Hurricane storm surge simulations for Tampa Bay

Using a high resolution, three-dimensional, primitive equation, finite volume coastal ocean model with flooding and drying capabilities, supported by a merged bathymetric-topographic data set and driven by prototypical hurricane winds and atmospheric pressure fields, we investigated the storm surge responses for the Tampa Bay, Florida, vicinity and their sensitivities to point of landfall, direction and speed of approach, and intensity. All of these factors were found to be important. Flooding potential by wind stress and atmospheric pressure induced surge is significant for a category 2 hurricane and catastrophic for a category 4 hurricane. Tide, river, and wave effects are additive, making the potential for flood-induced damage even greater. Since storm surge sets up as a slope to the sea surface, the highest surge tends to occur over the upper reaches of the bay, Old Tampa Bay and Hillsborough Bay in particular. For point of landfall sensitivity, the worst case is when the hurricane center is positioned north of the bay mouth such that the maximum winds associated with the eye wall are at the bay mouth. Northerly (southerly) approaching storms yield larger (smaller) surges since the winds initially set up (set down) water level. As a hybrid between the landfall and direction sensitivity experiments, a storm transiting up the bay axis from southwest to northeast yields the smallest surge, debunking a misconception that this is the worst Tampa Bay flooding case. Hurricanes with slow (fast) translation speeds yield larger (smaller) surges within Tampa Bay due to the time required to redistribute mass.     

Hurricane Katrina storm surge delineation: implications for future storm surge forecasts and warnings

The storm surge in coastal Mississippi caused by Hurricane Katrina was unprecedented in the region. The height and geographic extent of the storm surge came as a surprise to many and exceeded pre-impact surge scenarios based on SLOSH models that were the basis for emergency preparedness and local land use decision-making. This paper explores the spatial accuracy of three interpolated storm surge surfaces derived from post-event reconnaissance data by comparing the interpolation results to a specific SLOSH run. The findings are used to suggest improvements in the calibration of existing pre-event storm surge models such as SLOSH. Finally, the paper provides some suggestions on an optimal surge forecast map that could enhance the communication of storm surge risks to the public.     

Numerical storm surge model for India and Pakistan

The northeastern sector of the Arabian Sea, which covers the Gujarat coast of India and western coast of Pakistan, is a region vulnerable to extreme sea levels associated with tropical cyclones (TCs). Although the frequency of tropical cyclones in the Arabian Sea is not high, the coastal regions of India and Pakistan suffer in terms of loss of life and property caused by the surges. In view of this a location-specific fine resolution model is developed for the Gujarat coast of India and adjoining Pakistan coast. The east–west and north–south grid distance is about 3.0 km. Using this model, numerical experiments are carried out to simulate the surges generated by 1999 and 2001 cyclones which struck the Pakistan coast. The model computed surges are in agreement with the available observational estimates.     

Hurricane Gilbert: The storm of the century

Hurricane Gilbert has been labelled the storm of the century because of the many meteorological records it set. These include size, straightness of track, atmospheric pressure, precipitation, and total energy. After ravaging Jamaica as a Force 3 storm, Gilbert made landfall in Yucatan as a Force 5, one of only three hurricanes of such magnitude to do so in North America this century. In spite of a death toll of 318 and property damage in the billions of dollars, Gilbert's impacts were eclipsed by the extensive publicity that accompanied Hurricane Hugo's landfall in South Carolina the following year (1989).     

On the accuracy of atmospheric forcing for extra-tropical storm surge prediction

The ability of the SMARA storm surge numerical prediction system to reproduce local effects in estuarine and coastal winds was recently improved by considering one-way coupling of the air–sea momentum exchange through the wave stress, and best forecasting practices for downscaling. The inclusion of long period atmospheric pressure forcing in tide and tide/surge calculations corrected a systematic error in the surge, produced by the South Atlantic Ocean quasi-stationary pressure patterns. The maximum forecast range for the storm surge at Buenos Aires provided by the real-time use of water level observations is approximately 12 h. The best available water level prediction is the 6-h forecast (nowcast) based on the closest water level observations. The 24-h forecast from the numerical models slightly improves this nowcast. Although the numerical forecast accuracy degrades after the first 48 h, the improvement to the full range observation-based prediction is maintained at the inner Río de la Plata area and extends to the first 3 days at the intermediate navigation channels.     

Inclusion of ice cover in a storm surge model for the Beaufort Sea

Storm surges in the Beaufort Sea present a severe problem for navigation as well as for offshore oil drilling activities. Influence of ice cover on storm surges in the Beaufort Sea is examined making use of a numerical model as well as a set of observations.The automated shallow-water model of Henry has been modified to incorporate ice cover and is adapted to the Beaufort Sea. The leading edge of the permanent ice is calculated from the loci of identifiable points. Generalized similarity theory is employed to compute wind stresses. Simulations are made using model-predicted ice concentrations and observed ice concentrations. Ice motion is relatively small in units of model grid distance (approximately 18 km) during surges. Spherical effects are important and should be included in future adaptations of the model. Comparison of the computed surges with observed surges for eight different events showed reasonable agreement.     

Inclusion of ice cover in a storm surge model for the Beaufort Sea

Storm surges in the Beaufort Sea present a severe problem for navigation as well as for offshore oil drilling activities. Influence of ice cover on storm surges in the Beaufort Sea is examined making use of a numerical model as well as a set of observations. The automated shallow-water model of Henry has been modified to incorporate ice cover and is adapted to the Beaufort Sea. The leading edge of the permanent ice is calculated from the loci of identifiable points. Generalized similarity theory is employed to compute wind stresses. Simulations are made using model-predicted ice concentrations and observed ice concentrations. Ice motion is relatively small in units of model grid distance (approximately 18 km) during surges. Spherical effects are important and should be included in future adaptations of the model. Comparison of the computed surges with observed surges for eight different events showed reasonable agreement.     

Simulating storm surge waves for structural vulnerability estimation and flood hazard mapping

Natural Hazards - Wave action during storm surge is a common cause of building damage and therefore a critical consideration when estimating structural vulnerability and mapping flood risk....     

Extremity analysis of storm surge for fixing safe design water level

Phenomenal storm surge levels associated with cyclones are common in East Coast of India. The coastal regions of Andhra Pradesh are in rapid stride of myriad marine infrastructural developments. The safe elevations of coastal structures need a long-term assessment of storm surge conditions. Hence, past 50 years (1949–1998), tropical cyclones hit the Bay are obtained from Fleet Naval Meteorological & Oceanographic Center, USA, and analyzed to assess the storm surge experienced around Kakinada and along south Andhra Pradesh coast. In this paper, authors implemented Rankin Hydromet Vortex model and Bretschneider’s wind stress formulation to hindcast the surge levels. It is seen from the hindcast data that the November, 1977 cyclone has generated highest surge of the order of 1.98 m. Extreme value analysis is carried out using Weibull distribution for long-term prediction. The results reveal that the surge for 1 in 100-year return period is 2.0 m. Further the highest surge in 50 years generated by the severe cyclone (1977) is numerically simulated using hydrodynamic model of Mike-21. The simulation results show that the Krishnapatnam, Nizampatnam and south of Kakinada have experienced a surge of 1.0, 1.5 and 0.75 m, respectively.     

Advances in surrogate modeling for storm surge prediction: storm selection and addressing characteristics related to climate change

This paper establishes various advancements for the application of surrogate modeling techniques for storm surge prediction utilizing an existing database of high-fidelity, synthetic storms (tropical cyclones). Kriging, also known as Gaussian process regression, is specifically chosen as the surrogate model in this study. Emphasis is first placed on the storm selection for developing the database of synthetic storms. An adaptive, sequential selection is examined here that iteratively identifies the storm (or multiple storms) that is expected to provide the greatest enhancement of the prediction accuracy when that storm is added into the already available database. Appropriate error statistics are discussed for assessing convergence of this iterative selection, and its performance is compared to the joint probability method with optimal sampling, utilizing the required number of synthetic storms to achieve the same level of accuracy as comparison metric. The impact on risk estimation is also examined. The discussion then moves to adjustments of the surrogate modeling framework to support two implementation issues that might become more relevant due to climate change considerations: future storm intensification and sea level rise (SLR). For storm intensification, the use of the surrogate model for prediction extrapolation is examined. Tuning of the surrogate model characteristics using cross-validation techniques and modification of the tuning to prioritize storms with specific characteristics are proposed, whereas an augmentation of the database with new/additional storms is also considered. With respect to SLR, the recently developed database for the US Army Corps of Engineers’ North Atlantic Comprehensive Coastal Study is exploited to demonstrate how surrogate modeling can support predictions that include SLR considerations.     

Dynamic issues in the SE South America storm surge modeling

The ability of the SMARA storm surge numerical prediction system to reproduce local effects in estuarine and coastal winds was recently improved by considering one-way coupling of the air–sea momentum exchange through the wave stress, and best forecasting practices for downscaling. The inclusion of long period atmospheric pressure forcing in tide and tide/surge calculations corrected a systematic error in the surge, produced by the South Atlantic Ocean quasi-stationary pressure patterns. The maximum forecast range for the storm surge at Buenos Aires provided by the real-time use of water level observations is approximately 12 h. The best available water level prediction is the 6-h forecast (nowcast) based on the closest water level observations. The 24-h forecast from the numerical models slightly improves this nowcast. Although the numerical forecast accuracy degrades after the first 48 h, the improvement to the full range observation-based prediction is maintained at the inner Río de la Plata area and extends to the first 3 days at the intermediate navigation channels.     

On the recurvature of tropical cyclones and the storm surge problem in Bangladesh

The disproportionality of the large frequency of occurrence of severe storm surges on the coast of Bangladesh is highlighted. The reasons for the recurvature of these storms towards the Bangladesh coast and the associated severe surges are discussed in this paper.Atmospheric Environment Service, Ice Center, Environment Canada, 373 Sussex Drive, Ottawa, Ontario, Canada K1A 0H3.     

含盐风暴潮溃堤洪水数值模拟及风险分析

为了研究三角洲河口风暴潮溃堤时的盐水运动规律,建立一、二维耦合的盐度数学模型对风暴潮溃堤时的盐水运动进行模拟。模型考虑洪泛区建筑物对盐水运动的影响以及溃口的渐变发展过程。用2008年多个测站的实测数据对河网模型的潮位和盐度计算结果进行了验证。将模型应用于珠江三角洲河网某近海溃口风暴潮溃堤的盐水运动模拟,并绘制了最大盐度等值面图。计算结果表明,该溃口大部分区域的溃堤积水盐度超过了4psu,因此,溃堤洪水的高盐度积水影响不容忽视。通过比较“溃堤”和“不溃堤”两种情况下的河网盐度计算结果,发现上游河道的溃堤分流增大了河道的纳潮量,促使涨潮量增大,增大了下游河网的咸潮上溯风险,减弱了上游来流对咸潮的压制效果。     

Simulation of atmospheric states for a storm surge on the west coast of Korea: model comparison between MM5, WRF and COAMPS

High-quality informations on sea level pressure and sea surface wind stress are required to accurately predict storm surges over the Korean Peninsula. The storm surge on 31 March 2007 at Yeonggwang, on the western coast, was an abrupt response to mesocyclone development. In the present study, we attempted to obtain reliable surface winds and sea level pressures. Using an optimal physical parameterization for wind conditions, MM5, WRF and COAMPS were used to simulate the atmospheric states that accompanied the storm surge. The use of MM5, WRF and COAMPS simulations indicated the development of high winds in the strong pressure gradient due to an anticyclone and a mesocyclone in the southern part of the western coast. The response to this situation to the storm surge was sensitive. A low-level warm advection was examined as a possible causal mechanism for the development of a mesocyclone in the generating storm surge. The low-level warm temperature advection was simulated using the three models, but MM5 and WRF tended to underestimate the warm tongue and overestimate the wind speed. The WRF simulation was closer to the observed data than the other simulations in terms of wind speed and the intensity of the mesocyclone. It can be concluded that the magnitude of the storm surge at Yeonggwang was dependent, not only on the development of a mesocyclone but on ocean effects as well.     

Improvements in storm surge surrogate modeling for synthetic storm parameterization,node condition classification and implementation to small size databases

Surrogate models are becoming increasingly popular for storm surge predictions. Using existing databases of storm simulations, developed typically during regional flood studies, these models provide fast-to-compute, data-driven approximations quantifying the expected storm surge for any new storm (not included in the training database). This paper considers the development of such a surrogate model for Delaware Bay, using a database of 156 simulations driven by synthetic tropical cyclones and offering predictions for a grid that includes close to 300,000 computational nodes within the geographical domain of interest. Kriging (Gaussian Process regression) is adopted as the surrogate modeling technique, and various relevant advancements are established. The appropriate parameterization of the synthetic storm database is examined. For this, instead of the storm features at landfall, the features when the storm is at closest distance to some representative point of the domain of interest are investigated as an alternative parametrization, and are found to produce a better surrogate. For nodes that remained dry for some of the database storms, imputation of the surge using a weighted k nearest neighbor (kNN) interpolation is considered to fill in the missing data. The use of a secondary, classification surrogate model, combining logistic principal component analysis and Kriging, is examined to address instances for which the imputed surge leads to misclassification of the node condition. Finally, concerns related to overfitting for the surrogate model are discussed, stemming from the small size of the available database. These concerns extend to both the calibration of the surrogate model hyper-parameters, as well as to the validation approaches adopted. During this process, the benefits from the use of principal component analysis as a dimensionality reduction technique, and the appropriate transformation and scaling of the surge output are examined in detail.