Please use this identifier to cite or link to this item:
http://dr.iiserpune.ac.in:8080/xmlui/handle/123456789/9399
Title: | Exploring the Potential of Strongly Coupled Lagrangian Data Assimilation in an Ocean–Atmosphere System |
Authors: | Sun, Luyu APTE, AMIT Slivinski, Laura Spiller, Elanine T. Dept. of Data Science |
Keywords: | Buoy observations Surface observations Numerical weather prediction/forecasting Coupled models Data assimilation 2025-MAR-WEEK3 TOC-MAR-2025 2025 |
Issue Date: | Mar-2025 |
Publisher: | American Meteorological Society |
Citation: | Monthly Weather Review, 153(03), 425–445. |
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. |
URI: | https://doi.org/10.1175/MWR-D-23-0284.1 http://dr.iiserpune.ac.in:8080/xmlui/handle/123456789/9399 |
ISSN: | 0027-0644 1520-0493 |
Appears in Collections: | JOURNAL ARTICLES |
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.