Ice Cover in the Great Lakes has significant impacts on regional weather, economy, lake ecology, and human safety. However, forecast guidance for the lakes is largely focused on the ice-free season and associated state variables (currents, water temperatures, etc.) A coupled lake-ice model is proposed with potential to provide valuable information to stakeholders and society at large about the current and near-future state of Great Lakes Ice. The model is run for three of the five Great Lakes for prior years and the modeled ice cover is compared to observations via several skill metrics. Model hindcasts of ice conditions reveal reasonable simulation of year-to-year variability of ice extent, ice season duration, and spatial distribution, though some years appear to be prone to higher error. This modeling framework will serve as the basis for NOAA’s next-generation Great Lakes Operational Forecast System (GLOFS); a set of 3-D lake circulation forecast modeling systems which provides forecast guidance out to 120 h.
A one-way coupled atmospheric-lake modeling system was developed to generate short-term, mesoscale lake circulation, water level, and temperature forecasts for Lake Erie. The coupled system consisted of the semioperational versions of the Pennsylvania State University-National Center for Atmospheric Research threedimensional, mesoscale meteorological model (MM4), and the three-dimensional lake circulation model of the Great Lakes Forecasting System (GLFS). The coupled system was tested using archived MM4 36-h forecasts for three cases during 1992 and 1993. The cases were chosen to demonstrate and evaluate the forecasts produced by the coupled system during severe lake conditions and at different stages in the lake's annual thermal cycle. For each case, the lake model was run for 36 h using surface heat and momentum fluxes derived from MM4's hourly meteorological forecasts and surface water temperatures from the lake model. Evaluations of the lake forecasts were conducted by comparing forecasts to observations and lake model hindcasts. Lake temperatures were generally predicted well by the coupled system. Below the surface, the forecasts depicted the evolution of the lake's thermal structure, although not as rapidly as in the hindcasts. The greatest shortcomings were in the predictions of peak water levels and times of occurrence. The deficiencies in the lake forecasts were related primarily to wind direction errors and underestimation of surface wind speeds by the atmospheric model. The three cases demonstrated both the potential and limitations of daily high-resolution lake forecasts for the Great Lakes. Twice daily or more frequent lake forecasts are now feasible for Lake Erie with the operational implementation of mesoscale atmospheric models such as the U.S. National Weather Service's Eta Model and Rapid Update Cycle.
Social and economic hydrologic riSk factorS in the coaStal zone.Population and economic trends (Bin and Kruse 2006) in coastal counties have tremendous implications for how these areas respond to and recover from natural and man-made hazards, particularly those of a hydrologic/hydrodynamic nature (Willigen et al. 2005). Floods affect the entire spectrum of regional activities, from the morning commute to agribusiness to community decision making. As businesses expand into areas prone to storm surge, more drivers are vulnerable to floods as they navigate vehicles across low-lying coastal
The National Ocean Service (NOS) of National Oceanic and Atmospheric Administration is developing an operational nowcast/forecast system for the Gulf of Maine (GoMOFS). The system aims to produce real-time nowcasts and short-range forecast guidance for water levels, 3-dimensional currents, water temperature, and salinity over the broad GoM region. GoMOFS will be implemented using the Regional Ocean Model System (ROMS). This paper describes the system setup and results from a one-year (2012) hindcast simulation. The hindcast performance was evaluated using the NOS standard skill assessment software. The results indicate favorable agreement between observations and model forecasts. The root-mean-squared errors are about 0.12 m for water level, less than 1.5 • C for temperature, less than 1.5 psu for salinity, and less than 0.2 m/s for currents. It is anticipated to complete the system development and the transition into operations in fiscal year 2017.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.