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This paper reviews the state of the art in storm surge forecasting and its particular application in the northern Adriatic Sea. The city of Venice already depends on operational storm surge forecasting systems to warn the population and economy of imminent flood threats, as well as help to protect the extensive cultural heritage. This will be more important in the future, with the new mobile barriers called MOSE (MOdulo Sperimentale Elettromeccanico, Experimental Electromechanical Module) that will be completed by 2021. The barriers will depend on accurate storm surge forecasting to control their operation. In this paper, the physics behind the flooding of Venice is discussed, and the state of the art of storm surge forecasting in Europe is reviewed. The challenges for the surge forecasting systems are analyzed, especially in view of uncertainty. This includes consideration of selected historic extreme events that were particularly difficult to forecast. Four potential improvements are identified: (1) improve meteorological forecasts, (2) develop ensemble forecasting, (3) assimilation of water level measurements and (4) develop a multimodel approach.

The primary source of guidance used by the Storm Surge Unit (SSU) at the National Hurricane Center (NHC) for issuing storm surge watches and warnings is the Probabilistic Tropical Storm Surge model (P-Surge). P-Surge is an ensemble of Sea, Lake, and Overland Surges from Hurricanes (SLOSH) model forecasts that is generated based on historical error distributions from NHC official forecasts. A probabilistic framework is used for operational storm surge forecasting to account for uncertainty related to the tropical cyclone track and wind forcing. Previous studies have shown that the size of a storm’s wind field is an important factor that can affect storm surge. A simple radius of maximum wind (RMW) prediction scheme was developed to forecast RMW based on NHC forecast parameters. Verification results indicate this scheme is an improvement over the RMW forecasts used by previous versions of P-Surge. To test the impact of the updated RMW forecasts in P-Surge, retrospective cases were selected from 25 storms from 2008 to 2020 that had an adequate number of observations. Evaluation of P-Surge forecasts using these improved RMW forecasts shows that the probability of detection is higher for most probability of exceedance thresholds. In addition, the forecast reliability is improved, and there is an increase in the number of high probability forecasts for extreme events at longer lead times. The improved RMW forecasts were recently incorporated into the operational version of P-Surge (v2.9), and serve as an important step toward extending the lead time of skillful and reliable storm surge forecasts.

The German Bight coastline is regularly affected by storm surges driven by extratropical cyclones. Decadal‐scale predictions of local surges would foster coastal protection and decision making in affected areas. We examine the prediction skill of the Max‐Planck‐Institute Earth System Model (MPI‐ESM) decadal prediction system for three different storm surge metrics at Cuxhaven (Germany), Esbjerg (Denmark), and Delfzijl (The Netherlands). To avoid dynamical downscaling from the coarse model output to local surge heights, we use machine learning and train a neural network on observed surge heights and reanalyzed fields of mean sea‐level pressure (MSLP). We apply this network to MSLP output of our prediction system to generate decadal predictions of surge heights. The prediction system falls short of generating skillful predictions for high water event durations and individual lead years in general, but windows for more skillful predictions arise for deterministic predictions at longer multi‐year lead times. Plain Language Summary In the German Bight, storms regularly push water masses toward the coast, causing so‐called storm surges at the coastline. Forecasting whether these storm surges will occur more or less often over the next couple of years would be very helpful for the long‐term planning of coastal protection. However, current climate models, which are used to predict the global climate over the next years, are not able to produce forecasts on such a small regional level. To overcome this problem, we use machine learning to build a translator which can translate the large‐scale behavior of the atmosphere over Europe to the regional water level at Cuxhaven, Esbjerg, and Delfzijl, three locations on the German Bight coastline. We use this translator to quickly turn forecasts from a global climate model into forecasts of storm surges at the German Bight coastline. We then analyze how good these storm surge forecasts are for different years in the future. We find that the forecasts work better when we try to predict the mean climate over multiple years than when we make a forecast for one specific year. Key Points We train a neural network to translate large‐scale mean sea‐level pressure to surge heights at three locations in the German Bight We apply the neural network to mean sea‐level pressure output of a decadal prediction system The generated decadal predictions of surges show considerable prediction skill for deterministic and some for probabilistic predictions

The Bay of Bengal is a well-known breeding ground to some of the deadliest cyclones in history. Despite recent advancements, the complex morphology and hydrodynamics of this large delta and the associated modelling complexity impede accurate storm surge forecasting in this highly vulnerable region. Here we present a proof of concept of a physically consistent and computationally efficient storm surge forecasting system tractable in real time with limited resources. With a state-of-the-art wave-coupled hydrodynamic numerical modelling system, we forecast the recent Supercyclone Amphan in real time. From the available observations, we assessed the quality of our modelling framework. We affirmed the evidence of the key ingredients needed for an efficient, real-time surge and inundation forecast along this active and complex coastal region. This article shows the proof of the maturity of our framework for operational implementation, which can particularly improve the quality of localized forecast for effective decision-making over the Bengal delta shorelines as well as over other similar cyclone-prone regions.

Storm surges are the most severe type of marine disaster affecting the Guangdong–Hong Kong–Macao Greater Bay Area (GBA), and storm surge forecasting under the background of typhoons remains challenging. In this paper, we propose an operational coupling model (including the global–regional assimilation and prediction system [GRAPES] atmospheric model and the finite volume coastal ocean model [FVCOM]) to predict typhoon-induced storm surges in the GBA, namely, the Greater Bay Area Storm Surge Prediction System (GBASSP), and verified its performance. The highest horizontal resolution of the GBASSP is 80 m, and it has the following advantages. (i) It can provide early warning and forecasting for storm surge at least 2 days before typhoon landfall. (ii) For the next 24-hour forecast of a single typhoon, the maximum storm surge error is only 5 cm, while the mean absolute error of the maximum storm surge of the GBASSP is 19.7 cm. The difference in the occurrence time of the maximum storm surge between observations and the GBASSP is within 1 h. (iii) Comprehensively compared to other storm surge prediction models, the GBASSP performs well and has the best forecasting skills. The relative and root mean square errors of the GBASSP are 5.9% and 21 cm, respectively, the smallest of all the comparative models used in this study. In addition, the average absolute error is between those of the other models.

Timely and accurate forecasting of storm surges can effectively prevent typhoon storm surges from causing large economic losses and casualties in coastal areas. At present, numerical model forecasting consumes too many resources and takes too long to compute, while neural network forecasting lacks regional data to train regional forecasting models. In this study, we used the DUAL wind model to build typhoon wind fields, and constructed a typhoon database of 75 processes in the northern South China Sea using the coupled Advanced Circulation–Simulating Waves Nearshore (ADCIRC-SWAN) model. Then, a neural network with a Res-U-Net structure was trained using the typhoon database to forecast the typhoon processes in the validation dataset, and an excellent storm surge forecasting effect was achieved in the Pearl River Estuary region. The storm surge forecasting effect of stronger typhoons was improved by adding a branch structure and transfer learning.

A real-time forecast model of surface hydrodynamics in Lake Ontario (Coastlines-LO) was developed to automatically predict storm surges and surface waves. The system uses a dynamically coupled Delft3D–SWAN model with a structured grid to generate 48 h predictions for the lake that are updated every 6 h. The lake surface is forced with meteorological data from the High Resolution Deterministic Prediction System (HRDPS). The forecast model has been running since May 2021, capturing a wide variety of storm conditions. Good agreement between observations and modelled results is achieved, with root mean squared errors (RMSEs) for water levels and waves under 0.02 and 0.26 m, respectively. During storm events, the magnitude and timing of storm surges are accurately predicted at nine monitoring stations (RMSE <0.05 m), with model accuracy either improving or remaining consistent with decreasing forecast length. Forecast significant wave heights agree with observed data (1 %–12 % relative error for peak wave heights) at four wave buoys in the lake. Coastlines-LO forecasts for storm surge prediction for two consecutive storm events were compared to those from the Great Lakes Coastal Forecasting System (GLCFS) to further evaluate model performance. Both systems achieved comparable results with average RMSEs of 0.02 m. Coastlines-LO is an open-source wrapper code driven by open data and has relatively low computational requirements compared to GLCFS, making this approach suitable for forecasting marine conditions in other coastal regions.

Accurate storm surge forecasting is vital for saving lives and avoiding economic and infrastructural damage. Failure to accurately predict storm surge can have catastrophic repercussions. Advances in machine learning models show the ability to improve accuracy of storm surge prediction by leveraging vast amounts of historical and realtime data such as weather and tide patterns. This paper proposes a bidirectional attention-based LSTM storm surge architecture (BALSSA) to improve prediction accuracy. Training and evaluation utilized extensive meteorological and tide level data from 77 typhoon incidents in Hong Kong and Macao between 2017 and 2022. The proposed methodology is able to model complex non-linearities between large amounts of data from different sources and identify complex relationships between variables that are typically not captured by traditional physical methods. BALSSA effectively resolves the problem of long-term dependencies in storm surge prediction by the incorporation of an attention mechanism. It enables selective emphasis on significant features and boosts the prediction accuracy. Evaluation has been conducted using real-world datasets from Macao to validate our storm surge prediction model. Results show that accuracy and robustness of predictions were significantly improved by the incorporation of attention mechanisms in our models. BALSSA captures temporal dynamics effectively, providing highly accurate storm surge forecasts (MAE: 0.0126, RMSE: 0.0003) up to 72 h in advance. These findings have practical significance for disaster risk reduction strategies, saving lives through timely evacuation and early warnings. Experiments comparing BALSSA variations with other machine learning algorithms consistently validate BALSSA’s superior predictive performance. It offers an additional risk management tool for civil-protection agencies and governments, as well as an ideal solution for enhancing storm surge prediction accuracy, benefiting coastal communities.

Even though the maximum wind radius (Rmax) is an important parameter in determining the intensity and size of tropical cyclones, it has been overlooked in previous storm surge studies. This study reviews the existing estimation methods for Rmax based on central pressure or maximum wind speed. These over- or underestimate Rmax because of substantial variations in the data, although an average radius can be estimated with moderate accuracy. As an alternative, we propose an Rmax estimation method based on the radius of the 50 kt wind (R50). Data obtained by a meteorological station network in the Japanese archipelago during the passage of strong typhoons, together with the JMA typhoon best track data for 1990–2013, enabled us to derive the following simple equation, Rmax  =  0.23 R50. Application to a recent strong typhoon, the 2015 Typhoon Goni, confirms that the equation provides a good estimation of Rmax, particularly when the central pressure became considerably low. Although this new method substantially improves the estimation of Rmax compared to the existing models, estimation errors are unavoidable because of fundamental uncertainties regarding the typhoon's structure or insufficient number of available typhoon data. In fact, a numerical simulation for the 2013 Typhoon Haiyan as well as 2015 Typhoon Goni demonstrates a substantial difference in the storm surge height for different Rmax. Therefore, the variability of Rmax should be taken into account in storm surge simulations (e.g., Rmax  =  0.15 R50–0.35 R50), independently of the model used, to minimize the risk of over- or underestimating storm surges. The proposed method is expected to increase the predictability of major storm surges and to contribute to disaster risk management, particularly in the western North Pacific, including countries such as Japan, China, Taiwan, the Philippines, and Vietnam.

Accurate prediction of storm surges is crucial for mitigating the impact of extreme weather events. This paper introduces the Bidirectional Attention-based Long Short-Term Memory (LSTM) Storm Surge Architecture, BALSSA, addressing limitations in traditional physical models. By leveraging machine learning techniques and extensive historical and real-time data, BALSSA significantly enhances prediction accuracy. Utilizing a bidirectional attention-based LSTM framework, it captures complex, non-linear relationships and long-term dependencies, improving the accuracy of storm surge predictions. The enhanced model, D-BALSSA, further amplifies predictive capability through a doubled bidirectional attention-based structure. Training and evaluation involve a comprehensive dataset from over 70 typhoon incidents in Macao between 2017 and 2022. The results showcase the outstanding performance of BALSSA, delivering highly accurate storm surge forecasts with a lead time of up to 72 h. Notably, the model exhibits a low Mean Absolute Error (MAE) of 0.0287 m and Root Mean Squared Error (RMSE) of 0.0357 m, crucial indicators measuring the accuracy of storm surge predictions in water level anomalies. These metrics comprehensively evaluate the model’s accuracy within the specified timeframe, enabling timely evacuation and early warnings for effective disaster mitigation. An adaptive system, integrating real-time alerts, tropical cyclone (TC) chaser, and prospective visualizations of meteorological and tidal measurements, enhances BALSSA’s capabilities for improved storm surge prediction. Positioned as a comprehensive tool for risk management, BALSSA supports decision makers, civil protection agencies, and governments involved in disaster preparedness and response. By leveraging advanced machine learning techniques and extensive data, BALSSA enables precise and timely predictions, empowering coastal communities to proactively prepare and respond to extreme weather events. This enhanced accuracy strengthens the resilience of coastal communities and protects lives and infrastructure from the escalating threats of climate change.