The Great Lakes Forecasting System is a real-time coastal prediction system for forecasting, on a daily basis, the physical state of each of the Great Lakes for the next two days. Forecast variables include the surface water level fluctuation, horizontal and vertical structure of temperature and currents, and turbulence. The system uses meteorological observations, satellite data, and forecasts from numerical weather prediction models as input. Lake circulation and thermal structure are calculated using a three-dimensional hydrodynamic prediction model. Output from the model is used to provide information on the current state of the lake and to predict changes for the next two days. This information is used by scientists, government agencies, commercial operations, and the public for enhancement of commercial and recreational activity, resource management, and hazard avoidance.
This paper describes system design, data acquisition and analysis procedures, the hydrodynamic model, and sample model output. The initial implementation of the system provides daily nowcasts of system variables for one lake, Lake Erie. Requirements for implementing actual lake forecasts are discussed.
For ultrasonic backscatter devices to be of aid in studies of turbulent sediment transport dynamics, the procedures for converting signal to concentration estimates should be as accurate as possible, and the space and time resolution should be as high as possible. Signal conversion for the 3-MHz system considered here takes into account nearfield beam pattern effects on propagation and attenuation. Enhancing resolution involves explicit consideration of various sources of noise and interference. The results of field deployments demonstrate that, with signal conditioning and ensemble averaging, signal-to-noise ratios can be achieved that allow turbulent variation in concentration to be measured. Two possible sources of error in the conversion procedure are: (1) attenuation due to scatters is not measured in situ, but must be estimated from the data, and (2) with a single-frequency beam, the effects on scattering response of concentration variation and particle-size variation cannot be uncoupled.
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.
SUMMARYThis is the first of two articles intended to develop, apply and verify a new method for averaging the momentum and mass transport equations for turbulence. The new method is based on Gaussian filtering in both the spatial and temporal domains. Application is made to the problem of momentum and scalar transport in a one-dimensional transient Burgers' flow field. No actual calculations, with the averaged equations, are presented in this paper. However, an 'exact' solution of the one-dimensional flow situation is presented as an economical tool for verifying the performance of the different turbulence models. In the second paper calculations are performed with the averaged one-dimensional equations on coarse grids, and the results are compared to the exact or fully simulated data with a statistical verification procedure.
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