BAYESIAN OPTIMIZATION OF THE METHOD OF IDENTIFICATION OF MESOSCALE OCEAN EDDIES IN THE LABRADOR SEA IN EDDY-RESOLVING MODELLING DATA

Deep convection in the Labrador Sea has a significant role in the formation of the Northern Hemisphere climate. The eddy activity in the Labrador Sea, represented by Irminger rings, influences the spatial and temporal heterogeneity of the mixed layer depth. Automated eddy identification methods are widely used as a tool to study eddy activity in statistically significant samples. However, the most commonly used method for finding local extrema is highly dependent on a variety of parameters chosen by the author or the user of the method. In this paper, a new algorithm for the identification of mesoscale anticyclonic eddies in numerical simulation data is developed. Using Bayesian optimisation method, the optimal values of hyperparameters of the developed eddy identification algorithm were selected. The identification quality in the F1-score measure is improved to 0.232 compared to 0.352 in the baseline configuration.