Improvements to lake-effect snow forecasts using a one-way air-lake model coupling approach

Ayumi Fujisaki-Manome, Greg E. Mann, Eric J. Anderson, Philip Y. Chu, Lindsay E. Fitzpatrick, Stanley G. Benjamin, Eric P. James, Tatiana G. Smirnova, Curtis R. Alexander, David M. Wright
2020 Journal of Hydrometeorology  
Lake-effect convective snowstorms frequently produce high-impact, hazardous winter weather conditions downwind of the North American Great Lakes. During lake-effect snow events, the lake surfaces can cool rapidly, and in some cases, notable development of ice cover occurs. Such rapid changes in the lake-surface conditions are not accounted for in existing operational weather forecast models, such as the National Oceanic and Atmospheric Administration (NOAA)'s High Resolution Rapid Refresh
more » ... Rapid Refresh (HRRR) model, resulting in reduced performance of lake-effect snow forecasts. As a milestone to future implementations in the Great Lakes Operational Forecast System (GLOFS) and HRRR, this study examines the one-way linkage between the hydrodynamic-ice model (the Finite-Volume Community Ocean Model coupled with the unstructured grid version of the Los Alamos Sea Ice Model, FVCOM-CICE, the physical core model of GLOFS) and the atmospheric model (the Weather Research and Forecasting model, WRF, the physical core model of HRRR). The realistic representation of lake-surface cooling and ice development or its fractional coverage during three lake-effect snow events was achieved by feeding the FVCOM-CICE simulated lake-surface conditions to WRF (using a regional configuration of HRRR), resulting in the improved simulation of the turbulent heat fluxes over the lakes and resulting snow water equivalent in the downwind areas. This study shows that the one-way coupling is a practical approach that is well suited to the operational environment, as it requires little to no increase in computational resources yet can result in improved forecasts of regional weather and lake conditions.
doi:10.1175/jhm-d-20-0079.1 fatcat:4syo4a63indn7er5wqougyru3y