Massively Parallel Implicit Equal-Weights Particle Filter for Ocean Drift Trajectory Forecasting

TL;DR

This paper introduces a massively parallel implicit equal-weights particle filter for ocean drift forecasting, enabling faster, more accurate short-term predictions using simplified models and sparse observational data.

Contribution

It presents a novel algorithmic design leveraging GPU architectures for data assimilation in ocean models, improving computational speed and forecast accuracy.

Findings

Sparse drifter data significantly improve 12-hour forecasts.

Equidistant moored buoys with 0.1% coverage yield accurate probabilistic forecasts.

Parallel implementation enables real-time and large ensemble simulations.

Abstract

Forecasting ocean drift trajectories are important for many applications, including search and rescue operations, oil spill cleanup and iceberg risk mitigation. In an operational setting, forecasts of drift trajectories are produced based on computationally demanding forecasts of three-dimensional ocean currents. Herein, we investigate a complementary approach for shorter time scales by using a recent state-of-the-art implicit equal-weights particle filter applied to a simplified ocean model. To achieve this, we present a new algorithmic design for a data-assimilation system in which all components - including the model, model errors, and particle filter - take advantage of massively parallel compute architectures, such as graphical processing units. Faster computations can enable in-situ and ad-hoc model runs for emergency management, and larger ensembles for better uncertainty quantification. Using a challenging test case with near-realistic chaotic instabilities, we run data-assimilation experiments based on synthetic observations from drifting and moored buoys, and analyse the trajectory forecasts for the drifters. Our results show that even sparse drifter observations are sufficient to significantly improve short-term drift forecasts up to twelve hours. With equidistant moored buoys observing only 0.1% of the state space, the ensemble gives an accurate description of the true state after data assimilation followed by a high-quality probabilistic forecast.