Date of Award

May 2024

Degree Type

Thesis

Degree Name

Master of Science

Department

Atmospheric Science

First Advisor

Clark Evans

Committee Members

Jon Kahl, Sergey Kravtsov

Keywords

Forecasting, Great Lakes, High Resolution Rapid Refresh Model, Lake Breeze, Lake-air interaction, Marine Boundary layer

Abstract

We determined the ability of the High-Resolution Rapid Refresh (HRRR) mesoscale model to predict the lake-breeze front’s structure and faithfully represent the marine atmospheric boundary layer (MABL) behind it. First, two field missions were completed during the 2023 warm season over Lake Michigan to characterize the spatiotemporal evolution of the MABL and validate HRRR forecasts. We found the Lake Michigan MABL was characterized by minimal thermodynamic and kinematic variability on diurnal time scales, regardless of the stability or flow regime. Additionally, the HRRR was able to resolve MABL thermodynamic structures effectively but underestimated the vertical temperature distribution, leading to a persistent cold bias at all vertical levels over Lake Michigan. Second, a model-based lake-breeze detection algorithm was developed and tested on multiple 2023 warm season lake-breeze cases. The algorithm skillfully predicted the evolution of the lake-breeze front for offshore flow regimes across the warm season but struggled to consistently identify the front under onshore flow regimes. These results advance our understanding of the warm-season MABL structure over the Great Lakes, its influence on lake-breeze front propagation, and how faithfully the HRRR represents these features.

Download

Share

COinS