scholarly journals Improving weather forecasts by means of HPC solutions: the LEXIS approach in the 2020 Bitti flood event

2021 ◽  
Author(s):  
Paola Mazzoglio ◽  
Paolo Pasquali ◽  
Andrea Parodi ◽  
Antonio Parodi
Keyword(s):  
Real Time ◽  
Spatial Resolution ◽  
Early Warning ◽  
Heavy Rainfall ◽  
Large Scale ◽  
Detection System ◽  
Daily Rainfall ◽  
Early Warning Systems ◽  
Flood Event ◽  
Heavy Rainfall Event

<p>In the framework of LEXIS (Large-scale EXecution for Industry & Society) H2020 project, CIMA Research Foundation is running a 3 nested domain WRF (Weather Research and Forecasting) model with European coverage and weather radar data assimilation over Italy. Forecasts up to 48 hours characterized by a 7.5 km resolution are then processed by ITHACA ERDS (Extreme Rainfall Detection System), an early warning system for the heavy rainfall monitoring and forecasting. This type of information is currently managed by ERDS together with two global-scale datasets. The first one is provided by NASA/JAXA GPM (Global Precipitation Measurement) Mission through the IMERG (Integrated Multi-satellitE Retrievals for GPM) Early run data, a near real-time rainfall information with hourly updates, 0.1° spatial resolution and a 4 hours latency. The second one is instead provided by GFS (Global Forecast System) at a 0.25° spatial resolution.<br>The entire WRF-ERDS workflow has been tested and validated on the heavy rainfall event that affected the Sardinia region between 27 and 29 November 2020. This convective event significantly impacted the southern and eastern areas of the island, with a daily rainfall depth of 500.6 mm recorded at Oliena and 328.6 mm recorded at Bitti. During the 28th, the town of Bitti (Nuoro province) was hit by a severe flood event.<br>Near real-time information provided by GPM data allowed us to issue alerts starting from the late morning of the 28th. The first alert over Sardinia based on GFS data was provided in the late afternoon of the 27th, about 40 km far from Bitti. In the early morning of the 28th, a new and more precise alert was issued over Bitti. The first alert based on WRF data was instead provided in the morning of the 27th and the system continued to issue alerts until the evening of the 29th, confirming that, for this type of event, precise forecasts are needed to provide timely alerts.<br>Obtained results show how, taking advantage of HPC resources to perform finer weather forecast experiments, it is possible to significantly improve the capabilities of early warning systems. By using WRF data, ERDS was able to provide heavy rainfall alerts one day before than with the other data.<br>The integration within the LEXIS platform will help with the automatization by data-aware orchestration of our workflow together with easy control of data and workflow steps through a user-friendly web interface.</p>

scholarly journals Risk-Based Early Warning System for Pluvial Flash Floods: Approaches and Foundations

Geosciences ◽  
2019 ◽  
Vol 9 (3) ◽  
pp. 127 ◽  
Author(s):  
Julian Hofmann ◽  
Holger Schüttrumpf
Keyword(s):  
Information System ◽  
Early Warning ◽  
Urban Areas ◽  
Heavy Rainfall ◽  
Flood Hazard ◽  
Significant Risk ◽  
Early Warning Systems ◽  
Flash Floods ◽  
Water Levels ◽  
Heavy Rainfall Event

In times of increasing weather extremes and expanding vulnerable cities, a significant risk to civilian security is posed by heavy rainfall induced flash floods. In contrast to river floods, pluvial flash floods can occur anytime, anywhere and vary enormously due to both terrain and climate factors. Current early warning systems (EWS) are based largely on measuring rainfall intensity or monitoring water levels, whereby the real danger due to urban torrential floods is just as insufficiently considered as the vulnerability of the physical infrastructure. For this reason, this article presents a concept for a risk-based EWS as one integral component of a multi-functional pluvial flood information system (MPFIS). Taking both the pluvial flood hazard as well as the damage potential into account, the EWS identifies the urban areas particularly affected by a forecasted heavy rainfall event and issues object-precise warnings in real-time. Further, the MPFIS performs a georeferenced documentation of occurred events as well as a systematic risk analysis, which at the same time forms the foundation of the proposed EWS. Based on a case study in the German city of Aachen and the event of 29 May 2018, the operation principle of the integrated information system is illustrated.

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 Assessment of an Extreme Rainfall Detection System for Flood Prediction over Queensland (Australia)

Proceedings ◽  
2019 ◽  
Vol 18 (1) ◽  
pp. 1
Author(s):  
Paola Mazzoglio ◽  
Francesco Laio ◽  
Constantin Sandu ◽  
Piero Boccardo
Keyword(s):  
Real Time ◽  
Detection System ◽  
Extreme Rainfall ◽  
Early Warning Systems ◽  
Remotely Sensed ◽  
Flood Event ◽  
Management Service ◽  
Remotely Sensed Data ◽  
The Impact ◽  
Rainfall Detection

Flood events represent some of the most catastrophic natural disasters, especially in localities where appropriate measurement instruments and early warning systems are not available. Remotely sensed data can often help to obtain near real-time rainfall information with a global spatial coverage without the limitations that characterize other instruments. In order to achieve this goal, a freely accessible Extreme Rainfall Detection System (ERDS—erds.ithacaweb.org) was developed and implemented by ITHACA with the aim of monitoring and forecasting exceptional rainfall events and providing information in an understandable way for researchers as well as non-specialized users. The near real-time rainfall monitoring is performed by taking advantage of NASA GPM (Global Precipitation Measurement) IMERG (Integrated Multi-satellite Retrievals for GPM) half-hourly data (one of the most advanced rainfall measurements provided by satellite). This study aims to evaluate ERDS performance in the detection of the extreme rainfall that led to a massive flood event in Queensland (Australia) between January and February 2019. Due to the impressive amount of rainfall that affected the area, Flinders River (one of the longest Australian rivers) overflowed, expanding to a width of tens of kilometers. Several cities were also partially affected and Copernicus Emergency Management Service was activated with the aim of providing an assessment of the impact of the event. In this research, ERDS outputs were validated using both in situ and open source remotely sensed data. Specifically, taking advantage of both NASA MODIS (Moderate-resolution Imaging Spectroradiometer) and Copernicus Sentinel datasets, it was possible to gain a clear look at the full extent of the flood event. GPM data proved to be a reliable source of rainfall information for the evaluation of areas affected by heavy rainfall. By merging these data, it was possible to recreate the dynamics of the event.

scholarly journals Design and research on the fatigue detection system of ship bridge duty based on image processing

2021 ◽  
Vol 2131 (3) ◽  
pp. 032119
Author(s):  
Yonggang Zong ◽  
Xiandong Zhao ◽  
Zhongfeng Ba
Keyword(s):  
Image Processing ◽  
Real Time ◽  
Early Warning ◽  
Performance Indicators ◽  
Large Scale ◽  
Detection System ◽  
Warning System ◽  
Technical Standards ◽  
Eyelid Closure ◽  
Fatigue Driving

Abstract With the development of the marine economy, the number of ships is increasing day by day, and is developing towards large-scale, diversified and professional development, and marine accidents caused by driver fatigue have attracted more and more attention. In order to reduce marine traffic accidents caused by fatigue driving of ship drivers and ensure the safety of life and property at sea, it is very necessary and important to study effective methods to detect the fatigue state of ship drivers in real time. This article mainly studies the early warning of ship fatigue driving. In view of the difficulties of the ship fatigue driving detection technology, reasonable performance indicators of the ship anti-fatigue driving image processing and early warning system are proposed; according to the system performance indicators, the HOG+SVM method is determined to automatically track the human face, and the human eye detection and tracking method is designed. Improved the method of eyelid closure to determine fatigue. In order to determine the eye opening and closing state or blinking frequency. The PERCLOS method is used to determine whether the driver is tired, and a warning is given when the ship’s watch driver is tired. The system has the characteristics of non-contact, real-time, etc. and complies with the relevant technical standards of the International Maritime Organization (IMO) on the ship bridge fatigue warning system (BNWAS).

scholarly journals Assessment of an Extreme Rainfall Detection System for Flood Prediction over Queensland (Australia)

Proceedings ◽  
2019 ◽  
Vol 18 (1) ◽  
pp. 1
Author(s):  
Paola Mazzoglio ◽  
Francesco Laio ◽  
Constantin Sandu ◽  
Piero Boccardo
Keyword(s):  
Real Time ◽  
Detection System ◽  
Extreme Rainfall ◽  
Early Warning Systems ◽  
Remotely Sensed ◽  
Flood Event ◽  
Management Service ◽  
Remotely Sensed Data ◽  
The Impact ◽  
Rainfall Detection

Flood events represent some of the most catastrophic natural disasters, especially in localities where appropriate measurement instruments and early warning systems are not available. Remotely sensed data can often help to obtain near real-time rainfall information with a global spatial coverage without the limitations that characterize other instruments. In order to achieve this goal, a freely accessible Extreme Rainfall Detection System (ERDS—erds.ithacaweb.org) was developed and implemented by ITHACA with the aim of monitoring and forecasting exceptional rainfall events and providing information in an understandable way for researchers as well as non-specialized users. The near real-time rainfall monitoring is performed by taking advantage of NASA GPM (Global Precipitation Measurement) IMERG (Integrated Multi-satellite Retrievals for GPM) half-hourly data (one of the most advanced rainfall measurements provided by satellite). This study aims to evaluate ERDS performance in the detection of the extreme rainfall that led to a massive flood event in Queensland (Australia) between January and February 2019. Due to the impressive amount of rainfall that affected the area, Flinders River (one of the longest Australian rivers) overflowed, expanding to a width of tens of kilometers. Several cities were also partially affected and Copernicus Emergency Management Service was activated with the aim of providing an assessment of the impact of the event. In this research, ERDS outputs were validated using both in situ and open source remotely sensed data. Specifically, taking advantage of both NASA MODIS (Moderate-resolution Imaging Spectroradiometer) and Copernicus Sentinel datasets, it was possible to gain a clear look at the full extent of the flood event. GPM data proved to be a reliable source of rainfall information for the evaluation of areas affected by heavy rainfall. By merging these data, it was possible to recreate the dynamics of the event.

scholarly journals Rainfall Threshold Estimation and Landslide Forecasting for Kalimpong, India Using SIGMA Model

Water ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 1195 ◽  
Author(s):  
Minu Treesa Abraham ◽  
Neelima Satyam ◽  
Sai Kushal ◽  
Ascanio Rosi ◽  
Biswajeet Pradhan ◽  
...  
Keyword(s):  
Early Warning ◽  
Sigma Model ◽  
Warning System ◽  
Daily Rainfall ◽  
Early Warning Systems ◽  
Rainfall Threshold ◽  
Integrated System ◽  
Warning Systems ◽  
Rainfall Forecasting ◽  
Rainfall Thresholds

Rainfall-induced landslides are among the most devastating natural disasters in hilly terrains and the reduction of the related risk has become paramount for public authorities. Between the several possible approaches, one of the most used is the development of early warning systems, so as the population can be rapidly warned, and the loss related to landslide can be reduced. Early warning systems which can forecast such disasters must hence be developed for zones which are susceptible to landslides, and have to be based on reliable scientific bases such as the SIGMA (sistema integrato gestione monitoraggio allerta—integrated system for management, monitoring and alerting) model, which is used in the regional landslide warning system developed for Emilia Romagna in Italy. The model uses statistical distribution of cumulative rainfall values as input and rainfall thresholds are defined as multiples of standard deviation. In this paper, the SIGMA model has been applied to the Kalimpong town in the Darjeeling Himalayas, which is among the regions most affected by landslides. The objectives of the study is twofold: (i) the definition of local rainfall thresholds for landslide occurrences in the Kalimpong region; (ii) testing the applicability of the SIGMA model in a physical setting completely different from one of the areas where it was first conceived and developed. To achieve these purposes, a calibration dataset of daily rainfall and landslides from 2010 to 2015 has been used; the results have then been validated using 2016 and 2017 data, which represent an independent dataset from the calibration one. The validation showed that the model correctly predicted all the reported landslide events in the region. Statistically, the SIGMA model for Kalimpong town is found to have 92% efficiency with a likelihood ratio of 11.28. This performance was deemed satisfactory, thus SIGMA can be integrated with rainfall forecasting and can be used to develop a landslide early warning system.

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 Climate Extreme Seeds a New Domoic Acid Hotspot on the US West Coast

2020 ◽  
Vol 2 ◽  
Author(s):  
Vera L. Trainer ◽  
Raphael M. Kudela ◽  
Matthew V. Hunter ◽  
Nicolaus G. Adams ◽  
Ryan M. McCabe
Keyword(s):  
Early Warning ◽  
West Coast ◽  
Domoic Acid ◽  
Large Scale ◽  
Early Warning Systems ◽  
Warning Systems ◽  
Dungeness Crab ◽  
The Pacific ◽  
The Us ◽  
Us West

A heatwave that blanketed the northeast Pacific Ocean in 2013–2015 had severe impacts on the marine ecosystem through altered species composition and survival. A direct result of this marine heatwave was a sustained, record-setting harmful algal bloom (HAB), caused by the toxigenic diatom, Pseudo-nitzschia, that led to an unprecedented delay in harvest opportunity for commercial Dungeness crab (Metacarcinus magister) and closure of other recreational, commercial and tribal shellfish harvest, including razor clams. Samples collected during a cruise in summer 2015, showed the appearance of a highly toxic “hotspot” between Cape Mendocino, CA and Cape Blanco, OR that was observed again during cruises in the summers of 2016–2018. The transport of toxic cells from this retentive site northward during wind relaxations or reversals associated with storms resulted in economically debilitating delay or closure of Dungeness crab harvest in both northern California and Oregon in 2015–2019. Analyses of historic large-scale Pseudo-nitzschia HABs have shown that these events occur during warm periods such as El Niño, positive phases of the Pacific Decadal Oscillation, or the record-setting marine heatwave. In order to reduce the impacts of large-scale HABs along the west coast of North America, early warning systems have been developed to forewarn coastal managers. These early warning systems include the Pacific Northwest and California HAB Bulletins, both of which have documented elevated domoic acid and increased risk associated with the northern California hotspot. These early warnings enable mitigative actions such as selective opening of safe harvest zones, increased harvest limits during low risk periods, and early harvest in anticipation of impending HAB events. The aims of this study are to show trends in nearshore domoic acid along the US west coast in recent years, including the recent establishment of a new seed bed of highly-toxic Pseudo-nitzschia, and to explore how early warning systems are a useful tool to mitigate the human and environmental health and economic impacts associated with harmful algal blooms.

Laboratory Experiment Study on Real-Time Monitoring and Safety Early Warning System of Structure Settlement and Deformation

2015 ◽  
Vol 775 ◽  
pp. 264-267
Author(s):  
Xiao Dong Pan ◽  
Lei Zhao
Keyword(s):  
Real Time ◽  
Early Warning ◽  
Early Warning Systems ◽  
Transport Layer ◽  
Measuring Instruments ◽  
Test Scheme ◽  
Static Force ◽  
Force Level ◽  
Real Time Monitoring ◽  
Time Deformation

Currently the settlement and deformation of factory building structure is monitored using total stations and other more conventional measuring instruments, it is difficult to reflect the health of the structure timely and accurately. In order to change the situation, we establish a set of system for real-time monitoring of deformation and safety warning. The system is formed of sensing layer, transport layer and application layer. Sensing layer is composed of static force level and biaxial inclinometer. The system can be used in dynamic real-time factory structure safety monitoring, also applied to other similar structural monitoring. This paper will study the system components and principle, early warning systems grading, calculation of real-time deformation of roof frame, laboratory test scheme and verification. Experiments showed that the system is suitable for the actual factory structure monitoring, while the choice of static force level and biaxial inclinometer of precision to meet the requirements.

Sign in / Sign up

Export Citation Format

Share Document