A data assimilation procedure for operational prediction of storm surge in the northern Adriatic Sea

Lionello, Piero; Sanna, Antonella; Elvini, E.; Mufato, R.

doi:10.1016/j.csr.2006.01.003

Cited by 47 publications

(35 citation statements)

References 15 publications

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“…Lionello et al 2006;Canizares et al 2001;Yu 2012;Karri et al 2014) instead of real-life (pre-) operational systems. In Yu (2012), the applied process models have a resolution that is modest compared to other models proposed for the same area.…”

Section: Use Of Accurate Operational Modelsmentioning

confidence: 99%

“…Canizares et al 2001;Lionello et al 2006), the impact of the data assimilation scheme is assessed in hindcast mode only, with observations feeding into the assimilation scheme available throughout the computation. In the present paper, we supplement the impact on hindcast quality by an assessment of the impact on the forecasting skill, for various lead times.…”

Section: Use Of Accurate Operational Modelsmentioning

confidence: 99%

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Application of data assimilation for improved operational water level forecasting on the northwest European shelf and North Sea

2015

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For the Netherlands, accurate water level forecasting in the coastal region is crucial, since large areas of the land lie below sea level. During storm surges, detailed and timely water level forecasts provided by an operational storm surge forecasting system are necessary to support, for example, the decision to close the movable storm surge barriers in the Eastern Scheldt and the Rotterdam Waterway. In the past years, a new generation operational tide-surge model (Dutch Continental Shelf Model version 6) has been developed covering the northwest European continental shelf. In a previous study, a large effort has been put in representing relevant physical phenomena in this process model as well as reducing parameter uncertainty over a wide area. While this has resulted in very accurate water level representation (root-meansquare error (RMSE) ∼7-8 cm), during severe storm surges, the errors in the meteorological model forcing are generally non-negligible and can cause forecast errors of several decimetres. By integrating operationally available observational data in the forecast model by means of real-time data assimilation, the errors in the meteorological forcing are prevented from propagating to the hydrodynamic tide-surge model forecasts. This paper discusses the development of a computationally efficient steady-state Kalman filter to enhance the predictive quality for the shorter lead times by improving the system state at the start of the forecast. Besides evaluating the model quality against shelf-wide tide gauge observations for a year-long hindcast simulation, the predictive value of the Kalman filter is determined by comparing the forecast quality for various lead time intervals against the model without a steady-state Kalman filter. This shows that, even though the process model has a water level representation that is substantially better than that of other comparable operational models of this scale, substantial improvements in predictive quality in the first few hours are possible in an actual operational setting.

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Section: Use Of Accurate Operational Modelsmentioning

confidence: 99%

Section: Use Of Accurate Operational Modelsmentioning

confidence: 99%

Application of data assimilation for improved operational water level forecasting on the northwest European shelf and North Sea

2015

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“…They were progressively improved in time, from the simplest versions that only relied on the time series of the past, to the newest ones that take into account the predicted meteorological parameters (typically ECMWF pressure) and are "expert systems" capable of selecting a suitable set of coefficients, depending on the meteorological conditions. After 2002, hydrodynamic models were also operationally implemented at ICPSM, in particular a finite element model of the Mediterranean Sea, the SHYFEM model developed at ISMAR-CNR of Venice (Bajo et al, 2007), and a finite difference model of the Adriatic Sea, the HYPSE model developed at the University of Padua (Lionello et al, 2006). Both hydrodynamic models are forced by ECMWF pressure and wind fields.…”

Section: The Use Of Wave Information For Tidal Forecastmentioning

confidence: 99%

The Henetus wave forecast system in the Adriatic Sea

Bertotti

Canestrelli²,

Cavaleri

et al. 2011

Nat. Hazards Earth Syst. Sci.

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Abstract. We describe the Henetus wave forecast system in the Adriatic Sea. Operational since 1996, the system is continuously upgraded, especially through the correction of the input ECMWF wind fields. As these fields are of progressively improved quality with the increasing resolution of the meteorological model, the correction needs to be correspondingly updated. This ensures a practically constant quality of the Henetus results in the Adriatic Sea since 1996. After suitable and extended validation of the quality of the results at different forecast ranges, the operational range has been recently extended to five days. The Henetus results are used also to improve the tidal forecast on the Venetian coasts and the Venice lagoon, particularly during the most severe events. Extensive statistics on the model performance are provided, both as analysis and forecast, by comparing the model results versus both satellite and buoy data.

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“…Both practical methods were later included in a common general framework for data assimilation in hydrodynamic models, the COmmon Set of Tools for the Assimilation of Data (COSTA) (van Velzen and Verlaan 2007), as well as the Ensemble Kalman Filter (EnKF). Other approaches to assimilate data for storm-surge operational prediction considered adjoint techniques (Li et al 2013;Lionello et al 2006) and variational data assimilation (Peng and Xie 2006).…”

Section: Introductionmentioning

confidence: 99%

An investigation of ensemble-based assimilation of satellite altimetry and tide gauge data in storm surge prediction

2015

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Cyclogenesis and long-fetched winds along the southeastern coast of South America may lead to floods in populated areas, as the Buenos Aires Province, with important economic and social impacts. A numerical model (SMARA) has already been implemented in the region to forecast storm surges. The propagation time of the surge in such extensive and shallow area allows the detection of anomalies based on observations from several hours up to the order of a day prior to the event. Here, we investigate the impact and potential benefit of storm surge level data assimilation into the SMARA model, with the objective of improving the forecast. In the experiments, the surface wind stress from an ensemble prediction system drives a storm surge model ensemble, based on the operational 2-D depth-averaged SMARA model.

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A data assimilation procedure for operational prediction of storm surge in the northern Adriatic Sea

Cited by 47 publications

References 15 publications

Application of data assimilation for improved operational water level forecasting on the northwest European shelf and North Sea

Application of data assimilation for improved operational water level forecasting on the northwest European shelf and North Sea

The Henetus wave forecast system in the Adriatic Sea

An investigation of ensemble-based assimilation of satellite altimetry and tide gauge data in storm surge prediction

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