Lagrangian Data Assimilation of Surface Drifters in a Double-Gyre Ocean Model Using the Local Ensemble Transform Kalman Filter

Sun, Luyu; Penny, Stephen G.

doi:10.1175/MWR-D-18-0406.1

i

Lagrangian Data Assimilation of Surface Drifters in a Double-Gyre Ocean Model Using the Local Ensemble Transform Kalman Filter

2019
By Sun, Luyu ; Penny, Stephen G.

Details

Journal Title:

Monthly Weather Review
Personal Author:

Sun, Luyu ; Penny, Stephen G.
NOAA Program & Office:

CIRES (Cooperative Institute for Research in Environmental Sciences) ; OAR (Oceanic and Atmospheric Research) ; CPO (Climate Program Office) ; ESRL (Earth System Research Laboratory) ; PSL (Physical Sciences Laboratory) ; NWS (National Weather Service) ; NESDIS (National Environmental Satellite, Data, and Information Service)
Description:

The assimilation of position data from Lagrangian observing platforms is underdeveloped in operational applications because of two main challenges: 1) nonlinear growth of model and observation error in the Lagrangian trajectories, and 2) the high dimensionality of realistic models. In this study, we propose a localized Lagrangian data assimilation (LaDA) method that is based on the local ensemble transform Kalman filter (LETKF). The algorithm is tested with an “identical twin” approach in observing system simulation experiments (OSSEs) using a simple double-gyre configuration of the Geophysical Fluid Dynamics Laboratory (GFDL) Modular Ocean Model. Results from the OSSEs show that with a proper choice of localization radius, the LaDA can outperform conventional assimilation of surface in situ temperature and salinity measurements. The improvements are seen not only in the surface state estimate, but also throughout the ocean column to 1000 m depth. The impacts of localization radius and model error in estimating accuracy of both fluid and drifter states are further investigated.
Keywords:

Modelling Observations Oceanography
Source:

Mon. Wea. Rev. (2019) 147 (12): 4533–4551
DOI:

https://doi.org/10.1175/MWR-D-18-0406.1
Document Type:

Journal Article
Funding:

Grant no. NA14NES4320003 ; Grant no. NA16OAR4310140 ; Grant no. NA18NWS4680048 ; Grant no. NA16NWS4680011
Rights Information:

Other
Compliance:

Submitted
Main Document Checksum:

urn:sha-512:2e5aea094ba685746ec519f37a0ed05510de3e6eb0f59d0d0b6984130ab6c22a2187469df30dad76727fbc569c62061a9f9383af9f27a86d3e96a60e55991e55
Download URL:

https://repository.library.noaa.gov/view/noaa/25127/noaa_25127_DS1.pdf
File Type:

[PDF - 1.89 MB ]

ON THIS PAGE

Details

The NOAA IR serves as an archival repository of NOAA-published products including scientific findings, journal articles, guidelines, recommendations, or other information authored or co-authored by NOAA or funded partners. As a repository, the NOAA IR retains documents in their original published format to ensure public access to scientific information.