Impact of GOES Data on Surface Heat Flux Predictionsby Yi-Fei P. Chu, Ohio State Univ, Columbus, United States,
Keith W. Bedford, (M.ASCE), Ohio State Univ, Columbus, United States,
Carolyn J. Merry, Ohio State Univ, Columbus, United States,
Jay S. Hobgood, Ohio State Univ, Columbus, United States,
Abstract: Surface heat flux is the required input for the Great Lakes Forecasting System (GLFS) in order to predict three dimensional temperature structure of the Great Lakes. Since the flux is not operationally measured in the Great Lakes, GLFS utilizes a surface heat flux model based upon energy balance concept which requires cloud cover information in order to estimate radiation transfer terms and net surface heat flux. Currently, cloud fields are objectively analyzed using Great Lakes MARine OBServation Network (MAROBS) data obtained from surface station observations around the lake. The purpose of this paper is to test the sensitivity of cloud cover on surface heat flux, to explore the possibility of using satellite data to obtain better cloud cover, and to evaluate the impact of GOES data on surface heat flux predictions. Two weeks of surface heat flux calculations on a hourly basis based on different cloud data sources were made for this comparison.
Subject Headings: Lakes | Heat transfer | Computer networks | Data collection | Data processing | Hydrologic data | Weather forecasting | Forecasting | Great Lakes
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