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John G. W. Kelley, Jay S. Hobgood, Keith W. Bedford, and David J. Schwab

Abstract

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 semi-operational versions of the Pennsylvania State University–National Center for Atmospheric Research three-dimensional, 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.

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John G. W. Kelley, David W. Behringer, H. Jean Thiebaux, and Bhavani Balasubramaniyan

Abstract

The real-time, three-dimensional, limited-area Coastal Ocean Forecast System (COFS) has been developed for the northwestern Atlantic Ocean and implemented at the National Centers for Environmental Prediction. COFS generates a daily nowcast and 1-day forecast of water level, temperature, salinity, and currents. Surface forcing is provided by 3-h forecasts from the National Weather Service's Eta Model, a mesoscale atmospheric prediction model. Lateral oceanic boundary conditions are based on climatic data. COFS assimilates in situ sea surface temperature (SST) observations and multichannel satellite SST retrievals for the past 48 h. SST predictions from the assimilating and nonassimilating versions of COFS were compared with independent observations and a 14-km-resolution multichannel SST analysis. The assimilation of SST data reduced the magnitude and the geographic extent of COFS's characteristic positive temperature bias north of the Gulf Stream. The root-mean-square SST differences between the COFS predictions and in situ observations were reduced by up to 47%–50%. Qualitative comparisons were also made between predictions from the assimilating and nonassimilating versions and thermal profiles measured by expendable bathythermographs. These comparisons indicated that the assimilation scheme had positive impact in reducing temperature differences in the top 300 m at most locations. However, the subsurface comparisons also show that, in dynamically complex regions such as the Gulf Stream, the continental slope, or the Gulf of Maine, the data assimilation system has difficulty reproducing the observed ocean thermal structure and would likely benefit from the direct assimilation of observed profiles.

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