Abstract
Mesoscale eddies play an important role in ocean circulation, material energy exchange and variation of ocean environments. Machine learning methods can efficiently process massive amounts of data and automatically learn the implicit features, thus providing a new approach to eddy prediction research. Using the mesoscale eddy trajectory data derived from multimission satellite altimetry, we propose relevant machine learning models based on long short-term memory network (LSTM) and the extra trees (ET) algorithm for the prediction of eddy properties and propagation trajectories. Characteristic factors, including attribute features and past eddy displacements, were exploited to construct prediction models with high effectiveness and few predictors. To study their effects at different forecasting times, we separately trained the models by rebuilding the corresponding relationship between eddy samples and labels. In addition, the variation characteristics and the predictability of eddy properties and propagation trajectories were discussed from the prediction results. Cross-validation shows that at different prediction times, our method is superior to previous methods in terms of the mean absolute error (MAE) of eddy properties and the root mean square error (RMSE) of propagation. The stable variation in eddy properties makes the prediction more dependent on the historical time series than that of a propagation forecast. The short-term propagation prediction of eddies contained more noise than long-term predictions, and the long-term predictions revealed a more significant trend. Finally, we discuss the effect of eddy properties on the prediction ability of the eddy propagation trajectory.
Highlights
Mesoscale eddies play an important role in the mixing and transport of momentum, heat, mass and biogeochemical tracers in the global ocean [1,2,3]
The long short-term memory network (LSTM) network consisted of several processing layers that were used to continuously extract abstract features from the input data and match these features with the targets learned through regression tasks
The prediction performance of eddy amplitude was significantly improved compared with that of Ma et al [8], with the mean absolute error (MAE) of the radius increasing from 10.6 km to 23.3 km and the MAE of the maximum circum-average (MCA) speed increasing small.1.3
Summary
Mesoscale eddies play an important role in the mixing and transport of momentum, heat, mass and biogeochemical tracers in the global ocean [1,2,3]. Eddies have a critical influence on rainfall, near-surface wind, clouds and marine ecosystems in their vicinity [4,5,6]. The accurate prediction of eddies is of great scientific and applied significance for understanding eddy propagation and evolution characteristics and improving simulations and predictions of regional weather and climate change [7,8]. The main methods used for oceanic mesoscale prediction can be divided into three categories: dynamic statistical methods, numerical methods and machine learning methods. Ocean dynamic models have been used to forecast the evolution of ocean eddies.
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