Advancement of Integrated Winter Weather Forecasts in the Great Lakes Region: Linking Operational Weather, Lake, and Ice Models and User Engagement

Extreme and hazardous winter weather events involving ice and snow contribute to dozens of fatalities every year and impact a wide range of economic activities around the Great Lakes region. Rapid forming of lake ice poses challenge in commercial shipping and ice breaking operations by the U.S. Coast Guards. Lake effect snow storms can cause life-threatening impacts to transportation and utilities. Critical factors for safety and reduction of loss and damages from such hazardous events include, 1) accurate and timely forecasts of hazardous winter weather events, and 2) user friendly interface that effectively communicates the forecast information.

Currently, the development of the first-ever short-term ice forecast for the Great Lakes is underway to be implemented to the existing NOAA’s Great Lakes Operational Forecast System (GLOFS). This forecast model is based on a coupled ice-hydrodynamic model from the unstructured grid version of the Los Alamos Sea Ice Model (UG-CICE) and the unstructured grid Finite Volume Community Ocean Model (FVCOM). Extensive model verification has been carried out to ensure the model’s capability to reproduce ice extent, ice duration, and winter water temperature, in addition to the existing non-ice season products such as water temperature, water level, and lake currents. The model has variable horizontal resolution (200 m - 3 km) and is driven by prescribed surface meteorology from operational weather forecasts by NOAA’s High-Resolution Rapid Refresh (HRRR). HRRR is based on the Weather Research and Forecasting (WRF) model and has 3 km horizontal resolution. Until recently, the development efforts around the GLOFS-ice and HRRR models have been carried out separately, and the interactions between these model components have attracted less attention. As a result, the current operational HRRR uses a temporally-constant lake surface temperature over the forecast horizon. This simplified surface boundary condition could be erroneous when the lake surface condition rapidly changes, as in many winter weather events. Consequently, lake ice forecasts from GLOFS, which is driven by HRRR forecasts, could also suffer from errors introduced from the simplified surface boundary condition.

We present recent collaborative research across NOAA to address this challenge. We demonstrate that the one-way linkage between the weather and ice-lake models is a feasible and practical solution to enable coupling between HRRR and GLOFS-ice under the NOAA’s Hydrometeorology Testbed. In the one-way linkage, the weather model ingests the temporally-changing forecasted lake surface temperature and ice conditions (e.g. concentration, surface temperature) from GLOFS as the surface boundary conditions. In turn, the ice-hydrodynamic model will receive better and realistic surface meteorology from the linked weather model in the following forecast cycles. Thus, this iterative one-way linkage approach enables loose model coupling without increasing any computational expense by leveraging the existing dissemination channels for HRRR from NOAA’s National Weather Service and for GLOFS-Ice from NOAA’s National Ocean Service. We focus on recent lake-effect snowstorm events and verify the improvements by the one-way linkage approach by comparing the model results with available observations including those of lake ice cover, lake surface temperature, turbulent heat fluxes, and snow water equivalent.

In tandem with the research-to-operations (R2O) transmission of GLOFS-ice to NOS, the efforts of stakeholder and user engagement are growing at CIGLR and GLERL. We will present the outcome of the user engagement workshop held in Cleveland Ohio in July 2019 among the Great Lakes shipping community, U.S. Coast Guard, NOAA, and CIGLR, where the goal was to improve the usability of the upcoming ice forecast guidance from the GLOFS.