CoDiT: conditional diffusion models for multi-day ocean drifter trajectory prediction

Abstract

Accurate multi-day forecasting of floating-object trajectories on the ocean surface is critical for applications ranging from search-and-rescue to environmental tracking. This task remains however challenging due to the complex interplay of influencing factors such as ocean currents and winds. In this work, we frame trajectory prediction as a denoising task and present Conditional Diffusion models for Trajectories (CoDiT), which adapts the denoising diffusion framework, originally developed for image synthesis, to the problem of trajectory forecasting. CoDiT generates realistic trajectory forecasts, conditioned on heterogeneous context data: ocean currents and winds from reanalysis products, bathymetry, and the initial position. We train and evaluate CoDiT on two global, specialized datasets focusing on the open ocean and coastal regions, using GPS trajectories from the Global Drifter Program as ground truth. We compare CoDiT rigorously against various baselines, including a convolutional neural network that predicts velocity fields, and physical forecasts generated directly from the current and wind fields. Quantitative evaluations show that CoDiT achieves the lowest position error across both datasets and all forecast horizons, and the best probabilistic forecast quality among all methods, as measured by the energy score. Notably, in the coastal setting, CoDiT is the only method to surpass the naive persistence baseline in position error.