Scalability of an Unstructured Grid Continuous Galerkin Based Hurricane Storm Surge Model

Tanaka, Seizo; Bunya, Shintaro; Westerink, Joannes J.; Dawson, Clint; Luettich, R. A.

doi:10.1007/s10915-010-9402-1

Cited by 74 publications

(47 citation statements)

References 23 publications

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“…It is evident that although Figure 4 does not show any discrepancies, there are indeed some discrepancies in water elevation near the shorelines in certain locations near New Orleans. Such differences in the shallow water regions are reported by previous researchers as well [15]. Near shallow water regions, ADCIRC results seem to show wetting and drying convergence issues.…”

Section: Model Comparison Using Katrina Storm Surge Hindcastsupporting

confidence: 74%

“…Near shallow water regions, ADCIRC results seem to show wetting and drying convergence issues. The reason is cited to be the lack of mesh refinement [15].…”

Section: Model Comparison Using Katrina Storm Surge Hindcastmentioning

confidence: 99%

“…For this study, whole hindcast simulations are used (i.e., not only 70,000 time steps). Tanaka et al [15] have reported a dramatic reduction of scalability due to the writing of output files, and have proposed a dedicated writer core as a remedy. To investigate the scalability of the algorithm alone, the output file writing options in all models are turned off for this study.…”

Section: Model Execution Time and Parallel Scalabilitymentioning

confidence: 99%

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CaMEL and ADCIRC Storm Surge Models—A Comparative Study

Akbar

Luettich

Fleming³

et al. 2017

JMSE

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Abstract:The Computation and Modeling Engineering Laboratory (CaMEL), an implicit solver-based storm surge model, has been extended for use on high performance computing platforms. An MPI (Message Passing Interface) based parallel version of CaMEL has been developed from the previously existing serial version. CaMEL uses hybrid finite element and finite volume techniques to solve shallow water conservation equations in either a Cartesian or a spherical coordinate system and includes hurricane-induced wind stress and pressure, bottom friction, the Coriolis effect, and tidal forcing. Both semi-implicit and fully-implicit time stepping formulations are available. Once the parallel implementation is properly validated, CaMEL is evaluated against ADCIRC, an established storm surge model, using a hindcast of storm surge due to Hurricane Katrina. Observed high water marks are used to verify that both models have comparable accuracy. The effects of time step on the stability and accuracy of the models are investigated and indicate that the semi-and fully-implicit solvers in CaMEL allow the use of larger timesteps than ADCIRC's explicit and semi-implicit solvers. However, ADCIRC outperforms CaMEL in parallel scalability and execution wall clock times. Wall times of CaMEL improve significantly when the largest stable time step sizes are used in respective models, although ADCIRC still is faster.Keywords: shallow water equation; storm surge; parallel model; hybrid finite volume and finite element method; implicit matrix-free solver; spherical coordinate system

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Section: Model Comparison Using Katrina Storm Surge Hindcastsupporting

confidence: 74%

“…Near shallow water regions, ADCIRC results seem to show wetting and drying convergence issues. The reason is cited to be the lack of mesh refinement [15].…”

Section: Model Comparison Using Katrina Storm Surge Hindcastmentioning

confidence: 99%

Section: Model Execution Time and Parallel Scalabilitymentioning

confidence: 99%

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CaMEL and ADCIRC Storm Surge Models—A Comparative Study

Akbar

Luettich

Fleming³

et al. 2017

JMSE

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“…Coupling of ADCIRC with a wind-wave model for capturing waveinduced initial states has recently been completed (Dietrich et al 2011). The code has been parallelized for distributed memory, multicore computers, and has been demonstrated to achieve excellent scalability on these platforms (Tanaka et al 2011). For the specific theory and numerical formulation of the ADCIRC model, we direct the interested reader to Luettich and Westerink (2005).…”

Section: Adcirc Coastal Circulation and Storm Surge Modelmentioning

confidence: 99%

Improving Short-Range Ensemble Kalman Storm Surge Forecasting Using Robust Adaptive Inflation

Altaf

Butler

Luo

et al. 2013

Monthly Weather Review

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This paper presents a robust ensemble filtering methodology for storm surge forecasting based on the singular evolutive interpolated Kalman (SEIK) filter, which has been implemented in the framework of the H ' filter. By design, an H ' filter is more robust than the common Kalman filter in the sense that the estimation error in the H ' filter has, in general, a finite growth rate with respect to the uncertainties in assimilation. The computational hydrodynamical model used in this study is the Advanced Circulation (ADCIRC) model. The authors assimilate data obtained from Hurricanes Katrina and Ike as test cases. The results clearly show that the H ' -based SEIK filter provides more accurate short-range forecasts of storm surge compared to recently reported data assimilation results resulting from the standard SEIK filter.

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“…Coupling of the ADCIRC model with a wind wave model for capturing wave-induced setup has recently been completed (Dietrich et al 2011). The code has been parallelized for distributed memory and multicore computers and has been demonstrated to achieve excellent scalability on these platforms (Tanaka et al 2011). …”

Section: Adcirc Coastal Circulation Storm Surge Modelmentioning

confidence: 99%

Data Assimilation within the Advanced Circulation (ADCIRC) Modeling Framework for Hurricane Storm Surge Forecasting

Butler

Altaf

Dawson

et al. 2012

Monthly Weather Review

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Accurate, real-time forecasting of coastal inundation due to hurricanes and tropical storms is a challenging computational problem requiring high-fidelity forward models of currents and water levels driven by hurricane-force winds. Despite best efforts in computational modeling there will always be uncertainty in storm surge forecasts. In recent years, there has been significant instrumentation located along the coastal United States for the purpose of collecting data-specifically wind, water levels, and wave heights-during these extreme events. This type of data, if available in real time, could be used in a data assimilation framework to improve hurricane storm surge forecasts. In this paper a data assimilation methodology for storm surge forecasting based on the use of ensemble Kalman filters and the advanced circulation (ADCIRC) storm surge model is described. The singular evolutive interpolated Kalman (SEIK) filter has been shown to be effective at producing accurate results for ocean models using small ensemble sizes initialized by an empirical orthogonal function analysis. The SEIK filter is applied to the ADCIRC model to improve storm surge forecasting, particularly in capturing maximum water levels (high water marks) and the timing of the surge. Two test cases of data obtained from hindcast studies of Hurricanes Ike and Katrina are presented. It is shown that a modified SEIK filter with an inflation factor improves the accuracy of coarse-resolution forecasts of storm surge resulting from hurricanes. Furthermore, the SEIK filter requires only modest computational resources to obtain more accurate forecasts of storm surge in a constrained time window where forecasters must interact with emergency responders.

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Scalability of an Unstructured Grid Continuous Galerkin Based Hurricane Storm Surge Model

Cited by 74 publications

References 23 publications

CaMEL and ADCIRC Storm Surge Models—A Comparative Study

CaMEL and ADCIRC Storm Surge Models—A Comparative Study

Improving Short-Range Ensemble Kalman Storm Surge Forecasting Using Robust Adaptive Inflation

Data Assimilation within the Advanced Circulation (ADCIRC) Modeling Framework for Hurricane Storm Surge Forecasting

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