Abstract
This study assesses the performance of different source term packages of WAVEWATCH III (WWIII, V-6.07) wave model for various wave conditions in the Indian Ocean (IO). Eight simulations of WWIII were made for the year 2017, four using default source term packages (ST2, ST3, ST4, and ST6) and another four by tuning the wind–wave interaction parameter (β) in the ST4 and ST6 schemes. The simulated wave outputs are compared with in-situ and altimeter wave fields over a wide range of weather conditions. All wave simulations have significant errors in low wind speeds (e.g., in pre-monsoon season) compared to medium (e.g., post-monsoon) and strong (e.g., monsoon season) winds which is independent of the error in the forecast wind. Overall, the ST4 scheme reproduces well the wave characteristics in all seasons and different conditions of IO, while the ST6 scheme is best suited for cyclonic weather conditions for wave simulation. Changing the WWIII parameterization schemes based on wind conditions is not a practical option for a timely wave prediction for a basin like the IO, where varied wave patterns exist year-round, owing to the full spectrum of wind conditions. Instead, this study advocates selecting a scheme that works well in all conditions, like ST4, and tuning it to suit well in cyclonic conditions.
Similar content being viewed by others
References
Ardhuin F, Chapron B and Collard F 2009 Observation of swell dissipation across oceans; Geophys. Res. Lett. 36 L06607, https://doi.org/10.1029/2008GL037030.
Ardhuin F, Rogers E and Babanin A V et al. 2010 Semi-empirical dissipation source functions for ocean waves. Part I: Definition, calibration, and validation; J. Phys. Oceanogr. 40 1917–1941, https://doi.org/10.1175/2010JPO4324.1.
Babanin A 2011 Breaking and dissipation of ocean surface waves; Cambridge University Press, Cambridge, 463p., https://doi.org/10.1017/CBO9780511736162.
Babanin A V, Banner M L, Young I R and Donelan M A 2007 Wave-follower field measurements of the wind-input spectral function. Part III: Parameterization of the wind-input enhancement due to wave breaking; J. Phys. Oceanogr. 37 2764–2775, https://doi.org/10.1175/2007JPO3757.1.
Babanin A V, Rogers W E and de Camargo R et al. 2019 Waves and swells in high wind and extreme fetches, measurements in the Southern Ocean; Front. Mar. Sci. 6, https://doi.org/10.3389/fmars.2019.00361.
Banner M L and Morison R P 2010 Refined source terms in wind wave models with explicit wave breaking prediction. Part I: Model framework and validation against field data; Ocean Model. 33 177–189, https://doi.org/10.1016/J.OCEMOD.2010.01.002.
Berens P 2009 CircStat: A MATLAB Toolbox for circular statistics; J. Stat. Softw. 31, https://doi.org/10.18637/jss.v031.i10.
Bi F, Song J, Wu K and Xu Y 2015 Evaluation of the simulation capability of the WaveWatch III model for Pacific Ocean wave; Acta Oceanologica Sinica 34 43–57, https://doi.org/10.1007/s13131-015-0737-1.
Bidlot J-R, Janssen P and Abdalla S 2007 509 A revised formulation of ocean wave dissipation and its model impact; ECMWF Technical Memoranda Series.
Booij N, Ris R C and Holthuijsen L H 1999 A third-generation wave model for coastal regions 1. Model description and validation; J. Geophys. Res. Ocean. 104 7649–7666, https://doi.org/10.1029/98JC02622.
Brenner S, Gertman I and Murashkovsky A 2007 Preoperational ocean forecasting in the southeastern Mediterranean Sea: Implementation and evaluation of the models and selection of the atmospheric forcing; J. Mar. Syst. 65 268–287, https://doi.org/10.1016/J.JMARSYS.2005.11.018.
Callaghan D P, Nielsen P, Short A and Ranasinghe R 2008 Statistical simulation of wave climate and extreme beach erosion; Coast. Eng. 55 375–390, https://doi.org/10.1016/J.COASTALENG.2007.12.003.
Cavaleri L, Alves J H G M and Ardhuin F et al. 2007 Wave modelling – The state-of-the- art; Prog. Oceanogr. 75 603–674, https://doi.org/10.1016/J.POCEAN.2007.05.005.
Chalikov D and Belevich M Yu 1993 One-dimensional theory of the wave boundary layer; Bound.-Layer Meteorol. 63 65–96, https://doi.org/10.1007/BF00705377.
Chawla A, Tolman H L, Modeling M and Branch A 2007 Automated grid generation for WAVEWATCH III.
Chen C 2018 Case study on wave-current interaction and its effects on ship navigation; J. Hydrodyn. 30 411–419, https://doi.org/10.1007/s42241-018-0050-5.
Donelan M A, Babanin A V and Young I R et al. 2005 Wave-follower field measurements of the wind-input spectral function. Part I: Measurements and calibrations; J. Atmos. Ocean. Technol. 22 799–813, https://doi.org/10.1175/JTECH1725.1.
Donelan M, Babanin A V and Young I R et al. 2006 Wave-follower field measurements of the wind-input spectral function. Part II: Parameterization of the wind input; J. Phys. Oceanogr. 36 1672–1689, https://doi.org/10.1175/JPO2933.1.
Erick Rogers W, Babanin A V and Wang D W 2012 Observation-consistent input and white capping dissipation in a model for wind-generated surface waves: Description and simple calculations; J. Atmos. Ocean. Technol. 29 1329–1346, https://doi.org/10.1175/JTECH-D-11-00092.1.
Faltinsen O M and Shen Y 2018 Wave and current effects on floating fish farms: Keynote Contribution for the International Workshop on Wave Loads and Motions of Ships and Offshore Structures, Harbin, China, 5–7 November, 2017; J. Mar. Sci. Appl. 17 284–296, https://doi.org/10.1007/s11804-018-0033-5.
Hasselmann K, Bauer E and Janssen P A E M 1988 The WAM model – a third generation ocean wave prediction model; J. Phys. Oceanogr. 18 1775–1810.
Hwang P A 2011 A note on the ocean surface roughness spectrum, J. Atmos. Ocean. Technol. 28(3) 436–443.
Janssen P A E M 1991 Quasi-linear theory of wind-wave generation applied to wave forecasting; J. Phys. Oceanogr. 21 1631–1642, https://doi.org/10.1175/1520-0485(1991)021%3c1631:QLTOWW%3e2.0.CO;2.
Janssen P 2004 The interaction of ocean waves and wind; Cambridge University Press.
Kalourazi M Y, Siadatmousavi S M, Yeganeh-Bakhtiary A and Jose F 2021 WAVEWATCH-III source terms evaluation for optimizing hurricane wave modeling: A case study of Hurricane Ivan; Oceanologia 63 194–213, https://doi.org/10.1016/j.oceano.2020.12.001.
Lin S, Sheng J and Xing J 2020 Performance evaluation of parameterizations for wind input and wave dissipation in the spectral wave model for the northwest Atlantic Ocean; Atmos.-Ocean 58 258–286, https://doi.org/10.1080/07055900.2020.1790336.
Liu Q, Babanin A and Fan Y et al. 2017 Numerical simulations of ocean surface waves under hurricane conditions: Assessment of existing model performance; Ocean Model. 118 73–93, https://doi.org/10.1016/J.OCEMOD.2017.08.005.
Miles J W 1957 On the generation of surface waves by shear flows; J. Fluid Mech. 3 185, https://doi.org/10.1017/S0022112057000567.
Moon I J, Hara T and Ginis I et al. 2004 Effect of surface waves on air-sea momentum exchange: Part I: Effect of mature and growing seas; J. Atmos. Sci. 61 2321–2333, https://doi.org/10.1175/1520-0469(2004)061%3c2321:EOSWOA%3e2.0.CO;2.
Perrie W, Toulany B and Roland A et al. 2018 Modeling North Atlantic Nor’easters with modern wave forecast models; J. Geophys. Res. Oceans 123 533–557, https://doi.org/10.1002/2017JC012868.
Persson A 2013 User guide to ECMWF forecast products; In: Visualization and new forecasting tools, ECMWF Meteorol. Bull. M3 2.
Phillips O M 1984 On the response of short ocean wave components at a fixed wavenumber to ocean current variations; J. Phys. Oceanogr. 14 1425–1433, https://doi.org/10.1175/1520-0485(1984)014%3c1425:OTROSO%3e2.0.CO;2.
Powell M D, Vickery P J and Reinhold T A 2003 Reduced drag coefficient for high wind speeds in tropical cyclones; Nature 422 279–283, https://doi.org/10.1038/nature01481.
Rascle N and Ardhuin F 2013 A global wave parameter database for geophysical applications. Part 2: Model validation with improved source term parameterization; Ocean Model. 70 174–188, https://doi.org/10.1016/j.ocemod.2012.12.001.
Remya P G, Kumar R, Basu S and Sarkar A 2012 Wave hindcast experiments in the Indian Ocean using MIKE 21 SW model; J. Earth Syst. Sci. 121 385–392, https://doi.org/10.1007/s12040-012-0169-7.
Remya P G, Vishnu S and Praveen Kumar B et al. 2016 Teleconnection between the North Indian Ocean high swell events and meteorological conditions over the Southern Indian Ocean; J. Geophys. Res. Oceans 121 7476–7494, https://doi.org/10.1002/2016JC011723.
Remya P G, Kumar B P, Srinivas G and Nair T M B 2020 Impact of tropical and extra tropical climate variability on Indian Ocean surface waves; Clim. Dyn. 54 4919–4933, https://doi.org/10.1007/s00382-020-05262-x.
Seemanth M, Bhowmick S A, Kumar R and Sharma R 2016 Sensitivity analysis of dissipation parameterizations in a third-generation spectral wave model, WAVEWATCH III for Indian Ocean; Ocean Eng. 124 252–273, https://doi.org/10.1016/j.oceaneng.2016.07.023.
Stopa J E, Ardhuin F, Babanin A and Zieger S 2016 Comparison and validation of physical wave parameterizations in spectral wave models; Ocean Model. 103 2–17, https://doi.org/10.1016/J.OCEMOD.2015.09.003.
Tolman H 2002 Validation of WAVEWATCH III version 1.15 for a global domain NOAA/NWS/NCEP/OMB Technical Note Nr. 213, 33p.
Tolman H L and Chalikov D 1996 Source terms in a third-generation wind wave model; J. Phys. Oceanogr. 26 2497–2518, https://doi.org/10.1175/1520-0485(1996)026%3c2497:STIATG%3e2.0.CO;2.
Uihlein A and Magagna D 2016 Wave and tidal current energy – A review of the current state of research beyond technology; Renew. Sustain. Energy Rev. 58 1070–1081, https://doi.org/10.1016/J.RSER.2015.12.284.
Villatoro M, Silva R and Méndez F J et al. 2014 An approach to assess flooding and erosion risk for open beaches in a changing climate; Coast. Eng. 87 50–76, https://doi.org/10.1016/j.coastaleng.2013.11.009.
Wang J, Zhang J and Yang J et al. 2017 An evaluation of input/dissipation terms in WAVEWATCH III using in-situ and satellite significant wave height data in the South China Sea; Acta Oceanologica Sinica 36 20–25, https://doi.org/10.1007/s13131-017-1038-7.
Zieger S, Babanin A V, Erick Rogers W and Young I R 2015 Observation-based source terms in the third-generation wave model WAVEWATCH; Ocean Model. 96 2–25, https://doi.org/10.1016/J.OCEMOD.2015.07.014.
Acknowledgements
Director, INCOIS, is acknowledged for facilitating this research work. This research falls under OCCAS-Deep Ocean Mission, MoES, Govt. of India. We thank the developers of WAVEWATCH III NOAA/NCEP for providing the WAVEWATCH III source code (https://polar.ncep.noaa.gov/waves/) and for their consistent efforts to improve the accuracy of this open-source spectral model. The in-situ buoy observations used for this study are obtained from http://odis.incois.gov.in. We thank Dr Jossia Joseph, NIOT, for providing the wave spectrum data for the work. Jason-2 altimeter data was obtained from ftp://ftp.nodc.noaa.gov/pub/data.nodc/jason2/. This article is part of Abhijith Raj’s doctoral research and acknowledges the financial support through the Ministry of Earth Sciences (MoES, Govt. of India) ‘DEvelopment of SKilled Manpower in Earth Sciences (DESK)’ Research Fellow Program. This is INCOIS contribution no. 475.
Author information
Authors and Affiliations
Contributions
AR, RPG, and BPK conceived the hypothesis, conceptualized the study, and designed the experiments. AR, RPG and MS carried out the model experiments. AR, RPG, and BPK wrote the first version of the article with critical inputs from MS and TMB, which was subsequently edited and modified by all the authors. All authors read the revised manuscript, edited and approved the final version.
Corresponding author
Additional information
Communicated by C Gnanaseelan
Appendix
Appendix
1.1 A.1 Notes on WAVEWATCH III source term parameterizations of the selected packages
Among all the packages included in WWIII, the following four switches are considered in this study.
1.1.1 A.1.1 ST2
This package is based on the (Tolman and Chalikov 1996) parameterization, as updated by (Tolman 2002). This scheme combines a wind input adjusted to the numerical model of air-flow above waves by Chalikov and Belevich (1993) and a dissipation consisting of two separate terms, one for low-frequency waves and the other for the high-frequency tail of the spectrum. The model is tuned and tested using standard fetch-limited growth conditions.
The input source term is given as:
where β is a non-dimensional wind–wave interaction parameter whose value depends on non-dimensional frequency and drag coefficient at a height equal to the ‘apparent’ wavelength.
Energy flux from waves to the wind is not present in previous parametrizations of Sin (i.e., negative input source terms are introduced in this scheme).
1.1.2 A.1.2 ST3
ST3 is a WAM cycle 4 (ECWAM) scheme. This parameterization scheme combines the wind input term initially based on the wave growth theory of Miles (1957) with the feedback on the wind profile parameterized by Janssen (1991). A linear swell dissipation component due to stress variations in phase with orbital velocity was introduced by Janssen (2004). The input source term is expressed as follows:
where \({\rho }_{a}\) and \({\rho }_{w}\) are the air and water densities. \({\beta }_{\mathrm{max}}\) is a non-dimensional growth parameter (constant). \({u}_{*}\) is the wind friction velocity and \(K\) is the von Karman constant. \(Z\) is a function of roughness length given by Janssen (1991) and corrected for intermediate depths. \({z}_{\alpha }\) is a wave age parameter. \({P}_{in}\) is a constant that controls the directional distribution and \({S}_{\mathrm{out}}\left(k,\theta \right)\) is a linear decrease in swell included following Janssen (2004).
\({z}_{\alpha }\) is not well described in ST3 documentation; it has an important effect on wave growth. Essentially it shifts the wave age of the long waves, which typically increases the growth, and even generates waves that travel faster than the wind. This accounts for some gustiness in the wind and should possibly be resolution-dependent.
1.1.3 A.1.3 ST4
Parameterization with switch ST4 is built around a saturation-based dissipation, provided by Ardhuin et al. (2010), closely following Banner and Morison (2010), a cumulative effect that dissipates short waves due to the breaking of long waves and a swell dissipation that transitions from non-linear in turbulent conditions, to linear in the viscous regime (Ardhuin et al. 2009). The main advance of this scheme is an adjustment of the dissipation function without any predefined spectral shape. The wind input is loosely adapted from the (Janssen 1991) formulation, with an important reduction of input at high frequencies necessary to achieve a balance with the white capping term. This modification reduced the unrealistic large drag coefficients under high winds, but it removed the wave age dependence on the wind stress, which is not realistic (Rascle and Ardhuin 2013). The wind input term gives the flux of energy from atmospheric non-wave motion to wave motion, is the sum of \({S}_{\mathrm{in}}\) (wave generation) and \({S}_{\mathrm{out}}\) (wind generation term or −ve wind input term). The full wind input source term is as follows:
where \({\rho }_{a}\) and \({\rho }_{w}\) are the air and water densities. \({\beta }_{\mathrm{max}}\) is a non-dimensional growth parameter (constant). \({u}_{*}\) is the wind friction velocity and \(K\) is von Karman constant. \(Z\) is defined as effective wave age. The power of cosine is taken constant, P = 2.
For younger seas, the wind input is weaker than that given by Janssen (1991) (ST3), but stronger than that given by Tolman and Chalikov (1996) (ST2). However, the dissipation at the peak is generally stronger because it is essentially based on a local steepness and these dominant waves are the steepest in the sea state. As a result, the short fetch growth is relatively weaker than that with the source term combination proposed by Bidlot et al. (2007).
1.1.4 A.1.4 ST6
ST6 is an input-dissipation source term based on measurements from laboratory experiments carried out during AUSWEX at Lake Georgia, Australia. The wind input used is non-linear that relaxes under conditions of steep waves and strong winds to represent detachment of air-flow. The wind input function represents the energy flux transferred from the wind to waves. This term is due to form drag. AUSWEX data analysis and the wind input parameterization reported by Donelan et al. (2006, 2005) and (Babanin et al. 2007) show dependencies that have not been reported in previous experiments. Donelan et al. (2006) described the effect of full air-flow separation in which the wind detaches from the flow, skipping the wave troughs before it re-attaches on the windward side of the wave crest. The effect was parameterized by Donelan et al. (2006) and included in the new wind input term based on the observations. The input term is proposed as:
and
where \({\rho }_{a}\) and \({\rho }_{w}\) are densities of air and water, \(U\) is wind speed, \(c\) is phase speed of wave, \(\sigma\) is radian frequency and \(k\) is the wavenumber. The parameterization of wave steepness \(ak\) is replaced by the spectral saturation \(\sqrt{{B}_{n}}\) following Phillips (1984). The omni-directional action density is obtained by integration over all directions \(N(k) =\int N(k,\theta )d\theta\).
Drag coefficient parameterization proposed by Hwang (2011) is used, which accounts for saturation and decrease in magnitude for extreme winds.
Source term yields faster wave growth for young seas than ST2 and ST4 source terms. As the wave field develops, the value of the wind input term near the spectral peak reduces. Compared to previous schemes, input and dissipation are stronger at high frequencies.
Rights and permissions
About this article
Cite this article
Raj, A., Kumar, B.P., Remya, P.G. et al. Assessment of the forecasting potential of WAVEWATCH III model under different Indian Ocean wave conditions. J Earth Syst Sci 132, 32 (2023). https://doi.org/10.1007/s12040-023-02045-w
Received:
Revised:
Accepted:
Published:
DOI: https://doi.org/10.1007/s12040-023-02045-w