Massively parallel implicit equal-weights particle filter for ocean drift trajectory forecasting

Håvard Heitlo Holm,Martin Lilleeng Sætra,Peter Jan Van Leeuwen

doi:10.1016/j.jcpx.2020.100053

Abstract

Forecasting of 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 the recently proposed two-stage 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 analyze 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.

Highlights

Prediction of drift trajectories in the ocean has many applications that are important to society and the environment
Paper contribution We present an efficient graphical processing unit (GPU)-implementation of the recent implicit equal-weights particle filter applied to a simplified ocean model
This is followed by an illustration of how the standard particle filter collapses for the same case, even when starting from an ensemble that is centered around the true state with low variance

Summary

Introduction

Prediction of drift trajectories in the ocean has many applications that are important to society and the environment. To produce high-quality drift trajectory forecasts, it is important to have a good representation of ocean currents This is not an easy task, as ocean currents have large natural variability and there are typically few available observations. The operational approach for drift trajectory prediction is to use the currents from the most recent ocean forecasts directly [1] These are imported from computationally expensive ocean circulation models, which solve the dynamic state of the ocean in three dimensions. The aim is to build a data-assimilation system that can run efficiently on commodity-level desktop computers, and be extendable to supercomputers We achieve this by using a simplified ocean model and a data-assimilation method that both are able to take advantage of massively parallel accelerator hardware, such as the graphical processing unit (GPU). This work will contribute to more efficient simulations on supercomputers, since all algorithms may be extended to run on multiple GPUs and compute nodes

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