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Nonlinear Processes in Geophysics An interactive open-access journal of the European Geosciences Union
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Volume 20, issue 5
Nonlin. Processes Geophys., 20, 621-641, 2013
https://doi.org/10.5194/npg-20-621-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.

Special issue: Ensemble methods in geophysical sciences

Nonlin. Processes Geophys., 20, 621-641, 2013
https://doi.org/10.5194/npg-20-621-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 11 Sep 2013

Research article | 11 Sep 2013

The impact of initial spread calibration on the RELO ensemble and its application to Lagrangian dynamics

M. Wei1, G. Jacobs1, C. Rowley1, C. N. Barron1, P. Hogan1, P. Spence2, O. M. Smedstad2, P. Martin1, P. Muscarella3, and E. Coelho4 M. Wei et al.
  • 1Naval Research Laboratory, Stennis Space Center, MS, USA
  • 2Qinetiq-North America, Stennis Space Center, MS, USA
  • 3ASEE Postdoctoral Program, Naval Research Laboratory, Stennis Space Center, MS, USA
  • 4University of New Orleans at NRL, Stennis Space Center, MS, USA

Abstract. A number of real-time ocean model forecasts were carried out successfully at Naval Research Laboratory (NRL) to provide modeling support and numerical guidance to the CARTHE GLAD at-sea experiment during summer 2012. Two RELO ensembles and three single models using NCOM and HYCOM with different resolutions were carried out. A calibrated ensemble system with enhanced spread and reliability was developed to better support this experiment. The calibrated ensemble is found to outperform the un-calibrated ensemble in forecasting accuracy, skill, and reliability for all the variables and observation spaces evaluated. The metrics used in this paper include RMS error, anomaly correlation, PECA, Brier score, spread reliability, and Talagrand rank histogram. It is also found that even the un-calibrated ensemble outperforms the single forecast from the model with the same resolution.

The advantages of the ensembles are further extended to the Lagrangian framework. In contrast to a single model forecast, the RELO ensemble provides not only the most likely Lagrangian trajectory for a particle in the ocean, but also an uncertainty estimate that directly reflects the complicated ocean dynamics, which is valuable for decision makers. The examples show that the calibrated ensemble with more reliability can capture trajectories in different, even opposite, directions, which would be missed by the un-calibrated ensemble. The ensembles are applied to compute the repelling and attracting Lagrangian coherent structures (LCSs), and the uncertainties of the LCSs, which are hard to obtain from a single model forecast, are estimated. It is found that the spatial scales of the LCSs depend on the model resolution. The model with the highest resolution produces the finest, small-scale, LCS structures, while the model with lowest resolution generates only large-scale LCSs. The repelling and attracting LCSs are found to intersect at many locations and create complex mesoscale eddies. The fluid particles and drifters in the middle of these tangles are subject to attraction and repulsion simultaneously from these two kinds of LCSs. As a result, the movements of particles near the Deepwater Horizon (DWH) location are severely limited. This is also confirmed by the Lagrangian trajectories predicted by the ensembles.

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