Toward Operational Real-time Identification of Frontal Boundaries Using Machine Learning

We present and evaluate a deep learning first-guess front identification system that identifies cold, warm, stationary, and occluded fronts. Frontal boundaries play a key role in the daily weather around the world. Human-drawn fronts provided by the National Weather Service’s Weather Prediction Center, Ocean Prediction Center, Tropical Analysis & Forecast Branch, and Honolulu Forecast Office are treated as ground truth labels for training the deep learning models. The models are trained using ERA5 reanalysis data with variables known to be important to distinguishing frontal boundaries, including temperature, equivalent potential temperature, and wind velocity and direction at multiple heights. Using a 250 km neighborhood over the Continental United States domain, our best models achieve critical success index scores of 0.60 for cold fronts, 0.43 for warm fronts, 0.48 for stationary fronts, 0.45 for occluded fronts, and 0.71 using a binary classification system (front / no front), while scores over the full Unified Surface Analysis domain were lower. For cold and warm fronts and binary classification, these scores significantly outperform prior baseline methods that utilize 250 km neighborhoods. These first-guess deep learning algorithms can be used by forecasters to more effectively locate frontal boundaries and expedite the frontal analysis process.