Spatially Tracking Wave Events in Partitioned Numerical Wave Model Outputs

Haoyu Jiang College of Marine Science and Technology, China University of Geosciences, Wuhan, and Laboratory for Regional Oceanography and Numerical Modeling, Qingdao National Laboratory for Marine Science and Technology, Qingdao, and Shenzhen Research Institute, China University of Geosciences, Shenzhen, China

Search for other papers by Haoyu Jiang in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

Numerical wave models can output partitioned wave parameters at each grid point using a spectral partitioning technique. Because these wave partitions are usually organized according to the magnitude of their wave energy without considering the coherence of wave parameters in space, it can be difficult to observe the spatial distributions of wave field features from these outputs. In this study, an approach for spatially tracking coherent wave events (which means a cluster of partitions originating from the same meteorological event) from partitioned numerical wave model outputs is presented to solve this problem. First, an efficient traverse algorithm applicable for different types of grids, termed breadth-first search, is employed to track wave events using the continuity of wave parameters. Second, to reduce the impact of the garden sprinkler effect on tracking, tracked wave events are merged if their boundary outlines and wave parameters on these boundaries are both in good agreement. Partitioned wave information from the Integrated Ocean Waves for Geophysical and other Applications dataset is used to test the performance of this spatial tracking approach. The test results indicate that this approach is able to capture the primary features of partitioned wave fields, demonstrating its potential for wave data analysis, model verification, and data assimilation.

© 2019 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Haoyu Jiang, Haoyujiang@cug.edu.cn

Abstract

Numerical wave models can output partitioned wave parameters at each grid point using a spectral partitioning technique. Because these wave partitions are usually organized according to the magnitude of their wave energy without considering the coherence of wave parameters in space, it can be difficult to observe the spatial distributions of wave field features from these outputs. In this study, an approach for spatially tracking coherent wave events (which means a cluster of partitions originating from the same meteorological event) from partitioned numerical wave model outputs is presented to solve this problem. First, an efficient traverse algorithm applicable for different types of grids, termed breadth-first search, is employed to track wave events using the continuity of wave parameters. Second, to reduce the impact of the garden sprinkler effect on tracking, tracked wave events are merged if their boundary outlines and wave parameters on these boundaries are both in good agreement. Partitioned wave information from the Integrated Ocean Waves for Geophysical and other Applications dataset is used to test the performance of this spatial tracking approach. The test results indicate that this approach is able to capture the primary features of partitioned wave fields, demonstrating its potential for wave data analysis, model verification, and data assimilation.

© 2019 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Haoyu Jiang, Haoyujiang@cug.edu.cn
Save
  • Aarnes, J. E., and H. E. Krogstad, 2001: Partitioning sequences for the dissection of directional ocean wave spectra: A review. SINTEF Applied Mathematics Tech. Rep., 23 pp.

    • Search Google Scholar
    • Export Citation
  • Ailliot, P., C. Maisondieu, and V. Monbet, 2013: Dynamical partitioning of directional ocean wave spectra. Probab. Eng. Mech., 33, 95102, https://doi.org/10.1016/j.probengmech.2013.03.002.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ardhuin, F., and Coauthors, 2010: Semi-empirical dissipation source functions for wind-wave models: Part I: Definition, calibration and validation. J. Phys. Oceanogr., 40, 19171941, https://doi.org/10.1175/2010JPO4324.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Collard, F., F. Ardhuin, and B. Chapron, 2009: Monitoring and analysis of ocean swell fields from space: New methods for routine observations. J. Geophys. Res., 114, C07023, https://doi.org/10.1029/2008JC005215.

    • Search Google Scholar
    • Export Citation
  • Delpey, M. T., F. Ardhuin, F. Collard, and B. Chapron, 2010: Space-time structure of long ocean swell fields. J. Geophys. Res., 115, C12037, https://doi.org/10.1029/2009JC005885.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Devaliere, E.-M., J. L. Hanson, and R. Luettich, 2009: Spatial tracking of numerical wave model output using a spiral tracking search algorithm. World Congress on Computer Science and Information Engineering, Los Angeles, CA, Association for Computing Machinery, 404–408.

    • Crossref
    • Export Citation
  • ECMWF, 2018: Wave model output parameters. ECMWF wave model, ECMWF Rep., 6999, https://www.ecmwf.int/en/elibrary/18717-part-vii-ecmwf-wave-model.

    • Search Google Scholar
    • Export Citation
  • Gerling, T. W., 1992: Partitioning sequences and arrays of directional ocean wave spectra into component wave systems. J. Atmos. Oceanic Technol., 9, 444458, https://doi.org/10.1175/1520-0426(1992)009<0444:PSAAOD>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hanson, J. L., and O. M. Phillips, 2001: Automated analysis of ocean surface directional wave spectra. J. Atmos. Oceanic Technol., 18, 277293, https://doi.org/10.1175/1520-0426(2001)018<0277:AAOOSD>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Husson, R., 2012: Development and validation of a global observation-based swell model using wave mode operating synthetic aperture radar. Ph.D. thesis, Dept. of Earth Science, University of Bretagne Occidentale, 275 pp., http://tinyurl.com/kzm434f.

  • Jiang, H., A. Babanin, and G. Chen, 2016: Event-based validation of swell arrival time. J. Phys. Oceanogr., 46, 35633569, https://doi.org/10.1175/JPO-D-16-0208.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jiang, H., A. Mouche, H. Wang, A. Babanin, B. Chapron, and G. Chen, 2017: Limitation of SAR quasi-linear inversion data on swell climate: An example of global crossing swells. Remote Sens., 9, 107, https://doi.org/10.3390/rs9020107.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kpogo-Nuwoklo, K. A., M. Olagnon, and Z. Guédé, 2014: Wave spectra partitioning and identification of wind sea and swell events. 33rd Int. Conf. on Offshore Mechanics and Arctic Engineering, San Francisco, CA, American Society of Mechanical Engineers, OMAE2014-24689, https://doi.org/10.1115/OMAE2014-24689.

    • Crossref
    • Export Citation
  • Portilla, J., F. J. Ocampo-Torres, and J. Monbaliu, 2009: Spectral partitioning and identification of wind sea and swell. J. Atmos. Oceanic Technol., 26, 107122, https://doi.org/10.1175/2008JTECHO609.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Portilla, J., L. Cavaleri, and G. P. V. Vledder, 2015: Wave spectra partitioning and long term statistical distribution. Ocean Modell., 96, 148160, https://doi.org/10.1016/j.ocemod.2015.06.008.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rascle, N., and F. Ardhuin, 2013: A global wave parameter database for geophysical applications. Part 2: Model validation with improved source term parameterization. Ocean Modell., 70, 174188, https://doi.org/10.1016/j.ocemod.2012.12.001.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tolman, H. L., 2002: Alleviating the garden sprinkler effect in wind wave models. Ocean Modell., 4, 269289, https://doi.org/10.1016/S1463-5003(02)00004-5.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tracy, B., E.-M. Devaliere, T. Nicolini, H. L. Tolman, and J. L. Hanson, 2007: Wind sea and swell delineation for numerical wave modeling. Proc. 10th Int. Workshop on Wave Hindcasting and Forecasting, Kahuku, HI, U.S. Army Engineer Research and Development Center, P12.

  • Van der Westhuysen, A., 2013: Spatial and temporal tracking of ocean wave systems. First Waves Winter School, College Park, MD, NCEP, https://polar.ncep.noaa.gov/waves/workshop/pdfs/wwws_2013_wave_tracking.pdf.

    • Search Google Scholar
    • Export Citation
  • Voorrips, A. C., V. K. Makin, and S. Hasselmann, 1997: Assimilation of wave spectra from pitch-and-roll buoys in a North Sea wave model. J. Geophys. Res., 102, 58295849, https://doi.org/10.1029/96JC03242.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • WAVEWATCH III Development Group, 2016: User manual and system documentation of WAVEWATCH III version 5.16. NCEP Environmental Modeling Center Tech. Note, 361 pp., http://polar.ncep.noaa.gov/waves/wavewatch/manual.v5.16.pdf.

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 453 209 19
PDF Downloads 523 110 22