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A Real Time Storm Surge Forecasting System Using ADCIRC

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A Real Time Storm Surge Forecasting System Using ADCIRC A Real Time Storm Surge Forecasting System using ADCIRC Jason G. Fleming,∗ Crystal W. Fulcher, Richard A. Luettich, Brett D. Estrade,† Gabrielle D. Allen,‡ and Harley S. Winer§ Abstract An automated storm surge forecasting system was created around AD- CIRC to predict the storm surge from tropical cyclones in real time as a storm approaches. This system was then applied to Lake Pontchartrain in Southern Louisiana as a case study in order to assist the US Army Corps of Engineers with planning decisions that must be made as storms approach and make landfall. Surge forecasts are generated following each tropical storm advisory update issued by the National Hurricane Center. The gen- eral procedure is to create an ensemble of five ADCIRC storm surge runs based on the consensus storm forecast from the National Hurricane Center and perturbations to this forecast. Winds and pressure fields are generated using a parametric wind model (based on the Holland wind model) that has been coded as an ADCIRC subroutine to maximize execution speed. Initial outputs are water level and wind speed time series plots along the southern shore of Lake Pontchartrain near critical infrastructure. Results will be presented based on the forecasts for historical storms, as well as a summary of the system’s performance on the case study site during the 2007 hurricane season. 1 Introduction Techniques have been under development since at least the 1950’s to quantitatively predict the likely storm surge levels resulting from tropical cyclones (Massey et al, ∗University of North Carolina at Chapel Hill, Institute of Marine Sciences, 3431 Arendell St., Morehead City, NC 28557, jgfl[email protected] †Louisiana Optical Network Initiative, HPC Enablement Group ‡Louisiana State University Department of Computer Science and Center for Computation and Technology §US Army Corps of Engineers, New Orleans District 1 2007). While initial investigations were motivated by scientific curiosity, applied studies were later commissioned by federal agencies such as the US Army Corps of Engineers for storm surge protection designs and by the Federal Emergency Management Agency (FEMA) for the purpose of quantifying risk. More recently, the ongoing migration to coastal areas has created a need for real-time predictions of storm surge that can be used in disaster response operations. The provision of any sort of real-time prediction of storm surge is challenging for many reasons, including (1) uncertainty in the storm forecast, which translates directly into uncertainty in the predicted surge; (2) an extremely short shelflife of results, i.e., model results may go from lifesaving to useless in a matter of hours; and (3) the significant computational requirements of producing high resolution storm surge results; (4) the voluminous size of gridded wind and pressure fields (e.g., as generated by a forecast meteorological model) having enough resolution to robustly represent a tropical cyclone makes them time consuming to move across the internet; (5) the on-demand availability and high reliability requirements for an operational storm surge forecasting system are far greater than are normally required of a shared-use computer and commercial grade internet connections; (6) redundancy may be employed to increase reliability and availability of results but this increases complexity and requires portability of a forecast system to multiple computing platforms; (7) the results must be post-processed and reliably delivered to off-site end users in a form that is useful while communicating the underlying uncertainty. The challenges listed above have been surmounted to varying degrees of robustness by researchers over the years. Hubbert, et al (1991) developed a forecast system for the Australian coast that used the analytical model of Holland (1980) along with a finite-difference code for the shallow water equations that could run in a few minutes on a standard workstation of the time. It gave operators in the forecast office the ability to define and run several forecast scenarios of their own choosing. Flather (1994) described a combined 2D and 1D model, with the Bay of Bengal represented as a 2D depth averaged model and the many tributaries of the Ganges Delta represented in 1D in a unified formulation. This model was also driven with the analytical wind representation from Holland (1980). O’Connor, et al (1999) have constructed a system for forecasting the winds and water levels in Lake Erie using the Eta meteorological model with a 40 km spac- ing and an implementation of the POM model with a 5 km grid spacing in offline operation only. Verlaan, et al (2005) describe a long term project to maintain and enhance a small, continuously operating model that runs in a few minutes on a standard personal computer; the regional weather model that generates me- teorological input also includes assimilation of observational data in real time. Houston, et al (1999) found that analytical wind models usually produced results similar to those driven by more sophisticated weather data. However, they found that the use of real-time, observation-based winds could improve the results of storm surge computations in situations where landfalling hurricanes are affected 2 by synoptic conditions that analytical wind models do not take into account. Graber, et al (2006) qualitatively describe a system under active development that includes wind, wave, and surge forecasting using ADCIRC for the surge component. Mattocks, et al (2006) have created a North Carolina Forecast System that also has a background mode that generates tidal elevations on a daily basis. When a tropical cyclone approaches, it may be switched to event mode, where it superimposes an asymmetric vortex (extended from the work of Holland, 1980) on the background meteorology. The asymmetric vortex is defined by the consensus forecast from the National Hurricane Center (NHC). Finally, Ramakrishnan, et al (2006) describe the complexity of the software re- quired for large scale event based parallel scientific applications. The software that underlies the numerical model and its data must be carefully designed and implemented to meet the performance and reliability challenges for the real time prediction of storm surge. The following sections describe how a complete, automated real time storm surge forecasting system was developed to meet each of the challenges outlined above. The application of the system to a case study is then described, and conclusions about the system’s performance are provided. 2 Methods In this section, each of the methods used to meet the previously described chal- lenges is provided. The techniques used to deal with uncertainty in the meteo- rological forecast as well as communicate that uncertainty are discussed first. A detailed description is then provided of the methodology for arriving at the me- teorological forcing based on the forecast advisory from the National Hurricane Center. The methods used to provide results in a timely manner are discussed in principle and then a detailed description is provided to show the methods in practice. Finally, the measures taken to assure the reliability of an operational storm surge forecast system using the ADCIRC coastal ocean model (Luettich and Westerink, 2004) are discussed. 2.1 Uncertainty A dominant source of uncertainty in any storm surge forecast is the uncertainty in the hurricane forecast. In order to be useful, the method of producing storm surge results must take the uncertainties in the storm’s forecast intensity as well as the uncertainty around the consensus forecast track into account. Furthermore, the storm surge predictions should (ideally) express these uncertainties together with the results themselves. 3 2.1.1 Ensemble Approach In order to provide a more complete picture of the full envelope of possible out- comes, an ensemble approach was used to simulate the consensus forecast as well as four perturbations to that forecast. Ideally, a storm ensemble would be con- structed from a detailed probabilistic analysis of historical forecast uncertainty. We have taken a somewhat ad hoc approach which is a compromise between ma- nipulating a manageable number of storms in the ensemble and representing a realistic set of scenarios for decision makers, with a bias towards the worst case outcome. In the future additional or alternative storms can easily be added to the ensemble if desired. Our ensemble consists of five storms defined as follows: (1) the consensus storm forecast as provided by the National Hurricane Center; (2) a storm on the consensus track that has 20% faster wind speed; (3) a storm on the forecast track with with 20% slower forward speed; (4) a storm with the consensus intensity traveling along the right edge of the cone of uncertainty (also known as the “veer right” storm) ; and (5) a storm with the consensus intensity traveling along the left edge of the cone of uncertainty (also known as the “veer left” storm). The characteristics of each storm in the ensemble were chosen to provide a reasonable range of worst case conditions while minimizing the number of storms in the ensemble, thus avoiding the incremental computational power required to produce results, as well as the exponentially greater effort required for end users to interpret them. In order to generate this ensemble of storm parameters, the forecast advisory from the National Hurricane Center (NHC) was modified in the following way for each storm: (1) no change for the consensus storm; (2) the maximum wind speed from storm 1 was multiplied by 1.2; (3) the forecast period for storm 1 was multiplied by 1.2; (4) and (5) the forecast storm position was modified from storm 1 such that the hurricane center diverges further and further from the consensus storm over the course of the forecast (see Figure 1). The positions of Storm4 and Storm5 are specified as lying on a line perpendicular to the consensus track at a distance equal to the radius of uncertainty from the consensus position at that forecast period.
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