The lake surface temperature (LST) of the Great Lakes is an essential metric for understanding regional climate and climate impacts on the hydrologic system. While three-dimensional (3D) hydrodynamic models have been widely applied to Lake Michigan, challenges remain due to model representation of physical processes, errors, and uncertainty in boundary conditions and forcing terms. The Great Lakes region has a relatively dense and long-term observational record, such as NDBC buoys, thermistor measurements, and the NOAA Coast Watch Great Lakes Surface Environmental Analysis (GLSEA) daily LST datasets derived from satellite imagery. However, those have not been incorporated into 3D model simulations through data assimilation (DA) for a long-term run.
  In this project, we collaborated with the NOAA-GLERL team and applied a data assimilative hydrodynamic model to generate a long-term reanalysis LST database for Lake Michigan from 1995-2020. The GLSEA LSTs and in-situ observations were assimilated into a 3-D hydrodynamic model to improve LST accuracy from two aspects: 1) the new LST dataset has a higher temporal resolution from daily to hourly. 2) the new LST data resolves the local upwelling events, shown as a narrow strip of cold water close to the shore.

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