Coastal ice conditions have important impacts on the economy, recreation, and ecology. However, there are very few observations of ice thickness available in the Great Lakes and coastal ocean, which limits our ability to assess how well coastal forecast models predict ice conditions. This project will use novel satellite-derived ice conditions to validate model predictions for Alaska and the Great Lakes regions.
Why We Care
Coastal ice influences navigation, weather, ecosystem dynamics, and emergency response, yet reliable observations of ice thickness — critical for improving coastal forecast models — remain scarce. Limited ice thickness data hinders forecast validation and model refinement.
What We Are Doing
This project will leverage new algorithms, recently developed by the project team, to derive ice classification and thickness for key coastal regions. These satellite-derived datasets will help enhance assessment of modeled coastal ice conditions. The work will quantify model skill and generate open datasets and evaluation tools for NOAA and Cooperative Institute for Research to Operations in Hydrology (CIROH) partners.
Benefits of Our Work
Anticipated outcomes include improved understanding of coastal and Great Lakes model performance, enhanced coastal temperature and ice prediction, and strengthened research to operations integration across NOAA’s National Ocean Service and Office of Water Prediction and CIROH initiatives.
The project is led by Dr. Eric Anderson of the Colorado School of Mines. The project is part of CIROH.