Using remotely sensed data to modify wind forcing in operational storm surge forecasting

Storm surges are abnormal coastal sea level events caused by meteorological conditions such as tropical cyclones. They have the potential to cause widespread loss of life and financial damage and have done so on many occasions in the past. Accurate and timely forecasts are necessary to help mitigate the risks posed by these events. Operational forecasting models use discretisations of the governing equations for fluid flow to model the sea surface, which is then forced by surface stresses derived from a model wind and pressure fields. The wind fields are typically idealised and generated parametrically. In this study, wind field datasets derived from remotely sensed data are used to modify the model parametric wind forcing and investigate potential improvement to operational forecasting. We examine two methods for using analysis wind fields derived from remotely sensed observations of three hurricanes. Our first method simply replaces the parametric wind fields with its corresponding analysis wind field for a period of time. Our second method does this also but takes it further by attempting to use some of the information present in the analysis wind field to estimate future wind fields. We find that our methods do yield some forecast improvement, most notably for our second method where we get improvements of up to 0.29 m on average. Importantly, the spatial structure of the surge is changed in some places such that locations that were previously forecast small surges had their water levels increased. These results were validated by tide gauge data.     

The impact of extra-tropical transitioning on storm surge and waves in catastrophe risk modelling: application to the Japanese coastline

Catastrophe risk models are used to assess and manage the economic and societal impacts of natural perils such as tropical cyclones. Large ensembles of event simulations are required to generate useful model output. For example, to estimate the risk due to wind-driven storm surge and waves in tropical cyclone risk models, computationally efficient parametric representations of the wind forcing are required to enable the generation of large ensembles. This paper presents new results on the impact of including explicit representations of extra-tropical transitioning in parametric wind models used to force storm surge and wave simulations in a catastrophe risk modelling context. Extra-tropical transitioning is particularly important in modelling risk on the Japanese coastline, as roughly 40 % of typhoons hitting the Japanese mainland are transitioning before landfall. Using both a historical and idealized track set, we compare maximum storm surge and wave footprints along the Japanese coastline for models that include, and do not include, explicit representations of extra-tropical transitioning. We find that the inclusion of extra-tropical transitioning leads to lower storm surge (10–20 %) and waves (5–15 %) on the southern Japanese coast, with significantly higher storm surge and waves along the northern coast (25–50 %). The results of this paper demonstrate that useful risk assessment of coastal flood risk in Japan must consider the extra-tropical transitioning process.     

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.     

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.     

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.     

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.     

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.     

On the use of synthetic tropical cyclones and hypothetical events for storm surge assessment under climate change

Natural Hazards - This study presents a new approach to assess storm surge risk from tropical cyclones under climate change by direct calculation of the local flood levels using a limited number of...     

Simulation of the Hurricane Dennis storm surge and considerations for vertical resolution

A high-resolution storm surge model of Apalachee Bay in the northeastern Gulf of Mexico is developed using an unstructured grid finite-volume coastal ocean model (FVCOM). The model is applied to the case of Hurricane Dennis (July 2005). This storm caused underpredicted severe flooding of the Apalachee Bay coastal area and upriver inland communities. Accurate resolution of complicated geometry of the coastal region and waterways in the model reveals processes responsible for the unanticipated high storm tide in the area. Model results are validated with available observations of the storm tide. Model experiments suggest that during Dennis, excessive flooding in the coastal zone and the town of St. Marks, located up the St. Marks River, was caused by additive effects of coincident high tides (~10–15% of the total sea-level rise) and a propagating shelf wave (~30%) that added to the locally wind-generated surge. Wave setup, the biggest uncertainty, is estimated on the basis of empirical and analytical relations. The Dennis case is then used to test the sensitivity of the model solution to vertical discretization. A suite of model experiments is performed with varying numbers of vertical sigma (σ) levels, with different distribution of σ-levels within the water column and a varying bottom drag coefficient. The major finding is that the storm surge solution is more sensitive to resolution within the velocity shear zone at mid-depths compared to resolution of the upper and bottom layer or values of the bottom drag coefficient.     

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.     

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.     

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.     

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 influence of changes in the drag relation on surface wind speeds and storm surge forecasts

In this study it is investigated how uncertainties in the magnitude of the drag coefficient translate into uncertainties in storm surge forecasts in the case of severe weather. A storm surge model is used with wind stress data from a numerical weather prediction (NWP) model, to simulate several recent storms over the North Sea. For a fixed wind speed, the wind stress is linear in the drag coefficient. However, in the NWP model the wind speed is not fixed and increasing the drag in the NWP model results into reduced wind speeds. The results from simulations show that for given increase in the drag coefficient, the weakening of the 10-m wind field reduces the increase in the stress considerably. When the Charnock parameter is increased in the NWP model, the resulting relative changes in the wind stress are almost independent of the wind speed. This is related to the fact that the depth of the surface boundary layer depends on the wind speed. The ratio between relative changes in the wind stress and relative changes in the drag coefficient depends on the wind speed. For 10-m wind speeds larger than 20?m?s^?1 the ratio is 0.52; for lower wind speed criteria the ratio is somewhat larger (??0.60). Approximately 36% of the relative change in the drag coefficient translates into a relative change in the surge in stations at the Dutch coast. The relative increase in the storm surge is approximately 68% of the relative increase in the stress.     

Tropical cyclone wind field forcing for surge models: critical issues and sensitivities

Several wind fields developed for Hurricane Katrina (2005) in the US Gulf of Mexico (GOM) are applied with the ADCIRC hydrodynamic model to explore the sensitivity of predictions of coastal surges to wind fields developed by alternative methods. The alternative model predictions are evaluated against water level measurements provided by gages at two coastal locations. It is found that all the post-event analyzed wind fields yield a range of predictions of only ±10% of the available peak surge measurements regardless of whether the wind fields are produced by dynamical boundary layer models, kinematic analysis methods or a blend. However, the richness of meteorological forcing data in the GOM is not typically matched in other basins affected by tropical cyclones and errors may be much larger where storm intensity and size parameters are estimated mainly from satellite data. The attributes and remaining critical deficiencies of current methods for surface wind specification in both data-rich and data-poor environments are reviewed.     

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....