Articles | Volume 11, issue 2
https://doi.org/10.5194/os-11-237-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/os-11-237-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
11 Mar 2015
Research article |
| 11 Mar 2015
Constraining energetic slope currents through assimilation of high-frequency radar observations
A. K. Sperrevik
CORRESPONDING AUTHOR
Norwegian Meteorological Institute, Oslo, Norway
K. H. Christensen
Norwegian Meteorological Institute, Oslo, Norway
Norwegian Meteorological Institute, Oslo, Norway
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Cited articles
Albretsen, J., Sperrevik, A. K., Staalstrøm, A., Sandvik, A. D., Vikebø, F., and Asplin, L.: NorKyst-800 report no. 1: User manual and technical descriptions, Tech. Rep. 2, Institute of Marine Research, Bergen, Norway, available at: http://www.imr.no/filarkiv/2011/07/fh_2-2011_til_web.pdf/nb-no (last access: 5 March 2015), 2011.
Barrick, D. E., Evans, M. W., and Weber, B. L.: Ocean surface currents mapped by radar, Science, 198, 138–144, 1977.
Barth, A., Alvera-Azcarate, A., and Weisberg, R. H.: Assimilation of high-frequency radar currents in a nested model of the West Florida Shelf, J. Geophys. Res., 113, C08033, https://doi.org/10.1029/2007JC004585, 2008.
Blockley, E. W., Martin, M. J., McLaren, A. J., Ryan, A. G., Waters, J., Lea, D. J., Mirouze, I., Peterson, K. A., Sellar, A., and Storkey, D.: Recent development of the Met Office operational ocean forecasting system: an overview and assessment of the new Global FOAM forecasts, Geosci. Model Dev., 7, 2613–2638, https://doi.org/10.5194/gmd-7-2613-2014, 2014.
Brassington, G. B., Pugh, T., Spillman, C., Schulz, E., Beggs, H., Schiller, A., and Oke, P. R.: BLUElink> Development of operational oceanography and servicing, J. Res. Pract. Inf. Tech., 39, 151–164, 2007.
Breivik, Ø. and Saetra, Ø.: Real time assimilation of HF radar currents into a coastal ocean model, J. Marine Syst., 28, 161–182, 2001.
Broquet, G., Edwards, C. A., Moore, A. M., Powell, B. S., Veneziani, M., and Doyle, J. D.: Application of 4D-Variational data assimilation to the California Current System, Dynam. Atmos. Oceans, 48, 69–92, 2009.
Chapman, D. C.: Numerical treatment of cross-shelf open boundaries in a barotropic coastal ocean model, J. Phys. Oceanogr., 15, 1060–1075, 1985.
Chapman, R. D., Shay, L. K., Graber, H. C., Edson, J. B., Karachintsev, A., Trump, C. L., and Ross, D. B.: On the accuracy of HF radar surface current measurements: Intercomparisons with ship-based sensors, J. Geophys. Res.-Oceans, 102, 18737–18748, 1997.
Christensen, K. H., Sperrevik, A. K., and Röhrs, J.: The spring 2013 field experiment of the ENI/NOFO HF radar project, Tech. Rep. 25, Norwegian Meteorological Institute, Oslo, Norway, available at: http://met.no/Forskning/Publikasjoner/MET_report/2013/filestore/fieldEXP.pdf (last access: 5 March 2015), 2013.
Courtier, P., Thépaut, J.-N., and Hollingsworth, A.: A strategy for operational implementation of 4D-Var, using an incremental approach, Q. J. Roy. Meteorol. Soc., 120, 1367–1387, 1994.
Davis, R. E.: Drifter observations of coastal surface currents during CODE: The method and descriptive view, J. Geophys. Res., 90, 4741–4755, 1985.
Dimet, F.-X. L. E. and Talagrand, O.: Variational algorithms for analysis and assimilation of meteorological observations: theoretical aspects, Tellus A, 38, 97–110, 1986.
Evensen, G.: The Ensemble Kalman Filter: theoretical formulation and practical implementation, Ocean Dynam., 53, 343–367, 2003.
Flather, R. A.: A tidal model of the north-west European continental shelf, Memoires de la Society Royal des Sciences de Liege, 10, 141–164, 1976.
Fu, L.-L., Christensen, E. J., Yamarone, C. A., Lefebvre, M., Ménard, Y., Dorrer, M., and Escudier, P.: TOPEX/POSEIDON mission overview, J. Geophys. Res., 99, 24369–24381, 1994.
Gurgel, K.-W., Antonischki, G., Essen, H.-H., and Schlick, T.: Wellen Radar (WERA): a new ground-wave HF radar for ocean remote sensing, Coastal Eng., 37, 219–234, 1999.
Gustafsson, N.: Discussion on "4D-Var or EnKF?", Tellus A, 59, 774–777, 2007.
Isachsen, P. E.: Baroclinic instability and eddy tracer transport across sloping bottom topography: How well does a modified Eady model do in primitive equation simulations?, Ocean Model., 39, 183–199, 2011.
Isachsen, P. E., Koszalka, I., and LaCasce, J. H.: Observed and modeled surface eddy heat fluxes in the eastern Nordic Seas, J. Geophys. Res., 117, C08020, https://doi.org/10.1029/2012JC007935, 2012.
Kalnay, E., Li, H., Miyoshi, T., Yang, S.-C., and Bakkabrera-Poy, J.: 4-D-Var or ensemble Kalman filter?, Tellus A, 59, 758–773, 2007.
Kjelaas, A. G. and Whelan, C.: Rapidly deployable SeaSonde for modeling oil spill response, Sea Technol., 52, 10–13, 2011.
Kristiansen, J., Bjørge, D., Berge, H., Simonsen, M., Torheim, T., Aasen, I.-L., Rooney, G., and Edwards, J.: Improving the screen temperature forecasts of the Norwegian configuration of the UM: on model interoperability with respect to soil initial conditions, in: Unified Model User Workshop, Exeter, United Kingdom, 9–11 November, 2009.
Liu, Y. and Weisberg, R. H.: Evaluation of trajectory modeling in different dynamic regions using normalized cumulative Lagrangian separation, J. Geophys. Res., 116, C09013, https://doi.org/10.1029/2010JC006837, 2011.
Marchesiello, P., McWilliams, J. C., and Shchepetkin, A.: Open boundary conditions for long-term integration of regional oceanic models, Ocean Model., 3, 1–20, 2001.
Moe, H., Ommundsen, A., and Gjevik, B.: A high resolution tidal model for the area around the Lofoten Islands, northern Norway, Cont. Shelf Res., 22, 485–504, 2002.
Moore, A. M., Arango, H. G., Di Lorenzo, E., Miller, A. J., and Cornuelle, B. D.: An adjoint sensitivity analysis of the Southern California Current circulation and ecosystem, J. Phys. Oceanogr., 39, 702–720, 2009.
Moore, A. M., Arango, H. G., Broquet, G., Edwards, C., Veneziani, M., Powell, B., Foley, D., Doyle, J. D., Costa, D., and Robinson, P.: The Regional Ocean Modeling System (ROMS) 4-dimensional variational data assimilation systems: Part II – Performance and application to the California Current System, Prog. Oceanogr., 91, 50–73, 2011a.
Moore, A. M., Arango, H. G., Broquet, G., Edwards, C., Veneziani, M., Powell, B., Foley, D., Doyle, J. D., Costa, D., and Robinson, P.: The Regional Ocean Modeling System (ROMS) 4-dimensional variational data assimilation systems: Part III – Observation impact and observation sensitivity in the California Current System, Prog. Oceanogr., 91, 74–94, 2011b.
Moore, A. M., Arango, H. G., Broquet, G., Powell, B. S., Weaver, A. T., and Zavala-Garay, J.: The Regional Ocean Modeling System (ROMS) 4-dimensional variational data assimilation systems: Part I – System overview and formulation, Prog. Oceanogr., 91, 34–49, 2011c.
Oke, P. R., Allen, J. S., Miller, R. N., Egbert, G. D., and Kosro, P. M.: Assimilation of surface velocity data into a primitive equation coastal ocean model, J. Geophys. Res., 107, 5–1, 2002.
Oke, P. R., Brassington, G. B., Griffin, D. A., and Schiller, A.: Ocean data assimilation: a case for ensemble optimal interpolation, Australian Meteorological and Oceanographic Journal, 59, 67–76, 2010.
Paduan, J. D. and Shulman, I.: HF radar data assimilation in the Monterey Bay area, J. Geophys. Res., 109, CO7S09, https://doi.org/10.1029/2003JC001949, 2004.
Paduan, J. D. and Washburn, L.: High-frequency radar observations of ocean surface currents, Annual Review of Marine Science, 5, 115–136, 2013.
Paduan, J. D., Kim, K. C., Cook, M. S., and Chavez, F. P.: Calibration and validation of direction-finding high-frequency radar ocean surface current observations, IEEE J. Oceanic Eng., 31, 862–875, 2006.
Powell, B. S., Arango, H. G., Moore, A. M., Di Lorenzo, E., Milliff, R. F., and Foley, D.: 4DVAR data assimilation in the intra-Americas sea with the Regional Ocean Modeling System (ROMS), Ocean Model., 25, 173–188, 2008.
Rayner, N. A., Parker, D. E., Horton, E. B., Folland, C. K., Alexander, L. V., Rowell, D. P., Kent, E. C., and Kaplan, A.: Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century, J. Geophys. Res.-Atmos., 108, 4407, https://doi.org/10.1029/2002JD002670, 2003.
Roemmich, D., Johnson, G. C., Riser, S., Davis, R., Gilson, J., Owens, W. B., Garzoli, S. L., Schmid, C., and Ignaszewski, M.: The Argo Program: Observing the global ocean with profiling floats, Oceanography, 22, 34–43, 2009.
Röhrs, J., Christensen, K. H., Hole, L. R., Broström, G., Drivdal, M., and Sundby, S.: Observation-based evaluation of surface wave effects on currents and trajectory forecasts, Ocean Dynam., 62, 1519–1533, 2012.
Röhrs, J., Christensen, K. H., Vikebø, F., Sundby, S., Saetra, Ø., and Broström, G.: Wave-induced transport and vertical mixing of pelagic eggs and larvae, Limnol. Oceanogr., 59, 1213–1227, 2014.
Röhrs, J., Sperrevik, A. K., Christensen, K. H., Broström, G., and Breivik, Ø.: Comparison of HF radar measurements with Eulerian and Lagrangian surface currents, Ocean Dynam., in press, 2015.
Sakov, P., Counillon, F., Bertino, L., Lisæter, K. A., Oke, P. R., and Korablev, A.: TOPAZ4: an ocean-sea ice data assimilation system for the North Atlantic and Arctic, Ocean Sci., 8, 633–656, https://doi.org/10.5194/os-8-633-2012, 2012.
Wentz, F. J., Gentemann, C., Smith, D., and Chelton, D.: Satellite measurements of sea surface temperature through clouds, Science, 288, 847–850, 2000.
Zavala-Garay, J., Wilkin, J. L., and Arango, H. G.: Predictability of mesoscale variability in the east australian current given strong-constraint data assimilation, Jo. Phys. Oceanogr., 42, 1402–1420, 2012.
Zhang, W. G., Wilkin, J. L., Levin, J. C., and Arango, H. G.: An adjoint sensitivity study of buoyancy- and wind-driven circulation on the New Jersey inner shelf, J. Phys. Oceanogr., 39, 1652–1668, 2009.
Zhang, W. G., Wilkin, J. L., and Arango, H. G.: Towards an integrated observation and modeling system in the New York Bight using variational methods. Part I: 4DVAR data assimilation, Ocean Model., 35, 119–133, 2010.
