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Article Type:
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Exploring the Potential of Strongly Coupled Lagrangian Data Assimilation in an Ocean–Atmosphere System

Luyu Sun Department of Atmospheric and Oceanic Science, University of Maryland, College Park, College Park, Maryland

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https://orcid.org/0000-0001-5517-6773
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Amit Apte Department of Data Science, Indian Institute of Science Education and Research in Pune, Pune, India
International Centre for Theoretical Sciences - TIFR, Bengaluru, India

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Laura Slivinski NOAA/Physical Sciences Laboratory, Boulder, Colorado

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Elaine T. Spiller Department of Mathematical and Statistical Sciences, Marquette University, Milwaukee, Wisconsin

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Online Publication:
10 Mar 2025
Print Publication:
01 Mar 2025
DOI:
https://doi.org/10.1175/MWR-D-23-0284.1
Page(s):
425–445
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Abstract

Precise measurements of ocean surface flow velocities are essential for refining forecasts in a coupled ocean–atmosphere system. While oceanic data are generally sparse, surface drifters present an opportunity by providing detailed and frequently observed sea surface currents, which are a critical component in the dynamics at air–sea interface. Such observations could potentially address the usual data gaps in a coupled ocean–atmosphere assimilation system. In this study, we investigate the implications of assimilating drifter data within a coupled system with intermediate complexity based on a quasigeostrophic model—Modular Arbitrary-Order Ocean–Atmosphere Model (MAOOAM)—using observing system simulation experiments (OSSEs). Two main strategies for assimilating surface drifter data include the Eulerian approach, which translates Lagrangian positions into Eulerian velocity, and the fully Lagrangian method, which integrates both original fluid states and augmented drifter state variables into the system state vector. We evaluated both Lagrangian and Eulerian drifter assimilation techniques using the ensemble transform Kalman filter (ETKF) across two different coupling intensities within MAOOAM between the atmosphere and the ocean: one featuring strong interaction and the other featuring weak interaction. Our findings indicate a clear advantage of the Lagrangian method over the Eulerian, especially in estimating ocean streamfunctions and temperature. When combined with a large ensemble size and a short data assimilation (DA) window, the Lagrangian ensemble method adeptly manages atmospheric state error propagation. Additionally, as a preliminary demonstration, we evaluated a hybrid particle filter/ensemble Kalman filter (PF/EnKF) approach for Lagrangian DA in the coupled system with long DA windows, which can outperform the EnKF under specific configurations.

© 2025 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Luyu Sun, lysun@umd.edu

Abstract

Precise measurements of ocean surface flow velocities are essential for refining forecasts in a coupled ocean–atmosphere system. While oceanic data are generally sparse, surface drifters present an opportunity by providing detailed and frequently observed sea surface currents, which are a critical component in the dynamics at air–sea interface. Such observations could potentially address the usual data gaps in a coupled ocean–atmosphere assimilation system. In this study, we investigate the implications of assimilating drifter data within a coupled system with intermediate complexity based on a quasigeostrophic model—Modular Arbitrary-Order Ocean–Atmosphere Model (MAOOAM)—using observing system simulation experiments (OSSEs). Two main strategies for assimilating surface drifter data include the Eulerian approach, which translates Lagrangian positions into Eulerian velocity, and the fully Lagrangian method, which integrates both original fluid states and augmented drifter state variables into the system state vector. We evaluated both Lagrangian and Eulerian drifter assimilation techniques using the ensemble transform Kalman filter (ETKF) across two different coupling intensities within MAOOAM between the atmosphere and the ocean: one featuring strong interaction and the other featuring weak interaction. Our findings indicate a clear advantage of the Lagrangian method over the Eulerian, especially in estimating ocean streamfunctions and temperature. When combined with a large ensemble size and a short data assimilation (DA) window, the Lagrangian ensemble method adeptly manages atmospheric state error propagation. Additionally, as a preliminary demonstration, we evaluated a hybrid particle filter/ensemble Kalman filter (PF/EnKF) approach for Lagrangian DA in the coupled system with long DA windows, which can outperform the EnKF under specific configurations.

© 2025 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Luyu Sun, lysun@umd.edu
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