Multivariate Upstream Kuroshio Transport (UKT) Prediction and Targeted Observation Sensitive Area Identification of UKT Seasonal Reduction

Abstract

Variation and seasonal reduction in the Upstream Kuroshio Transport (UKT) have important impacts on surrounding climate and oceanic circulation systems. Therefore, reliable UKT prediction is crucial. In this paper, we propose an intelligent UKT prediction model, KuroshioNet, which is firstly pre-trained with simulation data generated by the Regional Ocean Modeling System (ROMS) and then fine-tuned with reanalysis data of the Simple Ocean Data Assimilation (SODA). Operating at a five-day time resolution and a 0.5°spatial resolution, KuroshioNet has the capability to predict multivariate fields associated with upstream Kuroshio, including 3D variables like velocity, temperature, as well as salinity and 2D variables like sea surface height. Subsequently, the UKT is computed from the predicted fields. We evaluate and analyze the experimental results, which show that KuroshioNet has a lead time of 55 days for UKT prediction. In order to enhance the physical interpretability of KuroshioNet, we conduct an ablation experiment to evaluate the impact of each predictor on prediction skill. It demonstrates that selecting zonal velocity, meridional velocity, temperature, salinity, and SSH contributes to UKT prediction by KuroshioNet. Besides, by analyzing model performance and visualizing what the convolutional kernels learn, we find that KuroshioNet, which has learned from ROMS data, is capable of obtaining better initial performance and acquiring more active kernels to better learn the features in SODA data. Furthermore, we identify the targeted observation sensitive area of UKT seasonal reduction by KuroshioNet with the saliency map method, which is situated to the east of upstream kuroshio. The sensitive area is consistent with the result identified by numerical models and yields 38.1% improvement on prediction demonstrated by observing system simulation experiments.