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.
Highlights
Mesoscale eddies can be defined as rotating water masses that play an important role in the transport of marine materials [1]
The Kuroshio intrudes into the South China Sea through the Luzon Strait with the waters of the Northwest Pacific Ocean, making an important contribution to the dynamic, thermal and salt balance of the South China Sea [35]
Only the time series data collected by remote sensing altimeters are used as inputs, and an empirical model is established using an improved Conv-LSTM network to output the future sea level anomaly (SLA) maps
Summary
Mesoscale eddies can be defined as rotating water masses that play an important role in the transport of marine materials [1]. In 1984, Robinson et al [16] published the first mesoscale eddy forecast using an observation network, a statistical model of an anisotropic mixed space-time target analysis scheme, and a dynamic model of the baroclinic quasi-ground rotation associated with airflow They successfully predicted the merging, weakening, and disappearance of an anticyclonic eddy in the California ocean current system over a two-week period and the enlarging and strengthening of another cyclonic eddy. In 1994, Masina and Pinardi [17] presented a quasi-geostrophic numerical model of the initial field of the Adriatic Sea region with a regular grid through objective analysis technology and made a 30-day dynamic prediction of an eddy in the eddy field. Shriver et al (2007) [18] increased the resolution of the forecasting system from 1/16◦ [19] to 1/32◦ based on the Navy
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