Great Lakes Observing & Forecasting Systems

Overview and Objectives

In this project, we will answer key questions pertaining to understanding hydrodynamics and the accuracy of hydrodynamic modeling in the western basin of Lake Erie and how they relate to 1) the inclusion of its second major tributary (i.e., Maumee River) and 2) the choice of meteorological forcing functions (i.e., atmospheric model derived or spatially interpolated). An important step in this process will be analysis of data from the acoustic Doppler current profilers (ADCP) deployed by GLERL in the western basin of Lake Erie in summer of 2015. This data set will also serve as a basis for model skill assessment in a variety of model settings.

In 2016-2017, CILER will continue to support NOAA-GLERL on the operational development of the Lake Erie harmful algal bloom (HAB) forecasts. These forecasts are based on Great Lakes Coastal Forecasting System (GLCFS) output, in particular circulation and thermal structure produced by the Finite Volume Coastal Ocean Model (FVCOM, Anderson et al., 2010). The Lake Erie Operational Forecasting System (LEOFS, part of the Great Lakes Operational Forecasting System [GLOFS]) became operational in 2015. Comparison of model results with the first long-term current observations conducted in Lake Erie’s western basin in 2015 will be very beneficial for additional testing of model skill. In addition, unlike the research version of FVCOM, neither of the existing hydrodynamic forecasting systems (GLCFS run by GLERL; GLOFS run by NOS CO-OPS) include the Maumee River flow in their predictions.  Maumee River flow may not only influence local and regional lake hydrodynamics (especially during major run-off events), but also influence dynamics of HABs in the vicinity of Toledo, OH, and beyond (Michalak et al., 2013). Finally, the existing hydrodynamic forecasting systems use different meteorological forcing data: GLCFS uses spatially-interpolated observations in its hindcasts, while GLOFS uses output from the new National Centers for Environmental Prediction (NCEP) High-Resolution Rapid Refresh model (HRRR). While spatial interpolation is done with the natural neighbor method (Beletsky et al., 2003), the HRRR model is a rapid update weather model that uses radar and other observations to improve forecasted weather conditions at a 3-km scale. Although the potential for meteorological models to improve outlooks is known (Beletsky et al., 2003; Beletsky et al., 2013), no systematic study of hydrodynamic response to HRRR forcing on any body of water has been carried out.