Building damage impact forecasting for winter windstorms in Switzerland

2021 ◽  
Author(s):  
Thomas Röösli ◽  
David N. Bresch
Keyword(s):  
False Alarm ◽  
Meteorological Data ◽  
Weather Forecast ◽  
Building Damage ◽  
False Alarms ◽  
Preventive Action ◽  
Wind Gusts ◽  
Geographical Regions ◽  
Forecasting System ◽  
The Impact

<p>Weather extremes can have high socio-economic impacts. Better impact forecasting and preventive action help to reduce these impacts. In Switzerland, the winter windstorms caused high building damage, felled trees and interrupted traffic and power. Events such as Burglind-Eleanor in January 2018 are a learning opportunity for weather warnings, risk modelling and decision-making.</p><p>We have developed and implemented an operational impact forecasting system for building damage due to wind events in Switzerland. We use the ensemble weather forecast of wind gusts produced by the national meteorological agency MeteoSwiss. We couple this hazard information with a spatially explicit impact model (CLIMADA) for building damages due to winter windstorms. Each day, the impact forecasting system publishes a probabilistic forecast of the expected building damages on a spatial grid.</p><p>This system produces promising results for major historical storms when compared to aggregated daily building insurance claims data from a public building insurer of the canton of Zurich. The daily impact forecasts were qualitatively categorized as (1) successful (2) miss or (3) false alarm. The impacts of windstorm Burglind-Eleanor and five other winter windstorms were forecasted reasonably well, with four successful forecasts, one miss and one false alarm.</p><p> The building damage due to smaller storm extremes was not as successfully forecasted. Thunderstorms are not as well forecasted with 2 days’ lead time and as a result the impact forecasting system produces more misses and false alarms outside the winter storm season. For the Alpine-specific southerly Foehn winds, the impact forecasts produce many false alarms, probably caused by an overestimation of wind gusts in the weather forecast.</p><p>The forecasting system can be used to improve weather warnings and allocate resources and staff in the claims handling process of building insurances. This will help to improve recovery time and costs to institutions and individuals. The open-source code and open meteorological data makes this implementation transferable to other hazard types and other geographical regions.</p>

scholarly journals Toward a Weather-Based Forecasting System for Fire Blight and Downy Mildew

Atmosphere ◽  
2018 ◽  
Vol 9 (12) ◽  
pp. 484 ◽  
Author(s):  
Ana Firanj Sremac ◽  
Branislava Lalić ◽  
Milena Marčić ◽  
Ljiljana Dekić
Keyword(s):  
Downy Mildew ◽  
Fire Blight ◽  
Meteorological Data ◽  
Weather Prediction ◽  
Weather Forecast ◽  
Atmospheric Model ◽  
Plasmopara Viticola ◽  
Forecasting System ◽  
Nwp Model ◽  
The Impact

The aim of this research is to present a weather-based forecasting system for apple fire blight (Erwinia amylovora) and downy mildew of grapevine (Plasmopara viticola) under Serbian agroecological conditions and test its efficacy. The weather-based forecasting system contains Numerical Weather Prediction (NWP) model outputs and a disease occurrence model. The weather forecast used is a product of the high-resolution forecast (HRES) atmospheric model by the European Centre for Medium-Range Weather Forecasts (ECMWF). For disease modelling, we selected a biometeorological system for messages on the occurrence of diseases in fruits and vines (BAHUS) because it contains both diseases with well-known and tested algorithms. Several comparisons were made: (1) forecasted variables for the fifth day are compared against measurements from the agrometeorological network at seven locations for three months (March, April, and May) in the period 2012–2018 to determine forecast efficacy; (2) BAHUS runs driven with observed and forecast meteorology were compared to test the impact of forecasted meteorological data; and (3) BAHUS runs were compared with field disease observations to estimate system efficacy in plant disease forecasts. The BAHUS runs with forecasted and observed meteorology were in good agreement. The results obtained encourage further development, with the goal of fully utilizing this weather-based forecasting system.

scholarly journals Cry Wolf Effect? Evaluating the Impact of False Alarms on Public Responses to Tornado Alerts in the Southeastern United States

2019 ◽  
Vol 11 (3) ◽  
pp. 549-563 ◽  
Author(s):  
JungKyu Rhys Lim ◽  
Brooke Fisher Liu ◽  
Michael Egnoto
Keyword(s):  
United States ◽  
False Alarm ◽  
Protective Action ◽  
Southeastern United States ◽  
The United States ◽  
False Alarms ◽  
Protective Behavior ◽  
Tornado Warnings ◽  
Research Findings ◽  
The Impact

Abstract On average, 75% of tornado warnings in the United States are false alarms. Although forecasters have been concerned that false alarms may generate a complacent public, only a few research studies have examined how the public responds to tornado false alarms. Through four surveys (N = 4162), this study examines how residents in the southeastern United States understand, process, and respond to tornado false alarms. The study then compares social science research findings on perceptions of false alarms to actual county false alarm ratios and the number of tornado warnings issued by counties. Contrary to prior research, findings indicate that concerns about false alarm ratios generating a complacent public may be overblown. Results show that southeastern U.S. residents estimate tornado warnings to be more accurate than they are. Participants’ perceived false alarm ratios are not correlated with actual county false alarm ratios. Counterintuitively, the higher individuals perceive false alarm ratios and tornado alert accuracy to be, the more likely they are to take protective behavior such as sheltering in place in response to tornado warnings. Actual country false alarm ratios and the number of tornado warnings issued did not predict taking protective action.

Validating extended neglect tolerance model for human robot interactions in humanoid soccer robots

Robotica ◽  
2010 ◽  
Vol 29 (3) ◽  
pp. 421-432 ◽  
Author(s):  
R. E. Mohan ◽  
W. S. Wijesoma ◽  
C. A. A. Calderon ◽  
C. J. Zhou
Keyword(s):  
False Alarm ◽  
Task Complexity ◽  
Human Robot Interaction ◽  
False Alarms ◽  
Robot Interaction ◽  
Tolerance Model ◽  
Robot Performance ◽  
Robot Tasks ◽  
The Impact ◽  
Soccer Robots

SUMMARYEstimating robot performance in human robot teams is a vital problem in human robot interaction community. In a previous work, we presented extended neglect tolerance model for estimation of robot performance, where the human operator switches control between robots sequentially based on acceptable performance levels, taking into account any false alarms in human robot interactions. Task complexity is a key parameter that directly impacts the robot performance as well as the false alarms occurrences. In this paper, we validate the extended neglect tolerance model for two robot tasks of varying complexity levels. We also present the impact of task complexity on robot performance estimations and false alarms demands. Experiments were performed with real and virtual humanoid soccer robots across tele-operated and semi-autonomous modes of autonomy. Measured false alarm demand and robot performances were largely consistent with the extended neglect tolerance model predictions for both real and virtual robot experiments. Experiments also showed that the task complexity is directly proportional to false alarm demands and inversely proportional to robot performance.

scholarly journals Automatic Regional Interpretation and Forecasting System Supported by Machine Learning

Atmosphere ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 793
Author(s):  
Chao Yan ◽  
Jing Feng ◽  
Kaiwen Xia ◽  
Chaofan Duan
Keyword(s):  
Machine Learning ◽  
Meteorological Data ◽  
Weather Prediction ◽  
Weather Forecast ◽  
Forecast Model ◽  
Support Vector ◽  
Observation Data ◽  
Forecasting System ◽  
Weather Changes ◽  
Model Output Statistics

The Model Output Statistics (MOS) model is a dynamic statistical weather forecast model based on multiple linear regression technology. It is greatly affected by the selection of parameters and predictors, especially when the weather changes drastically, or extreme weather occurs. We improved the traditional MOS model with the machine learning method to enhance the capabilities of self-learning and generalization. Simultaneously, multi-source meteorological data were used as the input to the model to improve the data quality. In the experiment, we selected the four areas of Nanjing, Beijing, Chengdu, and Guangzhou for verification, with the numerical weather prediction (NWP) products and observation data from automatic weather stations (AWSs) used to predict the temperature and wind speed in the next 24 h. From the experiment, it can be seen that the accuracy of the prediction values and speed of the method were improved by the ML-MOS. Finally, we compared the ML-MOS model with neural networks and support vector machine (SVM), the results show that the prediction result of the ML-MOS model is better than that of the above two models.

scholarly journals Detection of Severe Weather Events in a High-Resolution Ensemble Prediction System Using the Extreme Forecast Index (EFI) and Shift of Tails (SOT)

2018 ◽  
Vol 33 (4) ◽  
pp. 901-908
Author(s):  
Laure Raynaud ◽  
Benoît Touzé ◽  
Philippe Arbogast
Keyword(s):  
Reference Model ◽  
Weather Forecast ◽  
Ensemble Forecast ◽  
Severe Weather ◽  
Ensemble Prediction ◽  
False Alarms ◽  
Prediction System ◽  
Ensemble Prediction System ◽  
Wind Gusts ◽  
Weather Events

Abstract The extreme forecast index (EFI) and shift of tails (SOT) are commonly used to compare an ensemble forecast to a reference model climatology, in order to measure the severity of the current weather forecast. In this study, the feasibility and the relevance of EFI and SOT computations are examined within the convection-permitting Application of Research to Operations at Mesoscale (AROME-France) ensemble prediction system (EPS). First, different climate configurations are proposed and discussed, in order to overcome the small size of the ensemble and the short climate sampling length. Subjective and objective evaluations of EFI and SOT for wind gusts and precipitation forecasts are then presented. It is shown that these indices can provide relevant early warnings and, based on a trade-off between hits and false alarms, optimal EFI thresholds can be determined for decision-making.

scholarly journals Evaluation of Global Flood Detection Using Satellite-Based Rainfall and a Hydrologic Model

2012 ◽  
Vol 13 (4) ◽  
pp. 1268-1284 ◽  
Author(s):  
Huan Wu ◽  
Robert F. Adler ◽  
Yang Hong ◽  
Yudong Tian ◽  
Fritz Policelli
Keyword(s):  
False Alarm ◽  
Event Detection ◽  
Global Scale ◽  
Hydrologic Model ◽  
Flood Frequency ◽  
Probability Of Detection ◽  
Flood Event ◽  
False Alarms ◽  
Pearson Type ◽  
The Impact

Abstract A new version of a real-time global flood monitoring system (GFMS) driven by Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) rainfall has been developed and implemented using a physically based hydrologic model. The purpose of this paper is to evaluate the performance of this new version of the GFMS in terms of flood event detection against flood event archives to establish a baseline of performance and directions for improvement. This new GFMS is quantitatively evaluated in terms of flood event detection during the TRMM era (1998–2010) using a global retrospective simulation (3-hourly and ⅛° spatial resolution) with the TMPA 3B42V6 rainfall. Four methods were explored to define flood thresholds from the model results, including three percentile-based statistical methods and a Log Pearson type-III flood frequency curve method. The evaluation showed the GFMS detection performance improves [increasing probability of detection (POD)] with longer flood durations and larger affected areas. The impact of dams was detected in the validation statistics, with the presence of dams tending to result in more false alarms and greater false-alarm duration. The GFMS validation statistics for flood durations >3 days and for areas without dams vary across the four methods, but center around a POD of ~0.70 and a false-alarm rate (FAR) of ~0.65. The generally positive results indicate the value of this approach for monitoring and researching floods on a global scale, but also indicate limitations and directions for improvement of such approaches. These directions include improving the rainfall estimates, utilizing higher resolution in the runoff-routing model, taking into account the presence of dams, and improving the method for flood identification.

The Influence of False Alarms of Threats Technical Detection Means on the Security System Construction

2019 ◽  
Vol 7 (2) ◽  
pp. 22-28
Author(s):  
Александр Петренко ◽  
Aleksandr Petrenko ◽  
Александр Суворов ◽  
Aleksandr Suvorov ◽  
Евгения Плотникова ◽  
...  
Keyword(s):  
Mathematical Model ◽  
False Alarm ◽  
False Alarms ◽  
System Construction ◽  
Security Assurance ◽  
Probability Of False Alarm ◽  
Negative Factor ◽  
Timely Response ◽  
The Impact ◽  
Negative Factors

Some theoretical aspects for determining the required number of mobile groups involved in objects security assurance from the impact of various negative factors have been considered. A mathematical model, that allow substantiate the required number of groups for timely response to negative factors has been developed. The total number of response teams has been determined and corrected with account for probability of false alarm related to several technical detection means. The influence of placement for technical detection means, and response teams, as well as of negative factors’ characteristics on the catch line tailoring has been shown. The negative factor’s catch line sizes in dependence to the detection means’ concrete location have been calculated. A certainty increase for operation of technical detection means is carried out by determining the regularity of triggering moments, taking into account the appearance of false alarms. An inspection for received signals’ certainty is carried out by guard personnel from response teams. The developed model will allow increase the security system’s response time to the negative factor. The specified models will allow develop the software for modeling of real situations.

False Alarms, Tornado Warnings, and Tornado Casualties

2009 ◽  
Vol 1 (1) ◽  
pp. 38-53 ◽  
Author(s):  
Kevin M. Simmons ◽  
Daniel Sutter
Keyword(s):  
False Alarm ◽  
The United States ◽  
Probability Of Detection ◽  
False Alarms ◽  
Lead Times ◽  
Time Intervals ◽  
Standard Deviation Increase ◽  
False Alarm Ratio ◽  
Tornado Warnings ◽  
The Impact

Abstract This paper extends prior research on the societal value of tornado warnings to the impact of false alarms. Intuition and theory suggest that false alarms will reduce the response to warnings, yet little evidence of a “false alarm effect” has been unearthed. This paper exploits differences in the false-alarm ratio across the United States to test for a false-alarm effect in a regression model of tornado casualties from 1986 to 2004. A statistically significant and large false-alarm effect is found: tornadoes that occur in an area with a higher false-alarm ratio kill and injure more people, everything else being constant. The effect is consistent across false-alarm ratios defined over different geographies and time intervals. A one-standard-deviation increase in the false-alarm ratio increases expected fatalities by between 12% and 29% and increases expected injuries by between 14% and 32%. The reduction in the national tornado false-alarm ratio over the period reduced fatalities by 4%–11% and injuries by 4%–13%. The casualty effects of false alarms and warning lead times are approximately equal in magnitude, suggesting that the National Weather Service could not reduce casualties by trading off a higher probability of detection for a higher false-alarm ratio, or vice versa.

scholarly journals The impact of prior and ongoing threat on the false alarm threshold for facial discrimination

2019 ◽  
Author(s):  
Santiago Papini ◽  
Joseph E. Dunsmoor ◽  
Jasper A. J. Smits
Keyword(s):  
Anxiety Disorders ◽  
False Alarm ◽  
False Alarms ◽  
Facial Cues ◽  
Rapid Responses ◽  
Alarm Threshold ◽  
Identity Based ◽  
The Cost ◽  
The Impact

Perceptual adaptations facilitate rapid responses to threats but can come with the cost of false alarms, or the failure to discriminate safe or novel stimuli from signals of true threat. For example, a fatigued colleague might be avoided when their tired expression is interpreted as a scowl, or a glimpse at a stranger might cause a rush of anxiety if they resemble a known adversary. We examined false alarms in the context of facial cues, which can become exaggerated signals of threat across anxiety disorders. In Experiment 1, ongoing threat lowered the false alarm threshold for discrimination based on anger intensity compared to prior and no threat. In Experiment 2, prior and ongoing threat each lowered the false alarm threshold for identity-based facial discrimination compared to no threat. These results could be relevant for anxiety disorders in which excessive false alarms may contribute to overgeneralized threat responses.

scholarly journals A 5-yr Climatology of Tornado False Alarms

2011 ◽  
Vol 26 (4) ◽  
pp. 534-544 ◽  
Author(s):  
J. Brotzge ◽  
S. Erickson ◽  
H. Brooks
Keyword(s):  
United States ◽  
Population Density ◽  
False Alarm ◽  
Late Summer ◽  
Weather Forecast ◽  
False Alarms ◽  
Combined Effects ◽  
Tornado Warnings ◽  
County Population ◽  
Geographic Regions

Abstract During 2008 approximately 75% of tornado warnings issued by the National Weather Service (NWS) were false alarms. This study investigates some of the climatological trends in the issuance of false alarms and highlights several factors that impact false-alarm ratio (FAR) statistics. All tornadoes and tornado warnings issued across the continental United States between 2000 and 2004 were analyzed, and the data were sorted by hour of the day, month of the year, geographical region and weather forecast office (WFO), the number of tornadoes observed on a day in which a false alarm was issued, distance of the warned area from the nearest NWS radar, county population density, and county area. Analysis of the tornado false-alarm data identified six specific trends. First, the FAR was highest during nonpeak storm periods, such as during the night and during the winter and late summer. Second, the FAR was strongly tied to the number of tornadoes warned per day. Nearly one-third of all false alarms were issued on days when no tornadoes were confirmed within the WFO’s county warning area. Third, the FAR varied with distance from radar, with significantly lower estimates found beyond 150 km from radar. Fourth, the FAR varied with population density. For warnings within 50 km of an NWS radar, FAR increased with population density; however, for warnings beyond 150 km from radar, FAR decreased regardless of population density. Fifth, the FAR also varied as a function of county size. The FAR was generally highest for the smallest counties; the FAR was ~80% for all counties less than 1000 km2 regardless of distance from radar. Finally, the combined effects of distance from radar, population density, and county size led to significant variability across geographic regions.

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