Performance of the Adriatic early warning system during the multi-meteotsunami event of 11–19 May 2020: an assessment using energy banners

Abstract. This study quantifies the performance of the Croatian meteotsunami early warning system (CMeEWS) composed of a network of air pressure and sea level observations, a high-resolution atmosphere-ocean modelling suite and a stochastic surrogate model. The CMeEWS, which is not operational due to a lack of numerical resources, is used retroactively to reproduce the multiple events observed in the eastern Adriatic between the 11th and 19th of May 2020. It is found that deterministic simulations largely fail to forecast these extreme events at endangered locations along the Croatian cost mostly due to a systematic northwestward shift of the atmospheric disturbances. Additionally, the use of combined ocean and atmospheric model results, instead of atmospheric model results only, is not found to improve the selection of the transects used to extract the atmospheric parameters feeding the stochastic meteotsunami surrogate model. Finally, in operational mode, the stochastic surrogate model would have triggered the warnings for most of the observed events, but also setting off some false alarms. Due to the uncertainties associated with operational modelling of meteotsunamigenic disturbances, the stochastic approach has thus proven to overcome the failures of the deterministic forecasts and should be further developed.

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Performance of the Adriatic early warning system during the multi-meteotsunami event of 11–19 May 2020: an assessment using energy banners

Natural Hazards and Earth System Science ◽

10.5194/nhess-21-2427-2021 ◽

2021 ◽

Vol 21 (8) ◽

pp. 2427-2446

Author(s):

Iva Tojčić ◽

Cléa Denamiel ◽

Ivica Vilibić

Keyword(s):

Sea Level ◽

Early Warning ◽

Surrogate Model ◽

Early Warning System ◽

Warning System ◽

Atmospheric Model ◽

Air Pressure ◽

False Alarms ◽

Deterministic Models ◽

And Sea Level

Abstract. This study quantifies the performance of the Croatian meteotsunami early warning system (CMeEWS) composed of a network of air pressure and sea level observations, a high-resolution atmosphere–ocean modelling suite, and a stochastic surrogate model. The CMeEWS, which is not operational due to a lack of numerical resources, is used retroactively to reproduce the multiple events observed in the eastern Adriatic between 11 and 19 May 2020. The performances of the CMeEWS deterministic models are then assessed with an innovative method using energy banners based on temporal and spatial spectral analysis of the high-pass-filtered air pressure and sea level fields. It is found that deterministic simulations largely fail to forecast these extreme events at endangered locations along the Croatian coast, mostly due to a systematic northwestward shift of the atmospheric disturbances. Additionally, the use of combined ocean and atmospheric model results, instead of atmospheric model results only, is not found to improve the selection of the transects used to extract the atmospheric parameters feeding the stochastic meteotsunami surrogate model. Finally, in operational mode, the stochastic surrogate model would have triggered the warnings for most of the observed events but also set off some false alarms. Due to the uncertainties associated with operational modelling of meteotsunamigenic disturbances, the stochastic approach has thus proven to overcome the failures of the deterministic forecasts and should be further developed.

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Early Warning System for Financial Crises

Risk and Contingency Management ◽

10.4018/978-1-5225-3932-2.ch001 ◽

2018 ◽

pp. 1-25 ◽

Cited By ~ 1

Author(s):

Amir Manzoor

Keyword(s):

Financial Crisis ◽

Early Warning ◽

Financial Crises ◽

Early Warning System ◽

Financial Stability ◽

Time Horizon ◽

Warning System ◽

False Alarms ◽

Financial Risks ◽

Policy Makers

To maintain financial stability, prevention of financial crisis is very important. This prevention is especially is especially important for developing countries where we need robust instruments for prediction of financial crises. One such instrument is Early Warning System (EWS). An EWS provided signals that could reflect the likelihood of a financial crisis over a given time horizon. Changing nature of financial risks due to liberalization of economies has increased the importance of an effective EWS. This chapter explores the state of the art of EWS. It is suggested that policy makers should take into account their objectives and related thresholds of various while developing an EWS since there exists a sharp trade-off between correctly calling crises and false alarms.

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Brief communication: Using averaged soil moisture estimates to improve the performances of a regional-scale landslide early warning system

Natural Hazards and Earth System Science ◽

10.5194/nhess-18-807-2018 ◽

2018 ◽

Vol 18 (3) ◽

pp. 807-812 ◽

Cited By ~ 37

Author(s):

Samuele Segoni ◽

Ascanio Rosi ◽

Daniela Lagomarsino ◽

Riccardo Fanti ◽

Nicola Casagli

Keyword(s):

Soil Moisture ◽

Early Warning ◽

Early Warning System ◽

Preliminary Investigation ◽

Regional Scale ◽

Back Analysis ◽

Warning System ◽

False Alarms ◽

Rainfall Thresholds ◽

Landslide Early Warning

Abstract. We communicate the results of a preliminary investigation aimed at improving a state-of-the-art RSLEWS (regional-scale landslide early warning system) based on rainfall thresholds by integrating mean soil moisture values averaged over the territorial units of the system. We tested two approaches. The simplest can be easily applied to improve other RSLEWS: it is based on a soil moisture threshold value under which rainfall thresholds are not used because landslides are not expected to occur. Another approach deeply modifies the original RSLEWS: thresholds based on antecedent rainfall accumulated over long periods are substituted with soil moisture thresholds. A back analysis demonstrated that both approaches consistently reduced false alarms, while the second approach reduced missed alarms as well.

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Brief communication: Using punctual soil moisture estimates to improve the performances of a regional scale landslide early warning system

10.5194/nhess-2017-361 ◽

2017 ◽

Cited By ~ 1

Author(s):

Samuele Segoni ◽

Ascanio Rosi ◽

Daniela Lagomarsino ◽

Riccardo Fanti ◽

Nicola Casagli

Keyword(s):

Soil Moisture ◽

Early Warning ◽

Early Warning System ◽

Regional Scale ◽

Back Analysis ◽

Warning System ◽

Threshold Value ◽

False Alarms ◽

Rainfall Thresholds ◽

Landslide Early Warning

Abstract. We improved a state-of-art RSLEWS (regional scale landslide early warning system) based on rainfall thresholds by integrating punctual soil moisture estimates. We tested two approaches. The simplest can be easily applied to improve other RSLEWS: it is based on a soil moisture threshold value under which rainfall thresholds are not used because landslides are never expected to occur. Another approach deeply modifies the original RSLEWS: thresholds based on antecedent rainfall accumulated over long periods were substituted by soil moisture thresholds. A back analysis demonstrated that both approaches reduced consistently false alarms, while the second approach reduced missed alarms as well.

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Early Warning System for Financial Crises

Advances in Finance, Accounting, and Economics - Handbook of Research on Financial and Banking Crisis Prediction through Early Warning Systems ◽

10.4018/978-1-4666-9484-2.ch002 ◽

2016 ◽

pp. 22-45

Author(s):

Amir Manzoor

Keyword(s):

Financial Crisis ◽

Early Warning ◽

Financial Crises ◽

Early Warning System ◽

Financial Stability ◽

Time Horizon ◽

Warning System ◽

False Alarms ◽

Financial Risks ◽

Policy Makers

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Flood forecasting using quantitative precipitation forecasts and hydrological modeling in the Sebeya catchment, Rwanda

H2Open Journal ◽

10.2166/h2oj.2021.094 ◽

2021 ◽

Author(s):

Mukakarangwa Assoumpta ◽

Daniel Aja

Keyword(s):

Early Warning ◽

Early Warning System ◽

Lead Time ◽

Hydrological Modeling ◽

Model Performance ◽

Warning System ◽

Flood Forecasting ◽

False Alarms ◽

Time Flow ◽

Quantitative Precipitation Forecasts

Abstract The absence of a viable flood early warning system for the Sebeya River catchment continues to impede government efforts toward improving community preparedness, the reduction of flood impacts and relief. This paper reports on a recent study that used satellite data, quantitative precipitation forecasts and the rainfall–runoff model for short-term flood forecasting in the Sebeya catchment. The global precipitation measurement product was used as a satellite rainfall product for model calibration and validation and forecasted European Centre Medium-Range Weather Forecasts (ECMWF) rainfall products were evaluated to forecast flood. Model performance was evaluated by the visual examination of simulated hydrographs, observed hydrographs and a number of performance indicators. The real-time flow forecast assessment was conducted with respect to three different flood warning threshold levels for a 3–24-h lead time. The result for a 3-h lead time showed 72% of hits, 7.5% of false alarms and 9.5% of missed forecasts. The number of hits decreased, as the lead time increased. This study did not consider the uncertainties in observed data, and this can influence the model performance. This work provides a base for future studies to establish a viable flood early warning system in the study area and beyond.

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