Altimeter Observation-Based Eddy Nowcasting Using an Improved Conv-LSTM Network

Abstract

Eddies can be identified and tracked based on satellite altimeter data. However, few studies have focused on nowcasting the evolution of eddies using remote sensing data. In this paper, an improved Convolutional Long Short-Term Memory (Conv-LSTM) network named Prednet is used for eddy nowcasting. Prednet, which uses a deep, recurrent convolutional network with both bottom-up and top-down connects, has the ability to learn the temporal and spatial relationships associated with time series data. The network can effectively simulate and reconstruct the spatiotemporal characteristics of the future sea level anomaly (SLA) data. Based on the SLA data products provided by Archiving, Validation, and Interpretation of Satellite Oceanographic (AVISO) from 1993 to 2018, combined with an SLA-based eddy detection algorithm, seven-day eddy nowcasting experiments are conducted on the eddies in South China Sea. The matching ratio is defined as the percentage of true eddies that can be successfully predicted by Conv-LSTM network. On the first day of the nowcasting, matching ratio for eddies with diameters greater than 100 km is 95%, and the average matching ratio of the seven-day nowcasting is approximately 60%. In order to verify the performance of nowcasting method, two experiments were set up. A typical anticyclonic eddy shedding from Kuroshio in January 2017 was used to verify this nowcasting algorithm’s performance on single eddy, with the mean eddy center error is 11.2 km. Moreover, compared with the eddies detected in the Hybrid Coordinate Ocean Model data set (HYCOM), the eddies predicted with Conv-LSTM networks are closer to the eddies detected in the AVISO SLA data set, indicating that deep learning method can effectively nowcast eddies.

Keywords

• remote sensing data
• mesoscale oceanic eddies
• deep learning
• convolution LSTM network