ADCIRC Research Articles

Future projections of storm surge in Hurricane Katrina and sensitivity to meteorological forcing resolution

In this study, we investigated whether and how the storm surge induced by Hurricane Katrina could change if it occurs in a future warmer climate, and the sensitivity of the changes to atmospheric forcing resolution. Climate model simulations of Hurricane Katrina at 27 km, 4.5 km, and 3 km resolutions were used to drive storm surge simulations in historical and future climates using the ADvanced CIRCulation (ADCIRC) model. We found that peak surge height increased significantly in the future with all forcing resolutions. However, the future projection is 22% greater in the 3 km forcing, typical of regional climate models, compared to the 27 km forcing, typical of state-of-the-art global climate models. Additionally, the spatial extent of the future change is highly sensitive to forcing resolution, extending most broadly under the 27 km forcing. Furthermore, we found that storm surge duration decreases in the future with all forcing resolutions due to increasing TC translation speed and decreasing ocean lifetime. However, the future change in the surge duration is sensitive to the forcing resolution, decreasing by 31% in the 27 km forcing and 6% in the 3 km forcing.

Prediction of storm surge in the Pearl River Estuary based on data-driven model

Storm surges, a significant coastal hazard, cause substantial damage to both property and lives. Precise and efficient storm surge models are crucial for long-term risk assessment and guiding emergency management decisions. While high-fidelity dynamic models offer accurate predictions, their computational costs are substantial. Hence, recent efforts focus on developing data-driven storm surge surrogate models. This study focuses on the Pearl River Estuary in Guangdong Province. Initially, the dynamic ADvanced CIRCulation (ADCIRC) model was utilized to construct storm surge data for 16 historical typhoons, serving as training, validation, and testing data for data-driven models. Subsequently, Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), and Informer deep learning (DL) models were employed for forecasting of storm surge over the next 1h, 3h, 6h, 12h, and 18h. Finally, Shapley Additive exPlanations (SHAP) values were used for interpretability analysis of the input factors across different models. Results indicated that the proposed DL storm surge prediction model can effectively replicate the dynamic model’s simulation results in short-term forecasts, significantly reducing computational costs. This model offers valuable scientific assistance for future coastal storm surge forecasts in the Greater Bay Area.

Estimation of future storm surge inundation and effects of the adaptation measures in Qingdao, China

The ADvanced CIRCulation (ADCIRC) model was applied to the sea area surrounding Qingdao, China, to estimate the impact of climate change on storm surge and inundation induced by eight typical typhoons in current and future scenarios. Model results were validated using the available observations. These results indicate that the storm surge caused by typhoons with different intensities and paths has various effects on the Qingdao area. Furthermore, measurement results of the storm inundation area in the future scenario revealed that, in any case, with the future rise in sea level, the typhoon intensity, the inundation area, and the storm surge intensity increase. In response to changes in the storm surge and the actual situation in Qingdao, this study emphasized that coastal flooding is caused by the storm surge. Three adaptation measure scenarios were set as follows: the current level of adaptation measures (CLA), the continuation level of adaptation measures (CA), and the enhanced level of adaptation measures (EA). Based on different scenarios, the CA will reduce most of the inundation area caused by coastal flooding compared with the CLA. EA is the most significant in reducing inundation, demonstrating a decrease in inundation of more than 50%. This research has shown that good coastal protection will reduce most coastal flooding, and building dams will be considered a significant adaptation measure.

A new surge index with the incorporation of cyclone track approach angle information for the Bay of Bengal

A novel tropical cyclone surge potential index (TCSPI) for estimating tropical cyclone (TC) induced potential peak surge levels near the landfall locations for the entire coast of the Bay of Bengal (BoB) is presented. The proposed TCSPI incorporates parameters that are often readily available in real-time for BoB: maximum sustained wind speed (Vmax), the radius of given wind (R), translation speed (S), approach angle (θ), coastal geometry (a, b), and bathymetry information (L30). The inclusion of approach angle and associated coastal geometry information led to improvements of the TCSPI to other existing surge indices. A retrospective analysis of TCSPI using the Indian Meteorological Department (IMD) and Joint Typhoon Warning Center (JTWC) tropical cyclone (TC) best track data from 1990 to 2021 for BoB suggests that the index captures historical events reasonably well. In particular, it explains up to ∼90% of observed surge variance and up to ∼92% of those peak surge values obtained from physics-based numerical simulation using the Advanced Circulation (ADCIRC) model. Further, we utilized renowned regression-based supervised machine learning (ML) models on small data samples. The Linear Regression (LR), Regression Tree (TR), Support Vector Machine (SVM), Ensembles of Trees (EnTR), and Gaussian Process Regression (GPR) models are incorporated that can rapidly predict peak surge height across an extensive coastal region of BoB, using the same input parameter information (i.e., Vmax, R, S,L30, and θ) that have been considered in the TCSPI at the landfall time frame. Finally, the observed peak surge values associated with historical TC events in the BoB are compared with those estimated by TCSPI, ADCIRC, and ML models. The errors associated with TCSPI are comparatively less. The consideration of approach angle information in the TCSPI has improved the accuracy of the overall prediction of peak surge height associated with historical TCs by up to 17% and 19% for observed and ADCIRC-simulated peak surges, respectively. The development of such a prediction methodology can reduce the numerical computation requirements to improve the surge hazard maps, and hence, proper implementation based on surge risk is possible.

EVALUATION OF STORM SURGE BEHAVIOR DUE TO DIFFERENT TYPHOON TRACKS AND WIND SPEEDS ALONG THE COAST OF DAGUPAN CITY, LINGAYEN GULF, PHILIPPINES

Every year, an average of 19.4 typhoons traverse the Philippine Area of Responsibility and the country receives around nine landfalling typhoons. These typhoons can generate storm surges along the coasts and cause inundation of coastal communities. Dagupan City is a low-lying city located along the coast of Lingayen Gulf where 30% of its population live in the coastal barangays. As a coastal community located along the head of the Lingayen Gulf where the bathymetry is shallow, Dagupan city is susceptible to storm surges. This study aims to evaluate the storm surge behavior due to different typhoon tracks and windspeeds along the coast of Dagupan City. To achieve this objective, this study implements numerical simulation of storm surges using the Advanced Circulation (ADCIRC) model. A simple methodology is employed by selecting representative historical typhoons and creating synthetic typhoons by shifting the tracks along the latitude. Simulations of typhoons with different typhoon tracks reveal the critical tracks that give highest storm surges in Dagupan City. Finally, storm surges are simulated for windspeed intensities of 60, 80 and 100 knots which are applied to the identified critical tracks. The results of this study show that shifting the typhoon tracks affects the magnitude of storm surges in Dagupan City. Generally, the tracks that pass near the center of Lingayen Gulf generate the highest storm surge along the coast, however, eastward synthetic tracks near the mouth of the gulf can also potentially produce storm surges in Dagupan City. In addition, it is found that typhoon tracks coming from the West Philippine Sea can generate higher storm surges in Dagupan City compared to tracks from the Pacific Ocean. The highest storm surge generated by the representative historical typhoon is 1 m produced by Typhoon Vicki 1998 while a storm surge of 2 m could be potentially generated along Dagupan City by a typhoon with windspeed of 100 knots. The results of this study can be helpful in predicting storm surges for different typhoon tracks and windspeeds which can be used for coastal disaster preparedness in Dagupan City.

A study of the potential effects of deepening the Corpus Christi Ship Channel on hurricane storm surge

We present a case study of the potential impact on hurricane storm surge due to deepening the Corpus Christi Ship Channel through Aransas Pass in the region of Corpus Christi and Port Aransas, Texas. Our investigation is based on numerical mathematical models of the circulation of coastal water due to Hurricane Harvey and a synthetic hurricane derived from Harvey. The circulation models are based on the numerical solution of the shallow water equations using finite element and finite difference techniques. The shallow water equations are solved within the framework of the widely used ADvanced CIRCulation (ADCIRC) numerical model. From the models, we ascertain maximum storm surge as well as elevation time series at critical locations in the study region. To assess the effects of a deeper channel, we compare the model outputs for current and proposed future channels. The model results indicate that the changes to maximum storm surge magnitude are small and in large portions of the study area the models indicate a reduction in surge magnitude. However, there are some local areas where the models results show an increase of up to 30 centimeters in a synthetic extreme storm scenario.

DEVELOPMENT OF MESHES FOR COMPOUND FLOOD SIMULATION FOR THE TEXAS COAST

The Texas coast is subjected to frequent storms leading to floods in the yearly hurricane season. To provide forecasts of hurricane storm surge, a shallow water equation solver, the Advanced CIRCulation model, Luettich (1992) is used. The current state-of-the-art ADCIRC meshes for the Texas coast used in forecasting were developed about a decade ago, see, Hope (2013). ADCIRC uses unstructured meshes that require significant tailoring to ensure accurate and physically relevant solutions. The current mesh used in forecasting has very high resolution on the Texas coast for accuracy and numerical stability, whereas areas far away from Texas are of low resolution for computational efficiency. The applicability and accuracy of these meshes is regularly verified to be used in operational storm surge forecasting.

A Study on the Improvement of Wave and Storm Surge Predictions Using a Forecasting Model and Parametric Model: a Case Study on Typhoon Chaba

High waves and storm surges due to tropical cyclones cause great damage in coastal areas; therefore, accurately predicting storm surges and high waves before a typhoon strike is crucial. Meteorological forcing is an important factor for predicting these catastrophic events. This study presents an improved methodology for determining accurate meteorological forcing. Typhoon Chaba, which caused serious damage to the south coast of South Korea in 2016, was selected as a case study. In this study, symmetric and asymmetric parametric vortex models based on the typhoon track forecasted by the Model for Prediction Across Scales (MPAS) were used to create meteorological forcing and were compared with those models based on the best track. The meteorological fields were also created by blending the meteorological field from the symmetric / asymmetric parametric vortex models based on the MPAS-forecasted typhoon track and the meteorological field generated by the forecasting model (MPAS). This meteorological forcing data was then used given to two-way coupled tide-surge-wave models: Advanced CIRCulation (ADCIRC) and Simulating Waves Nearshore (SWAN). The modeled storm surges and waves correlated well with the observations and were comparable to those predicted using the best track. Based on our analysis, we propose using the parametric model with the MPAS-forecasted track, the meteorological field from the same forecasting model, and blending them to improve storm surge and wave prediction.

Understanding the Effects of Wind Intensity, Forward Speed, and Wave on the Propagation of Hurricane Harvey Surges

Hurricane storm surges are influenced by wind intensity, forward speed, width and slope of the ocean bottom, central pressure, angle of approach, shape of coastal lines, local features, and storm size. A numerical experiment is conducted using the Advanced Circulation + Simulation and Simulating Waves Nearshore (ADCIRC + SWAN) coupled model for understanding the effects of wind intensity, forward speed, and wave on the storm surges caused by Hurricane Harvey. The ADCIRC + SWAN is used to simulate hurricane storm surges and waves. The wind fields of Hurricane Harvey were reconstructed from observed data, aided by a variety of methodologies and analyses conducted by Ocean Weather Inc (OWI) after the event. These reconstructed wind fields were used as the meteorological forcing in the base case in ADCIRC+SWAN to investigate the storm surges caused by the hurricane. Hurricane Harvey was the second most costly hurricane in the United States, causing severe urban flooding by dropping more than 60 inches of rainfall in Texas. The hurricane made three landfalls, with its first landfall as a Category 4 based on the Saffir–Simpson Hurricane Wind Scale (SSHWS), with wind intensities of 212.98 km/h (59 m/s). The storm surges caused by Hurricane Harvey were unique due to the slow speed, crooked tracks, triple landfalls in the USA, and excessive rain. The model’s storm surge and wave results were compared against observed data. High water marks at 21 locations and time series at 12 National Oceanic and Atmospheric Administration (NOAA) gauges were compared with the generated results. Several cases were investigated by increasing or decreasing the wind intensity or hurricane forward speed by 25% of the OWI wind and pressure data. The effects of the wave were analyzed by comparing the results obtained from ADCIRC + SWAN (with waves) and ADCIRC (without waves) models. The study found that the changes in wind intensity had the most significant effect on storm surges, followed by wave and forward speed changes. This study signifies the importance of considering these factors to enhance accuracy in predicting storm surges.

Parameter estimation with maximal updated densities

A recently developed measure-theoretic framework solves a stochastic inverse problem (SIP) for models where uncertainties in model output data are predominantly due to aleatoric (i.e., irreducible) uncertainties in model inputs (i.e., parameters). The subsequent inferential target is a distribution on parameters. Another type of inverse problem is to quantify uncertainties in estimates of “true” parameter values under the assumption that such uncertainties should be reduced as more data are incorporated into the problem, i.e., the uncertainty is considered epistemic. A major contribution of this work is the formulation and solution of such a parameter identification problem (PIP) within the measure-theoretic framework developed for the SIP. The approach is novel in that it utilizes a solution to a stochastic forward problem (SFP) to update an initial density only in the parameter directions informed by the model output data. In other words, this method performs “selective regularization” only in the parameter directions not informed by data. The solution is defined by a maximal updated density (MUD) point where the updated density defines the measure-theoretic solution to the PIP. Another significant contribution of this work is the full theory of existence and uniqueness of MUD points for linear maps with Gaussian distributions. Data-constructed Quantity of Interest (QoI) maps are also presented and analyzed for solving the PIP within this measure-theoretic framework as a means of reducing uncertainties in the MUD estimate. We conclude with a demonstration of the general applicability of the method on two problems involving either spatial or temporal data for estimating uncertain model parameters. The first problem utilizes spatial data from a stationary partial differential equation to produce a MUD estimate of an uncertain boundary condition. The second problem utilizes temporal data obtained from the state-of-the-art ADvanced CIRCulation (ADCIRC) model to obtain a MUD estimate of uncertain wind drag coefficients for a simulated extreme weather event near the Shinnecock Inlet located in the Outer Barrier of Long Island, NY, USA.

Establishing connectivity patterns of eastern oysters (Crassostrea virginica) on regional oceanographic scales

AbstractLocalized depletion of marine and estuarine populations often results from large‐scale natural and anthropogenic disturbances (e.g., hurricanes, oil spills) as well as overharvest of fisheries resources. Understanding how such localized depletions may affect populations at larger regional scales requires knowledge of connectivity among local populations within the larger regional landscape or metapopulation. Efforts to restore populations following such decreases require similar knowledge. During the 2010 Deepwater Horizon oil spill, dramatic declines of oysters occurred throughout estuaries west and east of the Mississippi River. We examined trajectories of particles, which were parameterized to mimic oyster larvae, using the ADvanced CIRCulation (ADCIRC; https://adcirc.org) model to evaluate potential connectivity within and among embayments from Western Louisiana to Alabama. Patterns of larval settlement, which we defined as the intersection of a larval particle with known or expected oyster habitat at any point 13–21 days post‐release, reflected much greater local contributions, with 50%–90% of settled particles originating within the same subdivision of embayments. Exchange among subdivision was much less (0%–40%) and settlement originating from outside the embayment of release was trivial under most scenarios (0%–14%). Connectivity between adjacent basins was greatest for larvae released in the southern portions of the embayment, whereas connectivity among nonadjacent basins was not predicted under the scenarios modeled. Because most local populations are relatively isolated on ecological timescales, areas suffering from extensive local depletion are likely to require extensive time to recover due to the lack of larval subsidy from the overall regional population. Restoration would require building stepping‐stone populations or reefs within and among basin to restore a high degree of connectivity.

A STUDY ON THE TIDAL BEHAVIOR OF THE IZMIR BAY WITH AN OPEN-SOURCE MODEL

In this paper, tidal behavior of Izmir Bay modeled by open source ADCIRC - Advanced Circulation Model. The source code of the ADCIRC installed on a local computer, the preparation of the files will be used as input required for the model simulation, the creation of the calculation grid, the coefficients representing the open boundary conditions, and the obtaining of the bathymetry data were made and cloud-based and open-access DesignSafe CI environment was also used to run the simulations. The results are compared with observed data at Izmir Mentes Mareograph Station and simulations of different time intervals and periods were graphed with the help of FORTRAN based FigureGen visualization tool.

“SeaWARRDD”: Coastal Warning and Rapid Response Data Density: Rethinking Coastal Ocean Observing, Intelligence, Resilience, and Prediction

Abstract Florida has a rich coastal and offshore biodiversity and ecology, and its low-lying geography with three dynamic coastlines is unique in many respects. Millions of people are attracted to visit, live, and work in the region. The same unique qualities make Florida highly exposed to impact-weather events, climate change, sea level rise, and environmental interference from exploding population growth over the last few decades. Environmental conditions must be monitored, baselines formed, and advanced circulation and ecosystem models created and verified (in-situ). The SeaWARRDD team discusses the proposed implementation of a comprehensive “Florida Coastal Ocean Observing System” beginning with a pilot study along the inner-West Florida Shelf. Our SeaWARRDD team brings decades of experience to the ocean-observing community, from the federal, state, academic, and private sectors including designing, developing, installing, and maintaining ocean (bay and estuary) monitoring and data collection systems. The SeaWARRDDobserving technologies are described in their application to monitor impact-weather, the structure of water-column density (conductivity, temperature, depth/ocean heat content), water-quality parameters, harmful algal blooms, acidification, and met-ocean physical components. Also discussed is the engagement with new ocean technologies and artificial intelligence, machine learning, and neural networks as they progress from concept, to prototype, and onto operational status. SeaWARRDD takes ocean-data processing to higher levels within the observing community and opens new avenues to provide both direct and indirect benefits to the millions of people who live along the Florida coast.

Amplification of regional tides in response to sea level

The study investigates the temporal change in tides under the background of rapid sea level rise, for a highly vulnerable coastal region. It highlights the existence of non-astronomic tidal variability at seasonal and secular time-scales in the Ganga-Brahmaputra-Meghna (GBM) delta, along with increasing low and high tidal levels. The observed variability in semi-diurnal tides was found to be coherent to the mean sea level changes. M2 tide can be a proxy for sea level changes as they show a direct relationship. Steric changes were introduced in the barotropic ADCIRC (ADvanced CIRCulation) model and the results proved that change in water depth modulates the energetics of the tidal wave thereby causing amplification. A regionally varying tidal response was noticed, such that the regions associated with the GBM delta showed maximum amplification. The factors that can cause tidal amplification are region-dependent and coastal geomorphology plays an important role in it. Tidal prediction based on changing sea level was able to capture the observed seasonal modulation of tides and increasing trend in low and high tidal levels. Incorporating the effect of changing tides can improve the accuracy of tidal predictions and will help advance studies regarding regional sea level change, coastal flooding and tide-surge interaction.

Finite Element Modeling of Tsunami-induced Water Levels and Associated Inundation Extent: A Case Study of the 26th December 2004 Indian Ocean Tsunami

Abstract In recorded history, the tsunami due to the Great Sumatra earthquake of 26th December 2004 has been the most devastating, causing the loss of over 230,000 lives besides extensive damage to most of the coastal provinces bordering the Indian Ocean. Real-time prediction of tsunami wave heights and resultant inundation of inland coastal areas are essential to safeguard the life and property of the coastal community. In the present work, a finite-element-based ADvanced CIRCulation (ADCIRC) model (widely used for storm surge simulations) is used to compute tsunami wave height and associated coastal inundation due to the 2004 event. The prime focus is on the Cuddalore coast along the east coast of India, which was heavily affected due to the tsunami. A grid resolution of around 50 m was used in the Cuddalore region to simulate the inundation in the area. Computed results are validated against the available observations. It is noticed that the model computations are in good agreement with the observations. Simulation results depict that because of its computational efficiency and accuracy, the ADCIRC can be used for near real-time prediction across the tsunami warning centers.

The utilization of physically based models and GIS techniques for comprehensive risk assessment of storm surge: A case study of Huizhou

Quantitative analyses of storm surge risk, which are mostly focused on physical vulnerability, have been widely used to help coastal communities mitigate impacts and damage. Such assessments, however, overlook the social aspect of vulnerability in storm surge risk. By considering both the community’s social vulnerability and buildings’ physical vulnerability, this study proposed a methodology that incorporates social vulnerability into the framework for making quantitative risk assessments of storm surge using a coupled hydrodynamic and wave model, Geographical Information System (GIS) techniques, and the Principal Component Analysis (PCA) method. The coastal area of Huizhou was chosen as the case study due to its high concentration of oil and gas infrastructure in China’s southern Guangdong Province. By combining hazard, exposure, physical vulnerability, and social vulnerability, it was possible to explore the effect of social vulnerability on the physical vulnerability-based risk assessment of storm surge and determine the overall risk level. First, the Gumbel distribution was utilized to establish five representative and plausible hypothetical typhoon events with different return periods (10, 20, 50, 100, and 1000 years) for the study area. Then, using the well-validated fully-coupled model, the Simulating Waves Nearshore (SWAN) model and the ADvanced CIRCulation (ADCIRC) model, storm surge simulations for defined return periods were run, and the geographical distribution of the maximum surge elevations displayed on a GIS platform was used to assess hazard levels. In terms of the physical aspect, the depth-damage functions for buildings were established to estimate direct economic losses and assign risk levels accordingly. For the social vulnerability of a community, a composite score was computed using the PCA method by combining and aggregating indicators representing various characteristics of the social group. The results show that the overall risk level, taking into account both social vulnerability and physical vulnerability, has decreased on average. It suggests that social vulnerability-based risk assessment may account for a significant portion of the overall risk assessment, which is frequently overlooked in traditional storm surge risk assessment. Additionally, the comprehensive and precise risk maps can assist local policymakers in identifying areas at different risk levels and developing evacuation plans, thereby minimizing potential losses, especially in high-risk areas.

A literature review of supply chain development: Evidence from agricultural industry

In the context of the increasing demand for safety and quality of fresh produce, currently two major supply chain models for agricultural products are analyzed (E-commerce Platforms and Agricultural-Supermarket jointing model) and thus a dual-channel agricultural supply chain is proposed by combining the advantages of both. First of all, this paper analyzes the characteristics of different agricultural-supermarket jointing models in the United States, Japan and China, and at the same time compares the advanced agricultural products circulation models in the United States and Japan, puts forward the shortcomings of agricultural-supermarket jointing models in China, and makes suggestions for its future development, then analyze several current e-commerce operation models of fresh produce in China and point out the advantages and disadvantages of e-commerce platforms. Finally, using the case study of Freshippo, we further demonstrate that the dual-channel model plays a huge role in fresh produce.

Physics-based sizing functions for ocean-to-estuary shallow-water models

Modeling ocean processes requires a discrete representation of the system geometry, which itself contains multiple degrees of freedom, especially for systems of large geographical scale. This paper assesses the unstructured meshing requirements for a large-scale, astronomic-tide model domain of the western North Atlantic Ocean, Gulf of Mexico and Caribbean Sea with telescopic focus of the South Atlantic Bight (United States southeastern seaboard) and its estuaries. The methodologies of localized truncation error analysis (LTEA), which account only for the model interior and LTEA plus complex derivatives (+CD), which account for the model interior and boundary, are exploited due to their foundations stemming directly from tidal physics. LTEA and LTEA+CD are applied for the M2 tidal harmonics resulting from linear and nonlinear solutions of the ADCIRC (advanced circulation) model based on a highly resolved uniform mesh. The resultant target element size distributions are interrogated distinctively for the inshore (estuarine) region of the South Atlantic Bight, the inlet-punctuated coastline, the shelf-slope-rise triad and deep ocean, and the open-ocean boundary. The relatively large size of the LTEA computational molecule makes it the suitable choice for the deep ocean, offshore and shelf regions where the tidal dynamics are predominantly linear and smoothly changing in space. LTEA+CD is the logical choice for the shallows and estuaries because of the spatial coverage afforded by the combined utility of interior and boundary estimators (computational molecules), as well as its accounting for the nonlinearities in the evaluation of local truncation error. The combination of the two methods enables an almost complete coverage of the domain for generating spacing requirements, with LTEA+CD extending beyond the interior-only definition of LTEA to capture the inshore regions and model boundaries. Implementing the approach with LTEA and LTEA+CD to target finer resolution in areas with sharper gradients in the hydrodynamics, while relaxing resolution in areas with smoother hydrodynamics, resulted in a mesh with a far-reduced number of elements (∼1.3M) relative to an overly resolved uniform mesh (>10M elements) that is comparable in accuracy with ±5% tidal amplitude error for 99% of the domain.

Applicability Study of a Global Numerical Weather Prediction Model MPAS to Storm Surges and Waves in the South Coast of Korea

The south coast of Korea is vulnerable to coastal disasters, such as storm surges, high waves, wave overtopping, and coastal flooding caused by typhoons. It is imperative to predict such disastrous events accurately in advance, which requires accurate meteorological forcing for coastal ocean modeling. In this study, to acquire accurate meteorological data as important forcing variables for the prediction of storm surges and waves, we examined the forecast performance and applicability of a next-generation global weather/climate prediction model, the Model for Prediction Across Scales (MPAS). We compared the modeled surface pressure and wind with observations on the south coast of Korea for three typhoons that damaged Korea in 2020—Bavi, Maysak, and Haishen—and investigated the accuracy of these observations with the MPAS prediction. Those meteorological forcing variables were then used in the tightly coupled tide-surge-wave model, Advanced CIRCulation (ADCIRC) and the Simulating Waves Nearshore (SWAN) for the simulation of a typhoon-induced storm surge and wave. We also performed the hindcast of the wave and storm surges using a parametric tropical cyclone model, the best-track-based Generalized Asymmetric Holland Model (GAHM) embedded in ADCIRC+SWAN, to better understand the forecast performance and applicability of MPAS. We compared the forecast results of the typhoon-induced wave and storm surges with their hindcast in terms of the time-series and statistical indices for both significant wave height and storm surge height and found that wave and storm surge prediction forced by MPAS forecast provides a comparable accuracy with the hindcast. Comparable results of MPAS forcing with that of hindcast using best track information are encouraging because ADCIRC+SWAN forced by MPAS forecast with an at most four-day lead time still provides a reasonable prediction of wave and storm surges.

Improved wave predictions with ST6 Physics and ADCIRC+SWAN

The Simulating WAves Nearshore (SWAN, Booij et al. 1999) model is used widely for predictions of waves in coastal regions. Like other spectral wave models, SWAN uses parameterizations to represent wave evolution due to sources (e.g. wind), sinks (e.g. whitecapping, bottom friction, depth-limited breaking), and resonance (e.g. quadruplet and triad wave-wave interactions). Each parameterization is based typically on observational data to represent the transfer of energy to, from, and between waves. It is necessary for each term to represent its physical process, but it is also necessary for the terms to be calibrated collectively to represent their combined effects on wave evolution. The calibrated wave predictions can then be coupled with models for circulation and coastal flooding, e.g. ADvanced CIRCulation (ADCIRC, Luettich et al. 1992). SWAN release version 41.20 included a new “package” of wave physics (referred to as ST6 physics). This package has new parameterizations of wind input, whitecapping, swell dissipation, wind speed scaling, and other processes (Rogers et al. 2012). The ST6 physics have been adopted by other wave models (e.g. NOAA’s WaveWatch III, Liu et al. 2019), and it may become the preferred physics package for SWAN. However, because the ST6 physics package has changes to so many parameterizations, it is necessary to quantify its effects on wave predictions. Recent studies (e.g. Aydogan and Ayat 2021) have demonstrated the benefits of using the ST6 physics in the standalone version of SWAN, but its effects have not been quantified for the coupled ADCIRC+SWAN (Dietrich et al. 2011a), which is used for real-time forecasts during impending storms. Do the ST6 physics improve the ADCIRC+SWAN wave predictions?