Coupling a High Resolution Hurricane Storm Surge Model to Operational Weather and Ocean Prediction Systems

Author(s):  
Yuji Funakoshi ◽  
Jesse C. Feyen ◽  
Frank Aikman, III ◽  
Carlos Lozano ◽  
Hendrik Tolman
2016 ◽  
Vol 66 (8) ◽  
pp. 1005-1024 ◽  
Author(s):  
Alyssa Pampell-Manis ◽  
Juan Horrillo ◽  
Jens Figlus

2008 ◽  
Vol 136 (3) ◽  
pp. 833-864 ◽  
Author(s):  
Joannes J. Westerink ◽  
Richard A. Luettich ◽  
Jesse C. Feyen ◽  
John H. Atkinson ◽  
Clint Dawson ◽  
...  

Abstract Southern Louisiana is characterized by low-lying topography and an extensive network of sounds, bays, marshes, lakes, rivers, and inlets that permit widespread inundation during hurricanes. A basin- to channel-scale implementation of the Advanced Circulation (ADCIRC) unstructured grid hydrodynamic model has been developed that accurately simulates hurricane storm surge, tides, and river flow in this complex region. This is accomplished by defining a domain and computational resolution appropriate for the relevant processes, specifying realistic boundary conditions, and implementing accurate, robust, and highly parallel unstructured grid numerical algorithms. The model domain incorporates the western North Atlantic, the Gulf of Mexico, and the Caribbean Sea so that interactions between basins and the shelf are explicitly modeled and the boundary condition specification of tidal and hurricane processes can be readily defined at the deep water open boundary. The unstructured grid enables highly refined resolution of the complex overland region for modeling localized scales of flow while minimizing computational cost. Kinematic data assimilative or validated dynamic-modeled wind fields provide the hurricane wind and pressure field forcing. Wind fields are modified to incorporate directional boundary layer changes due to overland increases in surface roughness, reduction in effective land roughness due to inundation, and sheltering due to forested canopies. Validation of the model is achieved through hindcasts of Hurricanes Betsy and Andrew. A model skill assessment indicates that the computed peak storm surge height has a mean absolute error of 0.30 m.


2019 ◽  
Vol 99 (2) ◽  
pp. 1105-1130 ◽  
Author(s):  
Kun Yang ◽  
Vladimir Paramygin ◽  
Y. Peter Sheng

Abstract The joint probability method (JPM) is the traditional way to determine the base flood elevation due to storm surge, and it usually requires simulation of storm surge response from tens of thousands of synthetic storms. The simulated storm surge is combined with probabilistic storm rates to create flood maps with various return periods. However, the map production requires enormous computational cost if state-of-the-art hydrodynamic models with high-resolution numerical grids are used; hence, optimal sampling (JPM-OS) with a small number of (~ 100–200) optimal (representative) storms is preferred. This paper presents a significantly improved JPM-OS, where a small number of optimal storms are objectively selected, and simulated storm surge responses of tens of thousands of storms are accurately interpolated from those for the optimal storms using a highly efficient kriging surrogate model. This study focuses on Southwest Florida and considers ~ 150 optimal storms that are selected based on simulations using either the low fidelity (with low resolution and simple physics) SLOSH model or the high fidelity (with high resolution and comprehensive physics) CH3D model. Surge responses to the optimal storms are simulated using both SLOSH and CH3D, and the flood elevations are calculated using JPM-OS with highly efficient kriging interpolations. For verification, the probabilistic inundation maps are compared to those obtained by the traditional JPM and variations of JPM-OS that employ different interpolation schemes, and computed probabilistic water levels are compared to those calculated by historical storm methods. The inundation maps obtained with the JPM-OS differ less than 10% from those obtained with JPM for 20,625 storms, with only 4% of the computational time.


2006 ◽  
Vol 26 (1) ◽  
pp. 18-25 ◽  
Author(s):  
K. Zhang ◽  
S.-C. Chen ◽  
P. Singh ◽  
K. Saleem ◽  
N. Zhao

2012 ◽  
Vol 66 (2) ◽  
pp. 955-983 ◽  
Author(s):  
Alexandros A. Taflanidis ◽  
Gaofeng Jia ◽  
Andrew B. Kennedy ◽  
Jane M. Smith

2018 ◽  
Vol 149 (3-4) ◽  
pp. 413-425 ◽  
Author(s):  
Davina L. Passeri ◽  
Matthew V. Bilskie ◽  
Nathaniel G. Plant ◽  
Joseph W. Long ◽  
Scott C. Hagen

Ocean Science ◽  
2019 ◽  
Vol 15 (5) ◽  
pp. 1307-1326 ◽  
Author(s):  
Catherine Guiavarc'h ◽  
Jonah Roberts-Jones ◽  
Chris Harris ◽  
Daniel J. Lea ◽  
Andrew Ryan ◽  
...  

Abstract. The development of coupled atmosphere–ocean prediction systems with utility on short-range numerical weather prediction (NWP) and ocean forecasting timescales has accelerated over the last decade. This builds on a body of evidence showing the benefit, particularly for weather forecasting, of more correctly representing the feedbacks between the surface ocean and atmosphere. It prepares the way for more unified prediction systems with the capability of providing consistent surface meteorology, wave and surface ocean products to users for whom this is important. Here we describe a coupled ocean–atmosphere system, with weakly coupled data assimilation, which was operationalised at the Met Office as part of the Copernicus Marine Environment Service (CMEMS). We compare the ocean performance to that of an equivalent ocean-only system run at the Met Office and other CMEMS products. Sea surface temperatures in particular are shown to verify better than in the ocean-only systems, although other aspects including temperature profiles and surface currents are slightly degraded. We then discuss the plans to improve the current system in future as part of the development of a “coupled NWP” system at the Met Office.


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