scholarly journals AN EARLY WARNING SYSTEM FOR THE ON-LINE PREDICTION OF COASTAL STORM RISK ON THE ITALIAN COASTLINE

2012 ◽  
Vol 1 (33) ◽  
pp. 77 ◽  
Author(s):  
Mitchell D. Harley ◽  
Andrea Valentini ◽  
Clara Armaroli ◽  
Paolo Ciavola ◽  
Luisa Perini ◽  
...  
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Numerical Models ◽  
Warning System ◽  
End Users ◽  
Impact Indicator ◽  
On Line ◽  
A Chain ◽  
One Year ◽  
The One

The ability to predict the imminent arrival of coastal storm risks is a valuable tool for civil protection agencies in order to prepare themselves and, if needs be, execute the appropriate hazard-reduction measures. In this study we present a prototype Early Warning System (EWS) for coastal storm risk on the Emilia-Romagna coastline in Northern Italy. This EWS is run by executing a chain of numerical models (SWAN, ROMS and XBeach) daily, with the final output transformed into a format suitable for decision making by end-users. The storm impact indicator selected for this site is the Safe Corridor Width (SCW), which is a measure of how much dry beach width is available for safe passage by beach users. A three-day time-series of the predicted SCW is generated daily by the prototype EWS. If the minimum SCW exceeds a certain threshold, a warning is issued to end-users via an automated email service. All available prediction information is also updated daily on-line. Over the one year that the EWS has been operating (June 2011 until June 2012), 13 “code red” and 16 “code orange” warnings have been issued, with the remaining 305 predictions indicating low hazard in terms of the SCW. The reliability of the predictions from the perspective of the end-user has meant that the EWS is currently being expanded to include the entire Emilia-Romagna coastline.

scholarly journals Brief communication "Landslide Early Warning System: toolbox and general concepts"

2013 ◽  
Vol 13 (1) ◽  
pp. 85-90 ◽  
Author(s):  
E. Intrieri ◽  
G. Gigli ◽  
N. Casagli ◽  
F. Nadim
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Early Warning Systems ◽  
End Users ◽  
Flow Chart ◽  
Warning Systems ◽  
Limited Experience ◽  
Practical Guidelines ◽  
Landslide Early Warning

Abstract. We define landslide Early Warning Systems and present practical guidelines to assist end-users with limited experience in the design of landslide Early Warning Systems (EWSs). In particular, two flow chart-based tools coming from the results of the SafeLand project (7th Framework Program) have been created to make them as simple and general as possible and in compliance with a variety of landslide types and settings at single slope scale. We point out that it is not possible to cover all the real landslide early warning situations that might occur, therefore it will be necessary for end-users to adapt the procedure to local peculiarities of the locations where the landslide EWS will be operated.

scholarly journals PREDIKTIBILITAS CURAH HUJAN DIURNAL DI PULAU JAWA MENGGUNAKAN MODEL WRF [PREDICTIBILITY OF DIURNAL RAINFALL OVER JAVA ISLAND USING WRF]

2017 ◽  
Vol 14 (2) ◽  
pp. 57
Author(s):  
NFN Suaydhi
Keyword(s):  
Diurnal Cycle ◽  
Early Warning ◽  
Confidence Level ◽  
Early Warning System ◽  
Wrf Model ◽  
Warning System ◽  
Madden Julian Oscillation ◽  
Diurnal Cycles ◽  
Diurnal Rainfall ◽  
One Year

Indonesian region often experiences hydrometeorological disasters such as floods and landslides. To mitigate the losses from such disasters, an early warning system is needed. PSTA LAPAN has developed an early warning system called SADEWA (Satellite Disaster Early Warning System). The performance of this early warning system needs to be evaluated in order to increase the confidence level. The evaluation of the WRF performance in producing the prediction was carried out by analyzing the diurnal cycles of rainfall over Java and its surroundings using the results of WRF predictions implemented in SADEWA and GSMaP data for one year period (Maret 2014 Februari 2015). The contrasting diurnal cycles between Java island and its surrounding seas could be well simulated by the WRF model, both the amount and the frequency of the rainfall. However, the phase of diurnal cycle from the WRF prediction leads that of the observation by two hours and the amplitude of the simulated diurnal cycle is higher than the observed. The results also show that the WRF predictions could not simulate the effects of MJO (Madden-Julian Oscillation) on the diurnal cycles of rainfall over Java.ABSTRAKWilayah Indonesia sering mengalami bencana hidrometeorologi seperti banjir dan tanah longsor. Untuk mengurangi kerugian yang diakibatkan oleh kejadian bencana meteorologi diperlukan suatu sistem peringatan dini. PSTA LAPAN telah mengembangkan sebuah sistem peringatan dini yang diberi nama SADEWA (Satellite Disaster Early Warning System). Kinerja sistem peringatan dini seperti ini perlu dievaluasi agar tingkat kepercayaannya meningkat. Evaluasi kinerja hasil prediksi ini dilakukan dengan menganalisis siklus diurnal curah hujan di pulau Jawa dan sekitarnya pada data hasil prediksi WRF yang digunakan dalam SADEWA dan data GSMaP selama satu tahun (Maret 2014 Februari 2015). Siklus diurnal curah hujan yang kontras antara pulau Jawa dengan lautan sekitarnya mampu disimulasikan dengan baik oleh model WRF, baik dari jumlah maupun frekuensi curah hujannya. Namun fasa diurnal dari hasil prediksi WRF mendahului fasa data pengamatan sekitar dua jam dan mempunyai amplitudo lebih besar. Hasil analisis juga menunjukkan hasil prediksi WRF belum mampu mensimulasikan pengaruh MJO (Madden-Julian Oscillation) pada siklus diurnal curah hujan di Jawa.

scholarly journals Can an early warning system help minimize the impacts of coastal storms? A case study of the 2012 Halloween storm, Northern Italy

2015 ◽  
Vol 3 (5) ◽  
pp. 3409-3448 ◽  
Author(s):  
M. D. Harley ◽  
A. Valentini ◽  
C. Armaroli ◽  
L. Perini ◽  
L. Calabrese ◽  
...  
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Numerical Models ◽  
Warning System ◽  
Northern Italy ◽  
Water Levels ◽  
Storm Event ◽  
Forecast Performance ◽  
High Hazard ◽  
Key Sites

Abstract. The Emilia-Romagna Early Warning System (ER-EWS) is a state-of-the-art coastal forecasting system that comprises a series of numerical models (COSMO, ROMS, SWAN and XBeach) to obtain a daily three-day forecast of coastal storm hazard at eight key sites along the Emilia-Romagna coastline (Northern Italy). On the night of 31 October 2012, a major storm event occurred that resulted in elevated water levels (equivalent to a 1-in-20 to 1-in-50-year event) and widespread erosion and flooding. Since this storm happened just one month prior to the roll-out of the ER-EWS, the forecast performance related to this event is unknown. The aim of this study was to therefore reanalyse the ER-EWS as if it had been operating a day before the event and determine to what extent the forecasts may have helped reduce storm impacts. Three different reanalysis modes were undertaken: (1) a default forecast (DF) mode based on three-day wave and water-level forecasts and default XBeach parameters, (2) a "perfect" offshore (PO) forecast mode using measured offshore values and default XBeach parameters; and (3) a calibrated XBeach (CX) mode using measured offshore values and an optimized parameter set obtained through an extensive calibration process. The results indicate that while a "code red" alert would have been issued for the DF mode, an underprediction of the extreme water levels of this event limited high-hazard forecasts to only two of the eight ER-EWS sites. Forecasts based on measured offshore conditions (the PO mode) more-accurately indicate high hazard conditions for all eight sites. Further considerable improvements are observed using an optimized XBeach parameter set (the CX mode) compared to default parameters. A series of what-if scenarios at one of the sites show that artificial dunes, which are a common management strategy along this coastline, could have hypothetically been constructed as an emergency procedure to potentially reduce storm impacts.

Developing Strategies in the Sharing Economy

2022 ◽  
pp. 1224-1245
Author(s):  
Ramona Diana Leon
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Early Warning ◽  
Early Warning System ◽  
Business Models ◽  
Warning System ◽  
Sharing Economy ◽  
Knowledge Based ◽  
Artificial Neural ◽  
The One

The sharing economy is challenging the traditional business models and strategies by encouraging collaboration, non-ownership, temporal access, and redistribution of goods and/or services. Within this framework, the current chapter aims to examine how managers influence, voluntarily or involuntarily, the reliability of a managerial early warning system, based on an artificial neural network. The analysis focuses on seven Romanian sustainable knowledge-based organizations and brings forward that managers tend to influence the results provided by a managerial early warning system based on artificial neural network, voluntarily and involuntarily. On the one hand, they are the ones who consciously decide which departments and persons are involved in establishing the structure of the managerial early warning system. On the other hand, they unconsciously influence the structure of the managerial early warning system through the authority they exercise during the managerial debate.

scholarly journals STORM EARLY WARNING SYSTEM AS A LAST PLUG-IN OF A REGIONAL OPERATIONAL OCEANOGRAPHY SYSTEM: THE CASE OF THE GULF OF CADIZ

2012 ◽  
Vol 1 (33) ◽  
pp. 54 ◽  
Author(s):  
Theocharis A. Plomaritis ◽  
Javier Benavente ◽  
Laura Del Rio ◽  
Emma Reyes ◽  
Carlos Dastis ◽  
...  
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Model Output ◽  
Civil Protection ◽  
Gulf Of Cadiz ◽  
Indicator Approach ◽  
Operational Oceanography ◽  
Gulf Of Cádiz ◽  
On Line

The implementation of an Early Warning System for storm impacts in the urban beach of Cadiz is presented. The model train is described in detail together with the downscaling procedure. Emphasis is given on how the morphodynamic model receives the necessary information from the regional operational oceanography system specifically developed for the Gulf of Cadiz. The translation of the morphodynamic model output into useful information for the civil protection and other responsible authorities is provided based on the coastal state indicator approach. All the intermediate and final results are presented in a purpose-based on-line application.

scholarly journals Can an early-warning system help minimize the impacts of coastal storms? A case study of the 2012 Halloween storm, northern Italy

2016 ◽  
Vol 16 (1) ◽  
pp. 209-222 ◽  
Author(s):  
M. D. Harley ◽  
A. Valentini ◽  
C. Armaroli ◽  
L. Perini ◽  
L. Calabrese ◽  
...  
Keyword(s):  
Water Level ◽  
Early Warning ◽  
Early Warning System ◽  
Numerical Models ◽  
Warning System ◽  
Northern Italy ◽  
Water Levels ◽  
Storm Event ◽  
Forecast Performance ◽  
High Hazard

Abstract. The Emilia-Romagna early-warning system (ER-EWS) is a state-of-the-art coastal forecasting system that comprises a series of numerical models (COSMO, ROMS, SWAN and XBeach) to obtain a daily 3-day forecast of coastal storm hazard at eight key sites along the Emilia-Romagna coastline (northern Italy). On the night of 31 October 2012, a major storm event occurred that resulted in elevated water levels (equivalent to a 1-in-20- to 1-in-50-year event) and widespread erosion and flooding. Since this storm happened just 1 month prior to the roll-out of the ER-EWS, the forecast performance related to this event is unknown. The aim of this study was to therefore reanalyse the ER-EWS as if it had been operating a day before the event and determine to what extent the forecasts may have helped reduce storm impacts. Three different reanalysis modes were undertaken: (1) a default forecast (DF) mode based on 3-day wave and water-level forecasts and default XBeach parameters; (2) a measured offshore (MO) forecast mode using wave and water-level measurements and default XBeach parameters; and (3) a calibrated XBeach (CX) mode using measured boundary conditions and an optimized parameter set obtained through an extensive calibration process. The results indicate that, while a "code-red" alert would have been issued for the DF mode, an underprediction of the extreme water levels of this event limited high-hazard forecasts to only two of the eight ER-EWS sites. Forecasts based on measured offshore conditions (the MO mode) more-accurately indicate high-hazard conditions for all eight sites. Further considerable improvements are observed using an optimized XBeach parameter set (the CX mode) compared to default parameters. A series of what-if scenarios at one of the sites show that artificial dunes, which are a common management strategy along this coastline, could have hypothetically been constructed as an emergency procedure to potentially reduce storm impacts.

scholarly journals A Coastal Flood Early-Warning System Based on Offshore Sea State Forecasts and Artificial Neural Networks

2021 ◽  
Vol 9 (11) ◽  
pp. 1272
Author(s):  
Michalis Chondros ◽  
Anastasios Metallinos ◽  
Andreas Papadimitriou ◽  
Constantine Memos ◽  
Vasiliki Tsoukala
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Sea Water ◽  
Numerical Models ◽  
Methodological Approach ◽  
Warning System ◽  
Sea State ◽  
Flood Risks ◽  
Coastal Flood ◽  
Artificial Neural

An integrated methodological approach to the development of a coastal flood early-warning system is presented in this paper to improve societal preparedness for coastal flood events. The approach consists of two frameworks, namely the Hindcast Framework and the Forecast Framework. The aim of the former is to implement a suite of high-credibility numerical models and validate them according to past flooding events, while the latter takes advantage of these validated models and runs a plethora of scenarios representing distinct sea-state events to train an Artificial Neural Network (ANN) that is capable of predicting the impending coastal flood risks. The proposed approach was applied in the flood-prone coastal area of Rethymno in the Island of Crete in Greece. The performance of the developed ANN is good, given the complexity of the problem, accurately predicting the targeted coastal flood risks. It is capable of predicting such risks without requiring time-consuming numerical simulations; the ANN only requires the offshore wave characteristics (height, period and direction) and sea-water-level elevation, which can be obtained from open databases. The generic nature of the proposed methodological approach allows its application in numerous coastal regions.

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