertaken to analyse and understand the current early warning system in Nepal, considering available data and forecasts, information flows, early warning dissemination and decision making for early action. The research reviewed the availability and utilisation of complex forecasts in Nepal, their integration into dissemination (Department of Hydrology and Meteorology (DHM) bulletins and SMS warnings), and decision support tools (Common Alerting Protocols and Standard Operating Procedures), considering their impact on improving early action to increase the resilience of vulnerable communities to flooding.

scholarly journals Towards an operational database for real-time environmental monitoring and early warning systems

2017 ◽  
Vol 108 ◽  
pp. 2250-2259 ◽  
Author(s):  
Bartosz Balis ◽  
Marian Bubak ◽  
Daniel Harezlak ◽  
Piotr Nowakowski ◽  
Maciej Pawlik ◽  
...  
Keyword(s):  
Environmental Monitoring ◽  
Real Time ◽  
Early Warning ◽  
Early Warning Systems ◽  
Warning Systems ◽  
Monitoring And Early Warning

scholarly journals Live-Cell Systems in Real-Time Biomonitoring of Water Pollution: Practical Considerations and Future Perspectives

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7028
Author(s):  
Donald Wlodkowic ◽  
Tomasz M. Karpiński
Keyword(s):  
Water Quality ◽  
Real Time ◽  
Early Warning ◽  
Early Warning Systems ◽  
Live Cell ◽  
Warning Systems ◽  
Water Contaminants ◽  
Sustainable Food ◽  
Recent Advances ◽  
Cell Microarrays

Continuous monitoring and early warning of potential water contamination with toxic chemicals is of paramount importance for human health and sustainable food production. During the last few decades there have been noteworthy advances in technologies for the automated sensing of physicochemical parameters of water. These do not translate well into online monitoring of chemical pollutants since most of them are either incapable of real-time detection or unable to detect impacts on biological organisms. As a result, biological early warning systems have been proposed to supplement conventional water quality test strategies. Such systems can continuously evaluate physiological parameters of suitable aquatic species and alert the user to the presence of toxicants. In this regard, single cellular organisms, such as bacteria, cyanobacteria, micro-algae and vertebrate cell lines, offer promising avenues for development of water biosensors. Historically, only a handful of systems utilising single-cell organisms have been deployed as established online water biomonitoring tools. Recent advances in recombinant microorganisms, cell immobilisation techniques, live-cell microarrays and microfluidic Lab-on-a-Chip technologies open new avenues to develop miniaturised systems capable of detecting a broad range of water contaminants. In experimental settings, they have been shown as sensitive and rapid biosensors with capabilities to detect traces of contaminants. In this work, we critically review the recent advances and practical prospects of biological early warning systems based on live-cell biosensors. We demonstrate historical deployment successes, technological innovations, as well as current challenges for the broader deployment of live-cell biosensors in the monitoring of water quality.

Integration of operational Multi-Hazard Early Warning Systems and COVID-19 data for the humanitarian community

2021 ◽  
Author(s):  
Chiara Proietti ◽  
Alessandro Annunziato ◽  
Pamela Probst ◽  
Stefano Paris ◽  
Thomas Peter
Keyword(s):  
Real Time ◽  
Early Warning ◽  
Forest Fires ◽  
Large Scale ◽  
Warning System ◽  
Early Warning Systems ◽  
Research Centre ◽  
Time Data ◽  
Joint Research ◽  
Coordination System

<p>To improve preparedness and response in case of large-scale disasters, the international humanitarian community needs to understand the anticipated impact of an event as soon as possible in order to take informed operational decisions. The European Commission’s Joint Research Centre (JRC), DG ECHO, and the United Nations’ OCHA and UNOSAT launched the Global Disaster Alert and Coordination System (www.GDACS.org) in 2002-03 as cooperation platform to provide early disaster warning and coordination services to humanitarian actors. After more than 15 years, GDACS has around 30k registered users among humanitarian organisations at global level.</p><p>At the beginning, one of GDACS’s main tasks was the dissemination of automatic alerts for earthquakes, tsunamis and tropical cyclones; today, the system has been augmented to include also floods, droughts and volcanoes, and it will soon include forest fires.  Alerts are sent to the international humanitarian community to ensure timely warning in severe events that are expected to require international assistance. Alert levels are determined by automated algorithms without, or with very limited, human intervention, using automatic real-time data-feeds from various scientific institutes or the JRC’s own systems.</p><p>From 2020, because of the potential impact of the COVID-19 emergency on international preparedness and response activities, the COVID-19 situation in affected countries is now also monitored by the system, providing real time information updates on the website. This new feature allows to consider in the planning of the emergency response, the severity of the outbreak in the affected countries.</p><p>This contribution presents the challenges and outcomes of combining science-based information from different independent systems into a single Multi-Hazard Early Warning System and introduces new functionalities that were recently developed to address the new challenges related to the COVID-19 emergency.</p>

scholarly journals African Rainfall Climatology Version 2 for Famine Early Warning Systems

2013 ◽  
Vol 52 (3) ◽  
pp. 588-606 ◽  
Author(s):  
Nicholas S. Novella ◽  
Wassila M. Thiaw
Keyword(s):  
Real Time ◽  
Early Warning ◽  
Correlation Coefficients ◽  
Global Precipitation Climatology Project ◽  
Early Warning Systems ◽  
Warning Systems ◽  
Telecommunication System ◽  
African Rainfall ◽  
Global Telecommunication System ◽  
Rainfall Climatology

AbstractThis paper describes a new gridded, daily 29-yr precipitation estimation dataset centered over Africa at 0.1° spatial resolution. Called the African Rainfall Climatology, version 2 (ARC2), it is a revision of the first version of the ARC. Consistent with the operational Rainfall Estimation, version 2, algorithm (RFE2), ARC2 uses inputs from two sources: 1) 3-hourly geostationary infrared (IR) data centered over Africa from the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) and 2) quality-controlled Global Telecommunication System (GTS) gauge observations reporting 24-h rainfall accumulations over Africa. The main difference with ARC1 resides in the recalibration of all Meteosat First Generation (MFG) IR data (1983–2005). Results show that ARC2 is a major improvement over ARC1. It is consistent with other long-term datasets, such as the Global Precipitation Climatology Project (GPCP) and Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP), with correlation coefficients of 0.86 over a 27-yr period. However, a marginal summer dry bias that occurs over West and East Africa is examined. Daily validation with independent gauge data shows RMSEs of 11.3, 13.4, and 14, respectively, for ARC2, Tropical Rainfall Measuring Mission Multisatellite Precipitation Analysis 3B42, version 6 (3B42v6), and the CPC morphing technique (CMORPH) for the West African summer season. The ARC2 RMSE is slightly higher for Ethiopia than those of CMORPH and 3B42v6. Both daily and monthly validations suggested that ARC2 underestimations may be attributed to the unavailability of daily GTS gauge reports in real time, and deficiencies in the satellite estimate associated with precipitation processes over coastal and orographic areas. However, ARC2 is expected to provide users with real-time monitoring of the daily evolution of precipitation, which is instrumental in improved decision making in famine early warning systems.

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