Urgent computing of storm surge for North Carolina's coast

Forecasting and prediction of natural events, such as tropical and extra-tropical cyclones, inland flooding, and severe winter weather, provide critical guidance to emergency managers and decision-makers from the local to the national level, with the goal of minimizing both human and economic losses. This guidance is used to facilitate evacuation route planning, post-disaster response and resource deployment, and critical infrastructure protection and securing, and it must be available within a time window in which decision makers can take appropriate action. This latter element is that which induces the need for urgency in this area. In this paper, we outline the North Carolina Forecasting System (NCFS) for storm surge and waves for coastal North Carolina, which is threatened by tropical cyclones about once every three years. We initially used advanced cyberinfrastructure techniques (e.g., opportunistic grid computing) in an effort to provide timely guidance for storm surge and wave impacts. However, our experience has been that a distributed computing approach is not robust enough to consistently produce the real-time results that end users expect. As a result, our technical approach has shifted so that the reliable and timely delivery of forecast products has been guaranteed by provisioning dedicated computational resources as opposed to relying on opportunistic availability of external resources. Our experiences with this forecasting effort is discussed in this paper, with a focus on Hurricane Irene (2011) that impacted a substantial portion of the US east coast from North Carolina, up along the eastern seaboard, and into New England

Urgent computing of storm surge for North Carolina's coast

Forecasting and prediction of natural events, such as tropical and extra-tropical cyclones, inland flooding, and severe winter weather, provide critical guidance to emergency managers and decision-makers from the local to the national level, with the goal of minimizing both human and economic losses. This guidance is used to facilitate evacuation route planning, post-disaster response and resource deployment, and critical infrastructure protection and securing, and it must be available within a time window in which decision makers can take appropriate action. This latter element is that which induces the need for urgency in this area. In this paper, we outline the North Carolina Forecasting System (NCFS) for storm surge and waves for coastal North Carolina, which is threatened by tropical cyclones about once every three years. We initially used advanced cyberinfrastructure techniques (e.g., opportunistic grid computing) in an effort to provide timely guidance for storm surge and wave impacts. However, our experience has been that a distributed computing approach is not robust enough to consistently produce the real-time results that end users expect. As a result, our technical approach has shifted so that the reliable and timely delivery of forecast products has been guaranteed by provisioning dedicated computational resources as opposed to relying on opportunistic availability of external resources. Our experiences with this forecasting effort is discussed in this paper, with a focus on Hurricane Irene (2011) that impacted a substantial portion of the US east coast from North Carolina, up along the eastern seaboard, and into New England

The predictability of near-coastal currents using a baroclinic unstructured grid model

A limited domain, coastal ocean forecast system consisting of an unstructured grid model, a meteorological model, a regional ocean model, and a global tidal database is designed to be globally relocatable. For such a system to be viable, the predictability of coastal currents must be well understood with error sources clearly identified. To this end, the coastal forecast system is applied at the mouth of Chesapeake Bay in response to a Navy exercise. Two-day forecasts are produced for a 10-day period from 4 to 14 June 2010 and compared to real-time observations. Interplay between the temporal frequency of the regional model boundary forcing and the application of external tides to the coastal model impacts the tidal characteristics of the coastal current, even contributing a small phase error. Frequencies of at least 3 h are needed to resolve the tidal signal within the regional model; otherwise, externally applied tides from a database are needed to capture the tidal variability. Spatial resolution of the regional model (3 vs 1 km) does not impact skill of the current prediction. Tidal response of the system indicates excellent representation of the dominant M2 tide for water level and currents. Diurnal tides, especially K1, are amplified unrealistically with the application of coarse 27-km winds. Higher-resolution winds reduce current forecast error with the exception of wind originating from the SSW, SSE, and E. These winds run shore parallel and are subject to strong interaction with the shoreline that is poorly represented even by the 3-km wind fields. The vertical distribution of currents is also well predicted by the coastal model. Spatial and temporal resolution of the wind forcing including areas close to the shoreline is the most critical component for accurate current forecasts. Additionally, it is demonstrated that wind resolution plays a large role in establishing realistic thermal and density structures in upwelling prone regions