Medium-Range Trajectory Prediction Network Compliant to Physical Constraint for Oceanic Eddy

Abstract

Predicting the trajectory of ocean eddies can promote the understanding of the transport of matter and energy in the ocean. However, accurately and rapidly predicting the trajectory of eddies poses a significant challenge due to their intricate nonlinear motion within a physical environment. Regrettably, existing data-driven methods primarily focus on the migration and combination of models, as well as the fusion processing of diverse observational data on oceanic eddies. These ways often overlook the crucial aspect of modeling the underlying motion mechanism of the eddies. We believe that the expeditious and precise prediction of eddies is closely intertwined with the physical mechanism and historical time series. Consequently, a medium-range eddy trajectory prediction neural network (ETPNet) compliant with the physical constraint is proposed, which embeds the physical regulation, intrinsic relations, and mutual interactions into the network via constraints. Then, a novel variant of the long short-term memory (LSTM) cell is designed to enhance the dynamic interaction and representation ability of the features, constraints, and knowledge. Finally, a geographically informed comprehensive loss function for marine tasks is formulated, namely mean absolute geodetic error (MAGE), which optimizes the network in Euclidean and sphere space. The proposed network is evaluated by predicting the future seven days trajectory of anticyclone eddies in the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$15^{\circ }\text{N}$ </tex-math></inline-formula> to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$40^{\circ }\text{N}$ </tex-math></inline-formula> . The extensive experiments and evaluations demonstrate that the proposed network guided by the comprehensive loss function can implement a state-of-the-art performance. The code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/AI4Ocean/ETPNet</uri>

Topics & Concepts