Frontogenesis by material convergence in an estuary and its adjacent coastal ocean

Abstract Surface fronts are common features across the world's oceans, particularly in estuarine and coastal regions where the merging of freshwater and saltwater creates strong density gradients. It has long been documented that fronts in these regions can trap and concentrate various properties such as floating debris, nutrients, larvae, and other buoyant materials. The prediction of such fronts has important implications for environmental protection, search and rescue operations, and scientific research. However, the realistic simulation of such features remains a challenge. In this study, we apply a high‐resolution, numerical circulation model of Tampa Bay and the adjacent West Florida Shelf to predict surface fronts by computing surface convergence. The accuracy of the simulation is evaluated using drone and satellite imagery. The simulated convergence fields are then analyzed by a Self‐Organizing Map, an unsupervised machine learning method. Our findings show that convergence patterns vary with tidal phases (ebb and flood) as well as the spring–neap tidal cycle. This study provides a new framework for improving monitoring strategies and reducing observational bias. Although every estuary is unique, the physical mechanisms of frontogenesis are universal. Therefore, the method we propose can be applied to other estuarine systems and serve as a valuable tool for interdisciplinary research in estuarine and coastal environments.