Editorial Type:
Article
Article Type:
Research Article

Do Assimilated Drifter Velocities Improve Lagrangian Predictability in an Operational Ocean Model?

Philip Muscarella Naval Research Laboratory, Stennis Space Center, Mississippi

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Matthew J. Carrier Naval Research Laboratory, Stennis Space Center, Mississippi

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Hans Ngodock Naval Research Laboratory, Stennis Space Center, Mississippi

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Scott Smith Naval Research Laboratory, Stennis Space Center, Mississippi

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B. L. Lipphardt Jr. School of Marine Science and Policy, University of Delaware, Newark, Delaware

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A. D. Kirwan Jr. School of Marine Science and Policy, University of Delaware, Newark, Delaware

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Helga S. Huntley School of Marine Science and Policy, University of Delaware, Newark, Delaware

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Print Publication:
01 May 2015
DOI:
https://doi.org/10.1175/MWR-D-14-00164.1
Page(s):
1822–1832
Restricted access

Abstract

The Lagrangian predictability of general circulation models is limited by the need for high-resolution data streams to constrain small-scale dynamical features. Here velocity observations from Lagrangian drifters deployed in the Gulf of Mexico during the summer 2012 Grand Lagrangian Deployment (GLAD) experiment are assimilated into the Naval Coastal Ocean Model (NCOM) 4D variational (4DVAR) analysis system to examine their impact on Lagrangian predictability. NCOM-4DVAR is a weak-constraint assimilation system using the indirect representer method. Velocities derived from drifter trajectories, as well as satellite and in situ observations, are assimilated. Lagrangian forecast skill is assessed using separation distance and angular differences between simulated and observed trajectory positions. Results show that assimilating drifter velocities substantially improves the model forecast shape and position of a Loop Current ring. These gains in mesoscale Eulerian forecast skill also improve Lagrangian forecasts, reducing the growth rate of separation distances between observed and simulated drifters by approximately 7.3 km day−1 on average, when compared with forecasts that assimilate only temperature and salinity observations. Trajectory angular differences are also reduced.

Corresponding author address: Philip Muscarella, Naval Research Lab, 1009 Balch Blvd., Stennis Space Center, MS 39522-5001. E-mail: philip.muscarella@nrlssc.navy.mil

Abstract

The Lagrangian predictability of general circulation models is limited by the need for high-resolution data streams to constrain small-scale dynamical features. Here velocity observations from Lagrangian drifters deployed in the Gulf of Mexico during the summer 2012 Grand Lagrangian Deployment (GLAD) experiment are assimilated into the Naval Coastal Ocean Model (NCOM) 4D variational (4DVAR) analysis system to examine their impact on Lagrangian predictability. NCOM-4DVAR is a weak-constraint assimilation system using the indirect representer method. Velocities derived from drifter trajectories, as well as satellite and in situ observations, are assimilated. Lagrangian forecast skill is assessed using separation distance and angular differences between simulated and observed trajectory positions. Results show that assimilating drifter velocities substantially improves the model forecast shape and position of a Loop Current ring. These gains in mesoscale Eulerian forecast skill also improve Lagrangian forecasts, reducing the growth rate of separation distances between observed and simulated drifters by approximately 7.3 km day−1 on average, when compared with forecasts that assimilate only temperature and salinity observations. Trajectory angular differences are also reduced.

Corresponding author address: Philip Muscarella, Naval Research Lab, 1009 Balch Blvd., Stennis Space Center, MS 39522-5001. E-mail: philip.muscarella@nrlssc.navy.mil
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