AB-LSTM: a mesoscale eddy feature prediction method based on an improved Conv-LSTM model

Abstract

Mesoscale eddies are the most important mesoscale phenomena in the oceans, and determining how to predict their spatial and temporal characteristics is a very challenging task. Most previous studies focused on the accuracy of full-domain prediction and ignored the accuracy of single-eddy prediction. To solve this problem, in this paper, we first apply multi-year sea surface height data to produce a spatiotemporal sequence sample dataset with a bidirectional prediction mechanism. Then, we introduce an adversarial generative mechanism through stacked spatiotemporal prediction blocks and rely on the strong generative ability of the generative adversarial network models to construct an adversarial bidirectional long- and short-term memory model (AB-LSTM). Next, the mesoscale eddy mixing algorithm is used to extract the matching eddy pair features from the real and predicted data, and several evaluation metrics are used to conduct error analysis. The experiments yield the following results. Prediction sequence days 1–7: the root mean square error (RMSE) values are 1.97–7.70 cm, the structural similarity index (SSIM) values are >0.61, the accuracy is >54.6%, and the eddy centre distance error is 6.34 km. The result is 11.61 km, which is consistent with many spatiotemporal prediction models and passes the generalisation test in many different sea areas. Finally, we carry out single eddy prediction on the basis of the evaluation of the entire prediction of the sea surface height and also obtain a more satisfactory experimental effect. This method has a better prediction ability than the original spatiotemporal method and has a certain reference significance for mesoscale eddy spatiotemporal feature prediction technology and subsequent underwater reconstruction.