Data assimilation considerations for improved ocean predictability during the Gulf of Mexico Grand Lagrangian Deployment (GLAD)

Francisco J Beron-Vera; Gregg A Jacobs; Brent P Bartels; Milan Curcic; Darek J Bogucki; Brian K Haus; Angelique C Haza; Shuyi S Chen; Mohamed Iskandarani; Emanuel F Coelho; Arthur J. Mariano; Guillaume Novelli; Annalisa Griffa; M. Josefina Olascoaga; Matthew Gough; Tamay M Özgökmen; Edward H Ryan; Robert W Helber; Patrick J Hogan; Helga S Huntley; Falko Judt; A.D Kirwan; Nathan Laxague; Arnoldo Valle-Levinson; Bruce L Lipphardt; Hans E Ngodock; Andrew C Poje; Ad J.H.M Reniers; Clark D Rowley; Scott R Smith; Peter L Spence; Prasad G Thoppil; Mozheng Wei

doi:10.1016/j.ocemod.2014.09.003

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Data assimilation considerations for improved ocean predictability during the Gulf of Mexico Grand Lagrangian Deployment (GLAD)

Journal article

Peer reviewed

Data assimilation considerations for improved ocean predictability during the Gulf of Mexico Grand Lagrangian Deployment (GLAD)

Francisco J Beron-Vera, Gregg A Jacobs, Brent P Bartels, Milan Curcic, Darek J Bogucki, Brian K Haus, Angelique C Haza, Shuyi S Chen, Mohamed Iskandarani, Emanuel F Coelho, …

Ocean modelling (Oxford), Vol.83, pp.98-117

2014-11

DOI: https://doi.org/10.1016/j.ocemod.2014.09.003

Abstract

Drifter

Ocean

Covariance

Modeling

Assimilation

Gulf of Mexico

•Extensive drifter observations allow new understanding to data assimilation.•Background error covariance is the point at which assumptions have historically been placed.•Components of background error covariance are tested to determine impact.•Amplitude of background error covariance is a critical factor.•Time correlation in background errors must be considered in 3DVar and 4DVar. Ocean prediction systems rely on an array of assumptions to optimize their data assimilation schemes. Many of these remain untested, especially at smaller scales, because sufficiently dense observations are very rare. A set of 295 drifters deployed in July 2012 in the north-eastern Gulf of Mexico provides a unique opportunity to test these systems down to scales previously unobtainable. In this study, background error covariance assumptions in the 3DVar assimilation process are perturbed to understand the effect on the solution relative to the withheld dense drifter data. Results show that the amplitude of the background error covariance is an important factor as expected, and a proposed new formulation provides added skill. In addition, the background error covariance time correlation is important to allow satellite observations to affect the results over a period longer than one daily assimilation cycle. The results show the new background error covariance formulations provide more accurate placement of frontal positions, directions of currents and velocity magnitudes. These conclusions have implications for the implementation of 3DVar systems as well as the analysis interval of 4DVar systems.

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Collaboration types: Domestic collaboration; International collaboration
Citation topics: 8 Earth Sciences; 8.19 Oceanography, Meteorology & Atmospheric Sciences; 8.19.153 Internal Waves
Web Of Science research areas: Meteorology & Atmospheric Sciences; Oceanography
ESI research areas: Geosciences

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Details

Title: Data assimilation considerations for improved ocean predictability during the Gulf of Mexico Grand Lagrangian Deployment (GLAD)
Creators: Francisco J Beron-Vera - Rosenstiel School of Marine & Atmospheric Science, University of Miami, Miami, FL 33149, United States
Gregg A Jacobs - Naval Research Laboratory, Stennis Space Center, MS, 39529, United States
Brent P Bartels - Vencore Services and Solutions, Inc., Stennis Space Center, MS 39529, United States
Milan Curcic - Rosenstiel School of Marine & Atmospheric Science, University of Miami, Miami, FL 33149, United States
Darek J Bogucki - Department of Physical & Environmental Sciences, College of Science and Engineering, Texas A&M University, Corpus Christi, TX 78412, United States
Brian K Haus - Rosenstiel School of Marine & Atmospheric Science, University of Miami, Miami, FL 33149, United States
Angelique C Haza - Rosenstiel School of Marine & Atmospheric Science, University of Miami, Miami, FL 33149, United States
Shuyi S Chen - Rosenstiel School of Marine & Atmospheric Science, University of Miami, Miami, FL 33149, United States
Mohamed Iskandarani - Rosenstiel School of Marine & Atmospheric Science, University of Miami, Miami, FL 33149, United States
Emanuel F Coelho - Naval Research Laboratory, Stennis Space Center, MS, 39529, United States
Arthur J. Mariano - Rosenstiel School of Marine & Atmospheric Science, University of Miami, Miami, FL 33149, United States
Guillaume Novelli - Rosenstiel School of Marine & Atmospheric Science, University of Miami, Miami, FL 33149, United States
Annalisa Griffa - Rosenstiel School of Marine & Atmospheric Science, University of Miami, Miami, FL 33149, United States
M. Josefina Olascoaga - Rosenstiel School of Marine & Atmospheric Science, University of Miami, Miami, FL 33149, United States
Matthew Gough - Rosenstiel School of Marine & Atmospheric Science, University of Miami, Miami, FL 33149, United States
Tamay M Özgökmen - Rosenstiel School of Marine & Atmospheric Science, University of Miami, Miami, FL 33149, United States
Edward H Ryan - Rosenstiel School of Marine & Atmospheric Science, University of Miami, Miami, FL 33149, United States
Robert W Helber - Naval Research Laboratory, Stennis Space Center, MS, 39529, United States
Patrick J Hogan - Naval Research Laboratory, Stennis Space Center, MS, 39529, United States
Helga S Huntley - School of Marine Science and Policy, University of Delaware, Newark, DE 19716, United States
Falko Judt - Rosenstiel School of Marine & Atmospheric Science, University of Miami, Miami, FL 33149, United States
A.D Kirwan - School of Marine Science and Policy, University of Delaware, Newark, DE 19716, United States
Nathan Laxague - Rosenstiel School of Marine & Atmospheric Science, University of Miami, Miami, FL 33149, United States
Arnoldo Valle-Levinson - Department of Civil and Coastal Engineering, University of Florida, Gainesville, FL 32611, United States
Bruce L Lipphardt - School of Marine Science and Policy, University of Delaware, Newark, DE 19716, United States
Hans E Ngodock - Naval Research Laboratory, Stennis Space Center, MS, 39529, United States
Andrew C Poje - Department of Mathematics, College of Staten Island, City University of New York, NY 10314, United States
Ad J.H.M Reniers - Rosenstiel School of Marine & Atmospheric Science, University of Miami, Miami, FL 33149, United States
Clark D Rowley - Naval Research Laboratory, Stennis Space Center, MS, 39529, United States
Scott R Smith - Naval Research Laboratory, Stennis Space Center, MS, 39529, United States
Peter L Spence - Vencore Services and Solutions, Inc., Stennis Space Center, MS 39529, United States
Prasad G Thoppil - Naval Research Laboratory, Stennis Space Center, MS, 39529, United States
Mozheng Wei - Naval Research Laboratory, Stennis Space Center, MS, 39529, United States
Publication Details: Ocean modelling (Oxford), Vol.83, pp.98-117
Publisher: Elsevier Ltd
Academic Unit: Rosenstiel School; Rosenstiel - Ocean Sciences; Rosenstiel - Atmospheric Sciences; Rosenstiel - CIMAS
Language: English
Resource Type: Journal article
Record Identifier: 991031577067202976