scholarly journals Revisiting the Predictability of the Haicheng and Tangshan Earthquakes

Symmetry ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 1206
Author(s):  
Didier Sornette ◽  
Euan Mearns ◽  
Spencer Wheatley
Keyword(s):  
Lead Time ◽  
Warning System ◽  
Coarse Graining ◽  
Well Water ◽  
Tangshan Earthquake ◽  
Soil Resistivity ◽  
Time Prediction ◽  
Well Water Level ◽  
Haicheng Earthquake ◽  
Water Radon

We analyze a set of precursory data measured before but compiled in retrospect of the MS7.5 Haicheng earthquake in February 1975 and the MS7.6–7.8 Tangshan earthquake in July 1976. We propose a robust and simple coarse-graining method that aggregates and counts how all the anomalies together (levelling, geomagnetism, soil resistivity, earth currents, gravity, earth stress, well water radon, well water level) develop as a function of time. We demonstrate strong evidence for the existence of an acceleration of the number of anomalies leading up to the major Haicheng and Tangshan earthquakes. In particular for the Tangshan earthquake, the frequency of occurrence of anomalies is found to be well described by the log-periodic power law singularity (LPPLS) model, previously proposed for the prediction of engineering failures and later adapted to the prediction of financial crashes. Using a mock real-time prediction experiment and simulation study, based on this methodology of monitoring accelerated rates of physical anomalies measured at the surface, we show the potential for an early warning system with a lead time of a few days.

scholarly journals Long-Term Performance Metrics for National Weather Service Tornado Warnings

2018 ◽  
Vol 33 (6) ◽  
pp. 1501-1511 ◽  
Author(s):  
Harold E. Brooks ◽  
James Correia
Keyword(s):  
False Alarm ◽  
Lead Time ◽  
Performance Metrics ◽  
Warning System ◽  
Probability Of Detection ◽  
False Alarms ◽  
National Weather Service ◽  
False Alarm Ratio ◽  
Tornado Warnings ◽  
Weather Service

Abstract Tornado warnings are one of the flagship products of the National Weather Service. We update the time series of various metrics of performance in order to provide baselines over the 1986–2016 period for lead time, probability of detection, false alarm ratio, and warning duration. We have used metrics (mean lead time for tornadoes warned in advance, fraction of tornadoes warned in advance) that work in a consistent way across the official changes in policy for warning issuance, as well as across points in time when unofficial changes took place. The mean lead time for tornadoes warned in advance was relatively constant from 1986 to 2011, while the fraction of tornadoes warned in advance increased through about 2006, and the false alarm ratio slowly decreased. The largest changes in performance take place in 2012 when the default warning duration decreased, and there is an apparent increased emphasis on reducing false alarms. As a result, the lead time, probability of detection, and false alarm ratio all decrease in 2012. Our analysis is based, in large part, on signal detection theory, which separates the quality of the warning system from the threshold for issuing warnings. Threshold changes lead to trade-offs between false alarms and missed detections. Such changes provide further evidence for changes in what the warning system as a whole considers important, as well as highlighting the limitations of measuring performance by looking at metrics independently.

scholarly journals Flood Forecasting and Warning System Structures: Procedure and Application to a Small Urban Stream in South Korea

Water ◽  
2019 ◽  
Vol 11 (8) ◽  
pp. 1571 ◽  
Author(s):  
Song ◽  
Park ◽  
Lee ◽  
Park ◽  
Song
Keyword(s):  
Decision Making ◽  
Water Level ◽  
Real Time ◽  
Lead Time ◽  
Flash Flood ◽  
Warning System ◽  
Flood Forecasting ◽  
Urban Stream ◽  
Forecasting And Warning ◽  
Flood Warnings

The runoff from heavy rainfall reaches urban streams quickly, causing them to rise rapidly. It is therefore of great importance to provide sufficient lead time for evacuation planning and decision making. An efficient flood forecasting and warning method is crucial for ensuring adequate lead time. With this objective, this paper proposes an analysis method for a flood forecasting and warning system, and establishes the criteria for issuing urban-stream flash flood warnings based on the amount of rainfall to allow sufficient lead time. The proposed methodology is a nonstructural approach to flood prediction and risk reduction. It considers water level fluctuations during a rainfall event and estimates the upstream (alert point) and downstream (confluence) water levels for water level analysis based on the rainfall intensity and duration. We also investigate the rainfall/runoff and flow rate/water level relationships using the Hydrologic Engineering Center’s Hydrologic Modeling System (HEC-HMS) and the HEC’s River Analysis System (HEC-RAS) models, respectively, and estimate the rainfall threshold for issuing flash flood warnings depending on the backwater state based on actual watershed conditions. We present a methodology for issuing flash flood warnings at a critical point by considering the effects of fluctuations in various backwater conditions in real time, which will provide practical support for decision making by disaster protection workers. The results are compared with real-time water level observations of the Dorim Stream. Finally, we verify the validity of the flash flood warning criteria by comparing the predicted values with the observed values and performing validity analysis.

Confined well water level

2010 ◽  
pp. 829-832
Author(s):  
F Huang ◽  
Y Zhang ◽  
G Lai ◽  
R Yan
Keyword(s):  
Water Level ◽  
Well Water ◽  
Well Water Level

Quasi-Operational Testing of Real-Time Storm-Longevity Prediction via Machine Learning

2019 ◽  
Vol 34 (5) ◽  
pp. 1437-1451 ◽  
Author(s):  
Amy McGovern ◽  
Christopher D. Karstens ◽  
Travis Smith ◽  
Ryan Lagerquist
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Weather Prediction ◽  
Warning System ◽  
Objective Evaluation ◽  
Boosted Regression Trees ◽  
Time Prediction ◽  
Operational Testing ◽  
Trade Offs ◽  
Naturalistic Environment

Abstract Real-time prediction of storm longevity is a critical challenge for National Weather Service (NWS) forecasters. These predictions can guide forecasters when they issue warnings and implicitly inform them about the potential severity of a storm. This paper presents a machine-learning (ML) system that was used for real-time prediction of storm longevity in the Probabilistic Hazard Information (PHI) tool, making it a Research-to-Operations (R2O) project. Currently, PHI provides forecasters with real-time storm variables and severity predictions from the ProbSevere system, but these predictions do not include storm longevity. We specifically designed our system to be tested in PHI during the 2016 and 2017 Hazardous Weather Testbed (HWT) experiments, which are a quasi-operational naturalistic environment. We considered three ML methods that have proven in prior work to be strong predictors for many weather prediction tasks: elastic nets, random forests, and gradient-boosted regression trees. We present experiments comparing the three ML methods with different types of input data, discuss trade-offs between forecast quality and requirements for real-time deployment, and present both subjective (human-based) and objective evaluation of real-time deployment in the HWT. Results demonstrate that the ML system has lower error than human forecasters, which suggests that it could be used to guide future storm-based warnings, enabling forecasters to focus on other aspects of the warning system.

Indoor radon and well water radon in Virginia and Maryland

1990 ◽  
Vol 19 (6) ◽  
pp. 952-956 ◽  
Author(s):  
Douglas G. Mose ◽  
George W. Mushrush ◽  
Charles Chrosniak
Keyword(s):  
Indoor Radon ◽  
Well Water ◽  
Water Radon

Analysis of the Response Mechanism of Confined Aquifer Well Water Level System to Bulk Strain

2009 ◽  
Vol 52 (6) ◽  
pp. 1389-1401
Author(s):  
Xu-Yan LIU ◽  
Xiao-Jing ZHENG ◽  
Lin WANG ◽  
Ying-Feng JI
Keyword(s):  
Water Level ◽  
Well Water ◽  
Confined Aquifer ◽  
Level System ◽  
Response Mechanism ◽  
Bulk Strain ◽  
Well Water Level

Japan's Earthquake Early Warning System on 11 March 2011: Performance, Shortcomings, and Changes

2013 ◽  
Vol 29 (1_suppl) ◽  
pp. 341-368 ◽  
Author(s):  
Yukio Fujinawa ◽  
Yoichi Noda
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Lead Time ◽  
Warning System ◽  
Earthquake Early Warning ◽  
Island Arc ◽  
High Rate ◽  
Monitoring Networks ◽  
Earthquake Early Warning System ◽  
Improve Analysis

A well-developed public earthquake early warning (EEW) system has been operating in Japan since October 2007. At the time of the 2011 Tohoku-oki earthquake and tsunami (also known as 3.11), several million people near the epicenter received the EEW about 15 to 20 seconds before the most severe shaking occurred, and many more people in surrounding districts had greater lead time before less severe shaking started. Some 90% of these people were able to take advance actions to save their own lives and those of family members or to take other actions according to prior planning. Some actions were taken based on intuitive responses to the alerts. This high rate of effectiveness is assured to be the result of education regarding the EEW system, both in schools and in society at large. In spite of some shortcomings, the proven effectiveness of EEW has led Japan to strengthen the already extensive seismic- and tsunami-monitoring networks offshore, east of the Japan island arc at 150 sites, and to provide a special terminal for advanced uses of EEW in schools with more than 53,000 students. Efforts are also underway to improve analysis and dissemination schemes.

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