Introduction to special section on The U.S. IOOS Coastal and Ocean Modeling Testbed

Abstract

[1] Strong and strategic collaborations among experts from academia, federal operational centers, and industry have been forged to create a U.S. IOOS Coastal and Ocean Modeling Testbed (COMT). The COMT mission is to accelerate the transition of scientific and technical advances from the coastal and ocean modeling research community to improved operational ocean products and services. This is achieved via the evaluation of existing technology or the development of new technology depending on the status of technology within the research community. The initial phase of the COMT has addressed three coastal and ocean prediction challenges of great societal importance: estuarine hypoxia, shelf hypoxia, and coastal inundation. A fourth effort concentrated on providing and refining the cyberinfrastructure and cyber tools to support the modeling work and to advance interoperability and community access to the COMT archive. This paper presents an overview of the initiation of the COMT, the findings of each team and a discussion of the role of the COMT in research to operations and its interface with the coastal and ocean modeling community in general. Detailed technical results are presented in the accompanying series of 16 technical papers in this special issue.

Quantifying impacts of forecast uncertainties on predicted storm surges

“…As shown by Leuttich et al (2013) and Kerr et al (2013), the SLOSH model has not performed as well as unstructured grid models developed more recently that do not share the run-time constraint imposed on the operational SLOSH model. Unstructured circulation models and SLOSH were executed using the same wind stress and pressure forcing, but despite this and despite using a Gulf scale domain, the SLOSH runs consistently underperformed in comparison with the unstructured grid models.…”

“…Today, simple ''rules of thumb'' and/or expert opinions are often used to supplement forecast information in order to convert single-valued information, such as the mean expected surge, maximum expected surge or 90th percentile surge, into actionable decisions. Given recent test results using SLOSH (Leuttich et al 2013;Kerr et al 2013), the accuracy of this model as the basis for operational decision-making other than for evacuation are somewhat questionable. Also, for applicability to planning, such information does not convey information about the magnitude of uncertainty in these estimates nor a quantitative estimate of potential impact of uncertainty on the impacts of these decisions.…”

“…Several papers have attempted to establish some level of understanding of the relative errors in differing models (Leuttich et al 2013;Kerr et al 2013;Resio and Irish 2015); however, as will be shown here, it is important to understand errors in storm surges relative to the errors in forecast hurricane characteristics. It is hoped that this paper will help provide the scientific and engineering community with at least a preliminary estimate of the implications of surge modeling accuracy for forecast decision-making.…”

“…Here we see that the two models converge at a surge value equal to approximately 4.2 m. A best-fit regression line between the two models suggests that the SLOSH model predicts higher values than ADCIRC for surges less than about 4.2 m and predicts lower values than ADCIRC for surges above 4.2 m. The SLOSH model has not been tested as critically as the ADCIRC model in this area, and as noted previously, limitations on the SLOSH model, imposed by run-time constraints in real-time forecasts, can create significant differences in model performance between SLOSH and a set of less Fig. 8 Comparison of the primary surge at the Battery estimated from the SLOSH and ADCIRC models for the nine highest surge generating storms hindcast by Lin et al (2010) constrained models (Leuttich et al 2013;Kerr et al 2013). As pointed out in Resio and Irish (2015), the winds in SLOSH used in Fig.…”

Coastal Ocean Forecasting: system integration and evaluation

“…Increasingly, model validation is being done within the context of standardized test beds such as NOAA's Joint Hurricane Testbed (JHT) and the Coastal and Ocean Modeling Testbed (COMT) (Rappaport et al 2012;Luettich Jr et al 2013). These make use of standardized metrics, test cases and advanced IT infrastructure for sharing and comparing model results.…”