Parametric Wind Model Research Articles

STUDY OF STORM TIDE MODELING IN THE PEARL RIVER ESTUARY

By reproducing storm tides during nine historical typhoons in the Pearl River Estuary against observations, this paper presents a data-model comparison approach to quantify the uncertainties of three parameters of Holland (1980) parametric wind model. The wind reduction factor that represents effects of mountains and skyscrapers surrounding the estuary may be estimated by finding the minimum RMSE in simulated storm tide peak elevation. The radius to maximum winds can be efficiently adjusted according to observed pressures through the exponential distribution of atmospheric pressure field; and the peakedness is verified on the basis of maximum wind speed versus pressure drop relations. By applying these parameters with the wind model, storm tides in the estuary can be effectively simulated, except for wind conditions where most of the estuary is within the maximum wind radius and winds there are significantly affected by the mountainous lands. Wave setups are noticeable when typhoons made landfall on the right bank of the estuary. As a prerequisite for good model performance, the grid size somewhere in a storm tide model should be smaller than the minimum spacing among soundings points at that location in order to effectively produce the model bathymetry described by the soundings data.

Modified parametric hurricane wind model to improve the asymmetry in the region of maximum winds

The understanding of the ocean waves during extreme hurricane conditions is important for risk analysis in coastal areas, the design of maritime works, the evaluation of impacts on ecosystems, navigation, among many other scientific and engineering applications. Although the Reanalysis databases have improved the quality of the wind fields during the occurrence of those hurricanes for which more data is available, they have been characterized by greatly underestimating the magnitude of the wind speed. In this context, parametric models emerge as an alternative capable of meeting the need for sufficient spatial and temporal resolution, but with a significantly lower computational cost. In this research, a new asymmetric parametric model is presented that presents better results, by incorporating a series of equations that allows improving the asymmetry and the magnitude of maximum wind speed around the eye of the hurricane. This new work presents a broader and more robust validation of this model proposed for the Gulf of Mexico, in which: I) the winds of multiple hurricanes are compared, with several trajectories and categories, with speed and direction records from NOAA in-situ buoys, II) The spatial patterns obtained are compared with the wind fields available in H*wind considered the best existing database, III) the wind fields are employed as forcing of the WAVEWATCH III wave model and the results are used to validated scalar parameters and directional spectrum against information from NOAA in-situ buoys, IV) a detailed analysis is made of the changes suffered by the directional spectrum of the waves when it is forced with the results of the parametric wind model, versus the spectra recorded by NOAA buoys and, V) all previous results derived from the parametric wind model are compared with the information available in the ERA5 reanalysis database and is discussed about it. The results found show that the parametric model is capable of improving the magnitude of the maximum wind speed around the eye, as well as the directional dispersion of energy in the three-dimensional spectrum of the simulated waves under hurricane conditions.

Numerical study of extreme waves driven by synthetic tropical cyclones in the northwest Pacific Ocean

Extreme waves induced by extreme tropical cyclones (TCs) with a very strong intensity threaten marine production and transportation. In this paper, six tropical cyclones with a typical track and extremely strong intensity were synthesized to study the extreme waves. The data on TC tracks were extracted from the China Meteorological Administration. The historic TCs from 1949 to 2018 were classified into six groups according to their tracks, and a representative track was synthesized for each group of TCs. We applied an extremely strong intensity to the tracks for studying the extreme wave field. The synthesized track of typical track cyclones (TTCs) was based on the maximum probability of the translation distance and direction of the TC center according to historical data. The central pressure (Pc) was used to represent the TC intensity and was studied at the recurrence periods of 100, 1,000, and 10,000 years. The synthetic TC with a representative track and extremely strong intensity was called a typical tropical cyclone (TTC). The extreme wave fields driven by TTCs were simulated by Simulating Waves Nearshore (SWAN). The wind field driving the waves was calculated using the Holland parametric wind model and was well-verified with observations. This paper calculated the extreme Hs and Tp values of the return periods of 100, 1,000, and 10,000 years for six types of TTCs. It was found that the extreme Hs values were very different for each TTC. The highest Hs could reach 31.3 m in the recurrence period of 10,000 years. Followed by TTC-I with 18 m, TTC-VI was the weakest with less than 10 m. The TC track position frequency and its spatial variation of extreme intensity were discussed. The synthetic tracks were representative, and the intensity could be influenced by spatial variation. In the end, four historical typhoons with great intensity, wide impact, and serious disaster-causing effects were selected to compare with TTC. This paper can provide guidance for maritime planners.

Assessing the performances of parametric wind models in predicting storm surges in the Pearl River Estuary

Portraying wind fields plays a crucial role in storm surge simulation, which includes the information of the storm track, central maximum wind speed, maximum wind speed radius, and pressure drop. In this study, five widely used parametric wind models and their performances in predicting storm surges have been estimated in the Pearl River Estuary. The synthetic wind field reveals that different wind models produce wind fields with diverse structures, each with its own focus and advantages for the storm's center or its periphery. By implementing these wind models into surge simulation, the wind and surge estimates show a strong dependence on model selection. Selection of parametric wind models may lead to a surge height error greater than 1.45m. The distribution of maximum wind speed and peak water level under various typhoons dominated by storm intensity is also investigated. As the storm intensity increases, the prediction deviations between these selected models are also enlarged and over 10% in Typhoon Hato. Overall, the model proposed by Emanuel and Rotunno (2011) yields accurate estimates among the selected parametric wind models. The uncertainty of the storm track parameters and drag coefficient are quantified based on Emanuel and Rotunno (2011) model. Small track changes can result in huge variations in surge heights with maximum values of 0.5–1m. The choice of drag coefficient also has a maximum difference of 5.4%, which contributes a significant uncertainty to the simulation. The multiple choice of parameters in wind models increases the risk of uncertainty propagation. This study is expected to provide a reference for the numerical modeling of wind field and storm surge.

Relative accuracy of HWRF reanalysis and a parametric wind model during the landfall of Hurricane Florence and the impacts on storm surge simulations

Prediction and reanalysis of storm surge rely on wind and pressure fields from either parametric tropical cyclone wind models or numerical weather model reanalysis, and both are subject to large errors during landfall. This study assesses two sets of wind/pressure fields for Hurricane Florence that made landfall along the Carolinas in September 2018 and appraises the impacts of differential structural errors in the two suites of modeled wind fields on the predictive accuracy of storm surge driven thereby. The first set was produced using Holland 2010 (H10), and the second set is the Hurricane Weather Research and Forecasting (HWRF) reanalysis created by the NWS National Centers for Environmental Prediction (NCEP). Each is validated using a large surface data set collected at public and commercial platforms and then is used as input forcing to a 2-D coastal hydrodynamic model (Delft3D Flexible Mesh) to produce storm surge along the Carolina coasts and major sounds. Major findings include the following. First, wind fields from HWRF are overall more accurate than those based on H10 for the periphery of the storm, though they exhibit limitations in resolving high wind speeds near the center. Second, applying H10 to the best track data for Florence yields an erroneously spike in wind speed on September 15th when the storm reduced to a tropical depression. Third, HWRF wind fields exhibit a progressively negative bias after landfall, likely due to deficiencies of the model in representing boundary layer processes, and to the lack of assimilation of surface product after landfall for compensating for these deficiencies. Fourth, using HWRF reanalysis as the forcings to Delft3D yields more accurate peak surges simulations, though there is severe underestimation of surge along the shoreline close to the track center. The peak surge simulations by Delft3D are biased low when driven by H10, even though over several locations the H10 model clearly overpredicts surface wind speeds. This contrast highlights the importance of resolving wind fields further away from the center in order to accurately reproduce storm surge and associated coastal flooding.

Improvements in wind field hindcast for storm surge predictions in the Bay of Bengal: A case study for the tropical cyclone Varadah

Forecasting the storm surges is crucial to prevent the enormous devastation of coastal ecosystems, habitats, and economic losses. Further, estimating ocean wave parameters in tropical areas is difficult due to the complexity of wind fields associated with tropical cyclones (TCs). Usually, parametric and global model wind fields are the two primary sources to acquire wind and pressure fields to estimate the storm surges. The parametric models usually depict a better wind field near the cyclone center. Unlike the parametric models, the wind fields from global models have better accuracy in the areas far away from the eye of the cyclone. Owing to this inconsistency, a blended wind field approach has been introduced. However, there are no consensus on selecting parametric and background wind models due to the lack of a rigorous assessment of different wind models in the Bay of Bengal (BoB) region. Hence, in this study, five well-known parametric wind models, namely, Jelesnianski (SLOSH), rankine vortex (RV), Willoughby (WL06), Holland (HL80), and generalized asymmetric holland model (GAHM); and, two global wind models, namely, european center for medium-rangeweather forecasts (ECMWF), and weather research forecast (WRF) models were investigated with the available predictions and measurements in BoB. After investigating the best wind model in this region, a detailed analysis was carried out for the TC Varadah in the BoB region. Further, this study provides insight into the deviations in the resulting simulated surge, wave characteristics, and inundation induced by the different atmospheric forcing wind models. The results reveal that a blended wind model based on GAHM and WRF can better simulate the background wind field and the internal structure of the cyclone for the BoB region.

Hydrodynamic Modelling of Storm Surge with Modified Wind Fields along the East Coast of India

Propagation of tropical cyclones and their landfall along the coast affect the livelihood of the coastal community with loss of life, and Bay of Bengal is particularly vulnerable as past disasters have shown. The present study investigates the effects of tropical cyclones namely Phailin, Hudhud and Vardah during its landfall along the East Coast of India. Numerical modelling of storm surges primarily depends on the wind characteristics, for which, the performance of the simulated storm surge from cyclone wind and pressure fields of ECMWF is examined with Telemac-2D. The quality of the wind field is enhanced by applying available wind modification techniques, such as the parametric cyclone wind model superposed with ECMWF wind field, and the direct modification of ECMWF wind field. The superposed wind speed is found in good agreement with the measured wind data. The hydrodynamic simulation was then performed for the cyclonic events for the computation of the storm surge. The predictions agree well with the observed surges for the simulations performed with modified wind fields. The error reduced from 15 cm to 6 cm and model skill improved by 3% leading to a correlation coefficient of 0.98.

A Systematic Study on Berthing Capacity Assessment of Sanya Yazhou Fishing Port by Typhoon Prediction Model

This paper sheds light on the effect of combination modes on the evaluation of berthing capacity for Sanya Yazhou Fishing Port (SYFP) under hypothetical typhoon conditions. By statistically analysing the maximum probability of moving speeds and directions of historical typhoons passing through the fishing port, the representative typhoon path was determined with the nonparametric regression method. The designed typhoon wind fields of levels 12–17 were generated based on Holland’s parametric wind model. Then, the MIKE 21 BW model was used to obtain the high-precision wave distribution in the fishing port. The boundary conditions (significant wave height and peak period) of the MIKE 21 BW model were calculated by combining the MIKE 21 SW model with the designed typhoon wind fields. In SYFP, ships usually adopt the modes of multi-ship side-by-side and single anchor mooring during typhoons. In fair weather, approximately 158 vessels can be berthed if they are all large ones, while approximately 735 vessels can be moored if they are all small ones. However, with an increase in typhoon levels, the anchoring area for small vessels decreases. From the perspective of wave distribution in the fishing port, the number of large vessels moored was hardly affected by typhoons. This can be attributed to the breakwater, which significantly decreases the large wave height in the fishing port. Finally, a study on the framework of a method for hazard assessment of berthing capacity in the coming typhoon-driven storm waves was set up.

Intercomparison of Empirical Formulations of Maximum Wind Radius in Parametric Tropical Storm Modeling over Zhoushan Archipelago

The Holland (2010) parametric wind model has been extensively utilized in tropical cyclone and storm surge-related coastal hazard mitigation and management studies. The only remaining input parameter, the radius of maximum wind speed (Rm), is usually generated by previously proposed empirical relations which are, however, sensitivity to study areas in producing better performed numerical results. In order to acquire optimal Rm formulations over the region of Zhoushan Archipelago, East China Sea, 16 empirical relations were compiled into the Holland (2010) model to produce time series of the pressure, wind speed, and wind direction in comparison to observational records taken at three stations during the tropical cyclone events of Ampil and Rumbai. Their respective agreements were evaluated by error metrices including the root mean square error, correlation coefficient, mean bias error, and scatter index, whilst the overall performances of the 16 formulations were ranked according to a proposed comprehensive error. In the following order, the Rm formulations of Lu (2012), Zhou (2005), Kato (2018), and Jiang (2008) ranked the best for both events in terms of their minimum comprehensive errors; however, recommendations on the application of specific empirical formulations for the region of Zhoushan Archipelago are also provided herein from the perspective of conservation and accuracy.

Probabilistic storm surge hazard using a steady-state surge model for the Pearl River Delta Region, China

Storm surges caused by tropical cyclones (TCs) are one of the costliest threats to coastal communities in southern China. Numerical surge models remain computationally challenging when used to simulate the large number of TC events required for probabilistic hazard assessments at regional scale. The present study demonstrates the applicability of a simple 1D steady-state storm surge representation for such regional scale hazard assessment. The surge setups from wind shear stress and barometric pressure difference are calculated with the meteorological forcing derived from parametric wind models and TC track information. Being computationally efficient, the surge model results do not require further empirical coefficients derived from correlation against observed data as compared to the previous statistical and semi-empirical surge estimations. Using the Pearl River Delta (PRD) region in China as a case study, the root-mean-square errors between the estimated and reported peak storm surges along the PRD coastline are 0.37 m and 0.45 m using two different TC best track inputs, respectively, covering 118 observed surge records from 39 historical TC events. Probabilistic surge hazard maps are further developed for the PRD coastline using the two TC best track datasets covering 1951–2018 as inputs. The mean surge heights along the coastline are in the range of 1.5–3.2 m and 2.0–3.5 m under 100-year and 200-year return periods, respectively. Areas in the west and near the estuary outlet are more prone to higher surge levels due to more frequent TCs affecting the areas historically. Differences in TC characteristics exist between the two best track datasets, which gives rise to localised difference in surge heights along the PRD coastline. The maximum differences in the 100-year and 200-year return period surge levels from the two best track datasets are 0.51 m and 0.64 m, respectively.

Evaluation of parametric wind models for more accurate modeling of storm surge: a case study of Hurricane Michael

Storm surge induced by hurricane is a major threat to the Gulf Coasts of the United States. A numerical modeling study was conducted to simulate the storm surge during Hurricane Michael, a category 5 hurricane that landed on the Florida Panhandle in 2018. A high-resolution model mesh was used in the ADCIRC hydrodynamic model to simulate storm surge and tides during the hurricane. Two parametric wind models, Holland 1980 model and Holland 2010 model, have been evaluated for their effects on the accuracy of storm surge modeling by comparing simulated and observed maximum water levels along the coast. The wind model parameters are determined by observed hurricane wind and pressure data. Results indicate that both Holland 1980 and Holland 2010 wind models produce reasonable accuracy in predicting maximum water level in Mexico Beach, with errors between 1 and 3.7%. Comparing to the observed peak water level of 4.74 m in Mexico Beach, Holland 1980 wind model with radius of 64-knot wind speed for parameter estimation results in the lowest error of 1%. For a given wind model, the wind profiles are also affected by the wind data used for parameter estimation. Away from hurricane eye wall, using radius of 64-knot wind speed for parameter estimation generally produces weaker wind than those using radius of 34-knot wind speed for parameter estimation. Comparing model simulated storm tides with 17 water marks observed along the coast, Holland 2010 wind model using radius of 34-knot wind speed for parameter estimation leads to the minimum mean absolute error. The results will provide a good reference for researchers to improve storm surge modeling. The validated model can be used to support coastal hazard mitigation planning.

Discrepancies on Storm Surge Predictions by Parametric Wind Model and Numerical Weather Prediction Model in a Semi-Enclosed Bay: Case Study of Typhoon Haiyan

This study explores the discrepancies of storm surge predictions driven by the parametric wind model and the numerical weather prediction model. Serving as a leading-order storm wind predictive tool, the parametric Holland wind model provides the frictional-free, steady-state, and geostrophic-balancing solutions. On the other hand, WRF-ARW (Weather Research and Forecasting-Advanced Research WRF) provides the results solving the 3D time-integrated, compressible, and non-hydrostatic Euler equations, but time-consuming. To shed light on their discrepancies for storm surge predictions, the storm surges of 2013 Typhoon Haiyan in the Leyte Gulf and the San Pedro Bay are selected. The Holland wind model predicts strong southeastern winds in the San Pedro Bay after Haiyan makes landfall at the Leyte Island than WRF-ARW 3 km and WRF-ARW 1 km. The storm surge simulation driven by the Holland wind model finds that the water piles up in the San Pedro Bay and its maximum computed storm surges are almost twice than those driven by WRF-ARW. This study also finds that the storm surge prediction in the San Pedro Bay is sensitive to winds, which can be affected by the landfall location, the storm intensity, and the storm forward speed. The numerical experiment points out that the maximum storm surges can be amplified by more 5–6% inside the San Pedro Bay if Haiyan’s forward speed is increased by 10%.

Estimation of global tropical cyclone wind speed probabilities using the STORM dataset

Tropical cyclones (TC) are one of the deadliest and costliest natural disasters. To mitigate the impact of such disasters, it is essential to know extreme exceedance probabilities, also known as return periods, of TC hazards. In this paper, we demonstrate the use of the STORM dataset, containing synthetic TCs equivalent of 10,000 years under present-day climate conditions, for the calculation of TC wind speed return periods. The temporal length of the STORM dataset allows us to empirically calculate return periods up to 10,000 years without fitting an extreme value distribution. We show that fitting a distribution typically results in higher wind speeds compared to their empirically derived counterparts, especially for return periods exceeding 100-yr. By applying a parametric wind model to the TC tracks, we derive return periods at 10 km resolution in TC-prone regions. The return periods are validated against observations and previous studies, and show a good agreement. The accompanying global-scale wind speed return period dataset is publicly available and can be used for high-resolution TC risk assessments.

Increasing Historical Tropical Cyclone-Induced Extreme Wave Heights in the Northern East China Sea during 1979 to 2018

Tropical cyclone (TC)-induced wind waves are a major concern in coastal safety, therefore quantifying the long-term change in extreme TC waves is critical for the design of coastal infrastructures and for understanding variations in coastal morphology. In this study, a trend analysis is performed on the TC-induced extreme wave heights in the northern East China Sea using numerically simulated wave height data during the period of 1979 to 2018. The simulation was forced with historical TC winds constructed using a parametric TC wind model with satellite-observed TC best-track data as the input. The results show consistently increasing extreme wave heights throughout the study region, which are induced predominantly by the increasing TC intensity. The increase rates (0.01–0.08 m yr−1) are relatively large (small) in offshore (nearshore) waters and at relatively high (low) latitudes. The spatial variability of the wave height trend is highly sensitive to the type of TC track. An analytical model of extreme wave height trend is developed that can efficiently estimate the rate of change in the extreme wave heights using extreme wind speed information.

Simulation and Prediction of Storm Surges and Waves Using a Fully Integrated Process Model and a Parametric Cyclonic Wind Model

AbstractThis paper presents a fully integrated coastal process model and a simple parametric cyclonic wind‐pressure model for simulation of wind, storm surges, waves, tidal currents, and river flows. By sharing one computational grid within all those process modules and no need for switching executable codes from one module to another, this full‐coupling feature eliminates possible errors and loss of information due to interpolation and extrapolation of variables between different grids. To generate better cyclonic wind speed and barometric pressure, this parametric wind model includes nonlinear decay effect on wind intensity after hurricane's landfall. By implementing a new wind energy source term, the wave action model is capable of computing wave growth, propagation, and deformation through a regional‐scale domain from deepwater to shallow waters. Model validation and model skill assessment were performed by hindcasting wind, storm surges, waves, and river flows during Hurricane Gustav (2008) by using a high‐resolution grid covering the northern Gulf Coast. With improved wind fields estimated by the new parametric wind model, this fully integrated process model produced high‐quality wavefields in deep and shallow waters and storm tidal levels in the northern Gulf Coasts. Because of computing efficiency provided by seamless integration of process modules and optimized numerical solution schemes, faster‐than‐real‐time predictions of storm surges for the advisories during Hurricane Isaac (2012) were achieved by running the validated model in a desktop computer.

Simulating tropical cyclone waves in the East China Sea with an event-based, parametric-adjusted model

Parametric cyclone wind models with storm characteristics derived from statistical representation do not agreeably delineate the wind field in the East China Sea, leading to uncertain errors in the numerical simulations of Typhoon waves. A new approach to adjust the cyclone wind field in the framework of the Holland model is proposed. The radius of maximum wind speed and the logarithmically varied shape parameter B from the Holland model are iterated out with in situ data and extended to the cyclone track in the East China Sea. This event-based Holland parametric assimilation scheme can model different kinds of unimodal wind profiles and deliver a significant improvement over the original Holland model. The simulated wind fields of three typhoon events, Typhoons Saomai (200608), Fitow (201312) and Maria (201808), are then used to drive the Simulating Waves Nearshore (SWAN) wave model, and wave fields during the three typhoon events show better agreement with the in situ observations. Compared with satellite scatterometer and altimeter products, the effectiveness of modelled typhoon events is largely limited by temporal changes of typhoon structure. This approach is recommended for typhoon events with small temporal changes. Overall, the approach to adjusting wind profiles with observations satisfactorily improves the simulation performances of cyclonic winds and wave heights at a low computation cost.

The Calculation of Extreme Wind Speed Based on Bayesian Method

Zhao, J.; Yan, Z.; Chen, X.; Pang, L., and Lu, Y., 2020. The calculation of extreme wind speed based on Bayesian method. In: Zheng, C.W.; Wang, Q.; Zhan, C., and Yang, S.B. (eds.), Air-Sea Interaction and Coastal Environments of the Maritime and Polar Silk Roads. Journal of Coastal Research, Special Issue No. 99, pp. 105–114. Coconut Creek (Florida), ISSN 0749-0208.Wind speed is one of the main factors to be considered in the design of offshore structures. However, due to the long period fluctuation of typhoons and climate change, the return levels of the wind speed derived using the samples of different years are considerably different, which can lead to unstable results. To solve this problem, in this study, the extreme wind speed is calculated based on the Bayesian method. In the Bayesian parameter estimation method, the parameters of the distribution are considered as random variables, and thus, the priori information can be fully exploited for parameter estimations. The data utilized in this study are deriveds from the best data set of the northwest Pacific tropical cyclone. A typical point in the South China Sea is selected as the calculation point. The samples are determined are determined using a parametric wind model developed by Cardone. Considering the influence of the difference samples of in different time instants, the samples are divided into early and recent samples, which are used as priori and posteriori information, respectively. The normal distribution is used as the priori distribution, and the estimator of the parameters is the expectation of the posteriori distribution, which is calculated using the Markov Chain Monte Carlo (MCMC) algorithm. By comparing the results pertaining to between Bayesian and maximum likelihood method, it can be observed that the former is more stable and reliable.

Assessment of a Parametric Tropical Cyclone Model for Typhoon Wind Modeling in the Yellow Sea

Gong, J.; Jia, X.; Zhuge, W.; Guo, W., and Lee, D., 2020. Assessment of a parametric tropical cyclone model for typhoon wind modeling in the Yellow Sea. In: Zheng, C.W.; Wang, Q.; Zhan, C., and Yang, S.B. (eds.), Air-Sea Interaction and Coastal Environments of the Maritime and Polar Silk Roads.Journal of Coastal Research, Special Issue No. 99, pp. 67-73. Coconut Creek (Florida), ISSN 0749-0208.Tropical cyclones (TCs) pose one of the most dangerous threats to the lives and properties in coastal areas. Various parametric models have been developed to simulate TC wind fields and are critical for modeling and assessing the risk of storm surges and coastal inundations. The Holland parametric wind model (Holland, 1980) has been widely applied in the modeling of TC wind fields; however, systematic assessments of its performance in simulating typhoon wind fields in the Yellow Sea, where typhoons undergo extratropical transitions, are rare. Four typhoons that passed over the Yellow Sea were selected, and the wind measurements at 12 gauges and buoys along the Korean coast were used for model validation and assessment. The results showed that the Holland model is capable of producing satisfactory wind results for typhoons passing over the Yellow Sea given accurate TC parameters. The results also highlighted that the model-predicted typhoon winds were highly sensitive to Rmax and that the shape coefficient B thus should be chosen carefully when the Holland model wind fields are used for TC-induced storm surge or wave modeling.

Typhoon storm surge ensemble forecast based on GPU technique

The accuracy of typhoon forecasts plays an important role in the prediction of storm surges. The uncertainty of a typhoon’s intensity and track means it is necessary to use an ensemble model to predict typhoon storm surges. A hydrodynamic model, which is operational at the National Marine Environmental Forecasting Center, is applied to conduct surge simulations for South China coastal areas using the best track data with parametric wind and pressure models. The results agree well with tidal gauge observations. To improve the calculation efficiency, the hydrodynamic model is modified using CUDA Fortran. The calculation results are almost the same as those from the original model, but the calculation time is reduced by more than 99%. A total of 150 typhoon cases are generated by combining 50 typhoon tracks from the European Centre for Medium-Range Weather Forecasts with three possible typhoon intensity forecasts. The surge ensembles are computed by the improved hydrodynamic model. Based on the simulated storm surges for the different typhoon cases, ensemble and probability forecast products can be provided. The mean ensemble results and probability forecast products are shown to agree well with the observed storm surge caused by Typhoon Mangkhut. The improved model is highly suitable for ensemble numerical forecasts, providing better forecast products for decision-making, and can be easily implemented to run on regular workstations.

Effects of tropical cyclone paths and shelf bathymetry on inducement of severe storm surges in the Gulf of Thailand

The influences of tropical cyclone paths and shelf bathymetry on the inducement of extreme sea levels in a regional bay are investigated. A finite volume coastal ocean model (FVCOM) has been configured for the Gulf of Thailand-Sunda Shelf. A parametric wind model is used to drive the FVCOM. The contributions of the tropical cyclone characteristics are determined through a scenario-based study. Validation based on a historical extreme sea level event shows that the model can resolve the oscillation mechanism well. The intensification of severe storm surges in the region highly depends on four factors including phase propagation of the storm surge wave determined by the landfall position, funnel effect caused by locality of the coastline, and shelf bathymetry determined by the state of mean sea level and coastline crossing angle of the storm path. The coexistence of these factors can cause particular regions e.g. the Surat Thani Bay, inner Gulf of Thailand and Ca Mau Peninsular to experience a larger surge magnitude. These areas are found to be highly related to monsoon troughs that develop during the onset and early northeastern monsoon season (October–November).