The Identification and Prediction of Mesoscale Eddy Variation via Memory in Memory With Scheduled Sampling for Sea Level Anomaly

Rui Nian,Jingyu Wu,Bo He,Hui He,Yu Cai,Yuqi Qian,Zhengguang Zhang,Hua Yang,Xue Geng,Qiang Yuan

doi:10.3389/fmars.2021.753942

Abstract

Ocean mesoscale eddies are ubiquitous in world ocean and account for 90% oceanic kinetic energy, which dominate the upper ocean flow field. Accurately predicting the variation of ocean mesoscale eddies is the key to understand the oceanic flow field and circulation system. In this article, we propose to make an initial attempt to explore spatio-temporal predictability of mesoscale eddies, employing deep learning architecture, which primarily establishes Memory In Memory (MIM) for sea level anomaly (SLA) prediction, combined with the existing mesoscale eddy detection. Oriented to the western Pacific ocean (125°−137.5°E and 15°−27.5°N), we quantitatively investigate the historic daily SLA variability at a 0.25° spatial resolution from 2000 to 2018, derived by satellite altimetry. We develop the enhanced MIM prediction strategies, equipped with Gated Recurrent Unit (GRU) and spatial attention module, in a scheduled sampling manner, which overcomes the gradient vanishing and complements to strengthen spatio-temporal features for long-term dependencies. At the early stage, the real value SLA input guides the model training process for initialization, while the scheduled sampling intentionally feeds the newly predicted value, to resolve the distribution inconsistency of inference. It has been demonstrated in our experiment results that our proposed prediction scheme outperformed the state-of-art approaches for SLA time series, with MAPE, RMSE of the 14-day prediction duration, respectively, 5.1%, 0.023 m on average, even up to 4.6%, 0.018 m for the effective sub-regions, compared to 19.8%, 0.086 m in ConvLSTM and 8.3%, 0.040 m in original MIM, which greatly facilitated the mesoscale eddy prediction. This proposed scheme will be beneficial to understand of the underlying dynamical mechanism behind the predictability of mesoscale eddies in the future, and help the deployment of ARGO, glider, AUV and other observational platforms.

Highlights

Ocean mesoscale eddies, the rotating vortices with typical horizontal scales of tens to hundreds km and timescales on the order of weeks to months, are ubiquitous in world ocean
We further focus on constructing one spatial attention mechanism to refrain from the connection structure mismatching within the convolution operation for the enhanced Memory In Memory (MIM) scheme
Since it is of great importance to implementing deep learning with sufficient samples in appropriate models, we introduced a large number of extra sea level anomaly (SLA) time series for transfer learning, respectively from the regions located at the same latitude of our study area, as is shown in Figure 5B, with our study area in black rectangle, and the other regions in yellow rectangle

Summary

INTRODUCTION

The rotating vortices with typical horizontal scales of tens to hundreds km and timescales on the order of weeks to months, are ubiquitous in world ocean. In order to implement SLA prediction with long-term dependencies, we first develop one enhanced MIM mechanism, with GRU and spatial attention module, which makes a trade-off between model complexity and prediction accuracy for SLA time series highly associated with spatio-temporal variability of mesoscale eddies. Such deep learning scheme first applies the basic idea of stacking multiple MIM blocks to potentially handle the predictability of higher-order non-stationarity in SLA time series. Need to differentiate the SSH fields and avoiding the associated deleterious effects of noise that are problematic in numerous previous studies

SIMULATION EXPERIMENT AND RESULTS ANALYSIS

Evaluation criterion

CONCLUSION