scholarly journals Understanding Hurricane Storm Surge Generation and Propagation Using a Forecasting Model, Forecast Advisories and Best Track in a Wind Model, and Observed Data—Case Study Hurricane Rita

2019 ◽  
Vol 7 (3) ◽  
pp. 77 ◽  
Author(s):  
Abram Musinguzi ◽  
Muhammad K. Akbar ◽  
Jason G. Fleming ◽  
Samuel K. Hargrove
Keyword(s):  
Storm Surge ◽  
Surface Wind ◽  
Storm Surges ◽  
Wind Pressure ◽  
Forecasting Model ◽  
Wind Fields ◽  
Wind Model ◽  
Meteorological Forcing ◽  
Hurricane Rita

Meteorological forcing is the primary driving force and primary source of errors for storm surge forecasting. The objective of this study was to learn how forecasted meteorological forcing influences storm surge generation and propagation during a hurricane so that storm surge models can be reliably used to forecast actual events. Hindcasts and forecasts of Hurricane Rita (2005) storm surge was used as a case study. Meteorological forcing or surface wind/pressure fields for Hurricane Rita were generated using both the Weather Research and Forecasting (WRF) full-scale forecasting model along with archived hurricane advisories ingested into a sophisticated parametric wind model, namely Generalized Asymmetric Holland Model (GAHM). These wind fields were used to forecast Rita storm surges. Observation based wind fields from the OceanWeather Inc. (OWI) Interactive Objective Kinematic Analysis (IOKA) model, and Best track wind data ingested into the GAHM model were used to generate wind fields for comparison purposes. These wind fields were all used to hindcast Rita storm surges with the ADvanced CIRCulation (ADCIRC) model coupled with the Simulating Waves Nearshore (SWAN) model in a tightly coupled storm surge-wave model referred to as ADCIRC+SWAN. The surge results were compared against a quality-controlled database of observed data to assess the performance of these wind fields on storm surge generation and propagation. The surge hindcast produced by the OWI wind field performed the best, although some high water mark (HWM) locations were overpredicted. Although somewhat underpredicted, the WRF wind fields forecasted wider surge extent and wetted most HWM locations. The hindcast using the Best track parameters in the GAHM and the forecast using forecast/advisories from the National Hurricane Center (NHC) in the GAHM produced strong and narrow wind fields causing localized high surges, which resulted in overprediction near landfall while many HWM locations away from wind bands remained dry.

scholarly journals Advancing global storm surge modelling using the new ERA5 climate reanalysis

2019 ◽  
Vol 54 (1-2) ◽  
pp. 1007-1021 ◽  
Author(s):  
Job C. M. Dullaart ◽  
Sanne Muis ◽  
Nadia Bloemendaal ◽  
Jeroen C. J. H. Aerts
Keyword(s):  
Tropical Cyclones ◽  
Storm Surge ◽  
Global Climate ◽  
Storm Surges ◽  
Early Warning Systems ◽  
Climate Modelling ◽  
Wind Fields ◽  
Recent Advances ◽  
Global Climate Modelling ◽  
Surge Height

Abstract This study examines the implications of recent advances in global climate modelling for simulating storm surges. Following the ERA-Interim (0.75° × 0.75°) global climate reanalysis, in 2018 the European Centre for Medium-range Weather Forecasts released its successor, the ERA5 (0.25° × 0.25°) reanalysis. Using the Global Tide and Surge Model, we analyse eight historical storm surge events driven by tropical—and extra-tropical cyclones. For these events we extract wind fields from the two reanalysis datasets and compare these against satellite-based wind field observations from the Advanced SCATterometer. The root mean squared errors in tropical cyclone wind speed reduce by 58% in ERA5, compared to ERA-Interim, indicating that the mean sea-level pressure and corresponding strong 10-m winds in tropical cyclones greatly improved from ERA-Interim to ERA5. For four of the eight historical events we validate the modelled storm surge heights with tide gauge observations. For Hurricane Irma, the modelled surge height increases from 0.88 m with ERA-Interim to 2.68 m with ERA5, compared to an observed surge height of 2.64 m. We also examine how future advances in climate modelling can potentially further improve global storm surge modelling by comparing the results for ERA-Interim and ERA5 against the operational Integrated Forecasting System (0.125° × 0.125°). We find that a further increase in model resolution results in a better representation of the wind fields and associated storm surges, especially for small size tropical cyclones. Overall, our results show that recent advances in global climate modelling have the potential to increase the accuracy of early-warning systems and coastal flood hazard assessments at the global scale.

Study on the Forecast of the Economic Loss in Storm Surge Disaster Based on Fourier Series - A Case Study of China

2013 ◽  
Vol 734-737 ◽  
pp. 3280-3285
Author(s):  
Ling Di Zhao ◽  
Ya Ru Hao
Keyword(s):  
Fourier Series ◽  
Wind Speed ◽  
Wind Power ◽  
Storm Surge ◽  
Guangdong Province ◽  
Economic Loss ◽  
Forecasting Model ◽  
Storm Surge Disaster

The economic loss forecasting model is built up on the basis of the Fourier series to simulate economic loss and grades in storm surge disaster of Zhejiang, Fujian and Guangdong Provinces. The wind speed can be used to forecast the economic loss of Guangdong Province, and the accuracy of trend and grade forecasting is good (80%). The wind power data can be used in Zhejiang and Fujian Provinces, and the accuracy results are both inferior (60%). Therefore, in the economic warning of storm surge disaster, the Fourier series model can be applied to forecast economic loss and grades.

scholarly journals Simulation of storm surge inundation under different typhoon intensity scenarios: case study of Pingyang County, China

2020 ◽  
Vol 20 (10) ◽  
pp. 2777-2790
Author(s):  
Xianwu Shi ◽  
Pubing Yu ◽  
Zhixing Guo ◽  
Zhilin Sun ◽  
Fuyuan Chen ◽  
...  
Keyword(s):  
Storm Surge ◽  
Water Depth ◽  
Storm Surges ◽  
Economic Losses ◽  
Astronomical Tide ◽  
Evacuation Plans ◽  
Typhoon Intensity ◽  
Typhoon Tracks ◽  
The Impact

Abstract. China is one of the countries that is most seriously affected by storm surges. In recent years, storm surges in coastal areas of China have caused huge economic losses and a large number of human casualties. Knowledge of the inundation range and water depth of storm surges under different typhoon intensities could assist predisaster risk assessment and making evacuation plans, as well as provide decision support for responding to storm surges. Taking Pingyang County in Zhejiang Province as a case study area, parameters including typhoon tracks, radius of maximum wind speed, astronomical tide, and upstream flood runoff were determined for different typhoon intensities. Numerical simulations were conducted using these parameters to investigate the inundation range and water depth distribution of storm surges in Pingyang County considering the impact of seawall collapse under five different intensity scenarios (corresponding to minimum central pressure values equal to 915, 925, 935, 945, and 965 hPa). The inundated area ranged from 103.51 to 233.16 km2 for the most intense typhoon. The proposed method could be easily adopted in various coastal counties and serves as an effective tool for decision-making in storm surge disaster risk reduction practices.

scholarly journals Storm-induced sediment supply to coastal dunes on sand flats

2020 ◽  
Vol 8 (2) ◽  
pp. 335-350 ◽  
Author(s):  
Filipe Galiforni-Silva ◽  
Kathelijne M. Wijnberg ◽  
Suzanne J. M. H. Hulscher
Keyword(s):  
Numerical Model ◽  
Storm Surge ◽  
Storm Surges ◽  
Coastal Dunes ◽  
Sediment Supply ◽  
Aeolian Transport ◽  
Sand Flat ◽  
Sediment Transfer ◽  
Sand Flats

Abstract. Growth of coastal dunes requires a marine supply of sediment. Processes that control the sediment transfer between the subtidal and the supratidal zone are not fully understood, especially in sand flats close to inlets. It is hypothesised that storm surge events induce sediment deposition on sand flats, providing fresh material for aeolian transport and dune growth. The objective of this study is to identify which processes cause deposition on the sand flat during storm surge conditions and discuss the relationship between the supratidal deposition and sediment supply to the dunes. We use the island of Texel (NL) as a case study, of which multiannual topographic and hydrographic datasets are available. Additionally, we use the numerical model XBeach to simulate the most frequent storm surge events for the area. Results show that supratidal shore-parallel deposition of sand occurs in both the numerical model and the topographic data. The amount of sand deposited is directly proportional to surge level and can account for more than a quarter of the volume deposited at the dunes yearly. Furthermore, storm surges are also capable of remobilising the top layer of sediment of the sand flat, making fresh sediment available for aeolian transport. Therefore, in a sand flat setting, storm surges have the potential of reworking significant amounts of sand for aeolian transport in periods after the storm and as such can also play a constructive role in coastal dune development.

scholarly journals An Empirical Analysis on the Relationship between Tropical Cyclone Size and Storm Surge Heights along the U.S. Gulf Coast

2014 ◽  
Vol 18 (8) ◽  
pp. 1-15 ◽  
Author(s):  
Hal F. Needham ◽  
Barry D. Keim
Keyword(s):  
Tropical Cyclone ◽  
Hurricane Katrina ◽  
Storm Surge ◽  
Empirical Analysis ◽  
Gulf Coast ◽  
Inverse Correlation ◽  
Storm Surges ◽  
Wind Fields ◽  
The U.S

Abstract In the past decade, several large tropical cyclones have generated catastrophic storm surges along the U.S. Gulf and Atlantic Coasts. These storms include Hurricanes Katrina, Ike, Isaac, and Sandy. This study uses empirical analysis of tropical cyclone data and maximum storm surge observations to investigate the role of tropical cyclone size in storm surge generation. Storm surge data are provided by the Storm Surge Database (SURGEDAT), a global storm surge database, while a unique tropical cyclone size dataset built from nine different data sources provides the size of the radius of maximum winds (Rmax) and the radii of 63 (34 kt), 93 (50 kt), and 119 km h−1 (64 kt) winds. Statistical analysis reveals an inverse correlation between storm surge magnitudes and Rmax sizes, while positive correlations exist between storm surge heights and the radius of 63 (34 kt), 93 (50 kt), and 119 km h−1 (64 kt) winds. Storm surge heights correlate best with the prelandfall radius of 93 km h−1 (50 kt) winds, with a Spearman correlation coefficient value of 0.82, significant at the 99.9% confidence level. Many historical examples support these statistical results. For example, the 1900 Galveston hurricane, the 1935 Labor Day hurricane, and Hurricane Camille all had small Rmax sizes but generated catastrophic surges. Hurricane Katrina provides an example of the importance of large wind fields, as hurricane-force winds extending 167 km [90 nautical miles (n mi)] from the center of circulation enabled this large storm to generate a higher storm surge level than Hurricane Camille along the same stretch of coast, even though Camille’s prelandfall winds were slightly stronger than Katrina’s. These results may be useful to the storm surge modeling community, as well as disaster science and emergency management professionals, who will benefit from better understanding the role of tropical cyclone size for storm surge generation.

scholarly journals A MULTI-MODEL APPROACH TO SIMULATE THE STORM SURGE AT PUERTO RICO DUE TO HURRICANES IRMA AND MARIA

2018 ◽  
pp. 56
Author(s):  
David F. Kelly ◽  
Ewelina Luczko ◽  
Michael Fullarton ◽  
Yahia Kala
Keyword(s):  
Puerto Rico ◽  
Storm Surge ◽  
State Of The Art ◽  
Numerical Models ◽  
Storm Surges ◽  
Wind Pressure ◽  
Governing Equations ◽  
Storm Tide ◽  
Long Wave ◽  
Model Approach

In this paper we present the results of a multimodel approach to simulating the recent storm surges due to hurricanes Irma and Maria. The study focuses on Puerto Rico which, as a consequence of hurricane Maria, experienced storm surge around the entire perimeter of the island. In this study the storm tide is modeled using a variety of state-of-the-art 2DH numerical models. All models are based on the long wave assumption and employ the Non-Linear Shallow Water (NLSW) equations. The models vary according to the form of the governing NLSW equations that they employ. Differences include linearization and primitive variable or conserved variable (divergence) form. The numerical solution techniques used to solve the governing equations, as well as the options available for the wind, pressure, tidal forcing terms and wetting/drying techniques also vary between (and within) the models.

scholarly journals Comparisons of HRD and SLOSH Surface Wind Fields in Hurricanes: Implications for Storm Surge Modeling

1999 ◽  
Vol 14 (5) ◽  
pp. 671-686 ◽  
Author(s):  
Samuel H. Houston ◽  
Wilson A. Shaffer ◽  
Mark D. Powell ◽  
Jye Chen
Keyword(s):  
Storm Surge ◽  
Surface Wind ◽  
Wind Fields ◽  
Storm Surge Modeling

A Global Statistical–Dynamical Tropical Cyclone Wind Radii Forecast Scheme

2017 ◽  
Vol 32 (2) ◽  
pp. 629-644 ◽  
Author(s):  
John A. Knaff ◽  
Charles R. Sampson ◽  
Galina Chirokova
Keyword(s):  
Tropical Cyclone ◽  
Surface Wind ◽  
Global Model ◽  
Wind Pressure ◽  
Forecast Method ◽  
Wind Model ◽  
Global Models ◽  
Forecast Performance ◽  
Wind Structure ◽  
Dynamical Method

Abstract Forecasts of tropical cyclone (TC) surface wind structure have recently begun to show some skill, but the number of reliable forecast tools, mostly regional hurricane and select global models, remains limited. To provide additional wind structure guidance, this work presents the development of a statistical–dynamical method to predict tropical cyclone wind structure in terms of wind radii, which are defined as the maximum extent of the 34-, 50-, and 64-kt (1 kt = 0.514 m s−1) winds in geographical quadrants about the center of the storm. The basis for TC size variations is developed from an infrared satellite-based record of TC size, which is homogenously calculated from a global sample. The change in TC size is predicted using a statistical–dynamical approach where predictors are based on environmental diagnostics derived from global model forecasts and observed storm conditions. Once the TC size has been predicted, the forecast intensity and track are used along with a parametric wind model to estimate the resulting wind radii. To provide additional guidance for applications and users that require forecasts of central pressure, a wind–pressure relationship that is a function of TC motion, intensity, wind radii (i.e., size), and latitude is then applied to these forecasts. This forecast method compares well with similar wind structure forecasts made by global forecast and regional hurricane models and when these forecasts are used as a member of a simple consensus; its inclusion improves the forecast performance of the consensus.

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