Ocean Wind Observation Based on the Mean Square Slope Using a Self-Developed Miniature Wave Buoy
Abstract
:1. Introduction
2. Instrumentation and Algorithm
2.1. Architecture of the Self-Developed Miniature Wave Buoy
2.2. Working Principle of the MEMS Gyroscope
2.3. Algorithms for Data Processing
3. Laboratory Calibration
3.1. Time Domain: Accuracy of Water Surface Slope Measurement
3.2. Frequency Domain: Frequency Response Curve of the Miniature Wave Buoy
4. Field Observations
4.1. Regression Analysis between U10 and In Situ LPMSS
4.2. Effect of Wave Age on in situ LPMSS
4.3. Detection of Mean Wind Direction Based on the 2D Distribution of Slope Components
5. Discussion: Spectral Tail Slope Analysis
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Castro-Santos, L.; Diaz-Casas, V. (Eds.) Floating Offshore Wind Farms; Springer: Berlin, Germany, 2016. [Google Scholar]
- Hamilton, G.D. NOM Data Buoy Office Programs. Bull. Am. Meteorol. Soc. 1980, 61, 1012–1017. [Google Scholar] [CrossRef]
- Kao, C.C.; Jao, K.C.; Doong, D.J.; Chen, H.L.; Kuo, C.L. Buoy and radar observation network around Taiwan. In Proceedings of the OCEANS 2006-Asia Pacific, Singapore, 16–19 May 2006; pp. 1–7. [Google Scholar]
- Jeong, S.M.; Son, B.H.; Lee, C.Y. Estimation of the motion performance of a light buoy adopting ecofriendly and lightweight materials in waves. J. Mar. Sci. Eng. 2020, 8, 139. [Google Scholar] [CrossRef]
- Atlas, R.; Hoffman, R.N.; Ardizzone, J.; Leidner, S.M.; Jusem, J.C.; Smith, D.K.; Gombos, D. A cross-calibrated, multiplatform ocean surface wind velocity product for meteorological and oceanographic applications. Bull. Am. Meteorol. Soc. 2011, 92, 157–174. [Google Scholar] [CrossRef]
- Ricciardulli, L.; Wentz, F. Challenges of integrating multiple scatterometer observations into a climate data record: The effect of diurnal variability. In Proceedings of the International Ocean Vector Wind Science Team, Kona, HI, USA, 6–8 May 2013. [Google Scholar]
- Ashton, I.G.C.; Saulnier, J.B.; Smith, G.H. Spatial variability of ocean waves, from in-situ measurements. Ocean. Eng. 2013, 57, 83–98. [Google Scholar] [CrossRef]
- Raghukumar, K.; Chang, G.; Spada, F.; Jones, C.; Janssen, T.; Gans, A. Performance characteristics of “Spotter”, a newly developed real-time wave measurement buoy. J. Atmos. Ocean. Technol. 2019, 36, 1127–1141. [Google Scholar] [CrossRef]
- Smit, P.B.; Houghton, I.A.; Jordanova, K.; Portwood, T.; Shapiro, E.; Clark, D.; Sosa, M.; Janssen, T.T. Assimilation of distributed ocean wave sensors. Ocean. Model. 2020, 159, 101738. [Google Scholar] [CrossRef]
- Voermans, J.J.; Smit, P.B.; Janssen, T.T.; Babanin, A.V. Estimating wind speed and direction using wave spectra. J. Geophys. Res. Ocean. 2020, 125, e2019JC015717. [Google Scholar] [CrossRef]
- Yurovsky, Y.Y.; Dulov, V.A. MEMS-based wave buoy: Towards short wind-wave sensing. Ocean. Eng. 2020, 217, 108043. [Google Scholar] [CrossRef]
- Wei, X.; Liu, N.; Dong, T.; Qi, Z. A preliminary assessment of an innovative air-launched wave measurement buoy. Appl. Ocean. Res. 2021, 106, 102458. [Google Scholar] [CrossRef]
- Donelan, M.A.; Pierson, W.J., Jr. Radar scattering and equilibrium ranges in wind-generated waves with application to scatterometry. J. Geophys. Res. Ocean. 1987, 92, 4971–5029. [Google Scholar] [CrossRef]
- Clarizia, M.P.; Ruf, C.S. Wind speed retrieval algorithm for the Cyclone Global Navigation Satellite System (CYGNSS) mission. IEEE Trans. Geosci. Remote Sens. 2016, 54, 4419–4432. [Google Scholar] [CrossRef]
- Ruf, C.S.; Atlas, R.; Chang, P.S.; Clarizia, M.P.; Garrison, J.L.; Gleason, S.; Katzberg, S.J.; Jelenak, Z.; Johnson, J.T.; Majumdar, S.J.; et al. New ocean winds satellite mission to probe hurricanes and tropical convection. Bull. Am. Meteorol. Soc. 2016, 97, 385–395. [Google Scholar] [CrossRef]
- Gleason, S.; Zavorotny, V.U.; Akos, D.M.; Hrbek, S.; PopStefanija, I.; Walsh, E.J.; Masters, D.; Grant, M.S. Study of surface wind and mean square slope correlation in hurricane Ike with multiple sensors. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2018, 11, 1975–1988. [Google Scholar] [CrossRef]
- Li, S.; Zhao, D.; Zhou, L.; Liu, B. Dependence of mean square slope on wave state and its application in altimeter wind speed retrieval. Int. J. Remote Sens. 2013, 34, 264–275. [Google Scholar] [CrossRef]
- Zhong, Y.Z.; Cheng, H.Y.; Chien, H.; Koppe, B. Miniature Wave Buoy–Laboratory and Field Tests for Development of a Robust Low-Cost Measuring Technique. In Proceedings of the Coastal Structures 2019, Hannover, Germany, 29 September–2 October 2019; pp. 453–462. [Google Scholar]
- Phillips, O.M. (Ed.) The Dynamics of the Upper Ocean, 2nd ed.; Cambridge University Press: Cambridge, UK, 1977; p. 336. [Google Scholar]
- Cox, C.; Munk, W. Statistics of the sea surface derived from sun glitter. J. Mar. Res. 1954, 13, 198–227. [Google Scholar]
- Jackson, F.C.; Walton, W.T.; Hines, D.E.; Walter, B.A.; Peng, C.Y. Sea surface mean square slope from K u-band backscatter data. J. Geophys. Res. Ocean. 1992, 97, 11411–11427. [Google Scholar] [CrossRef]
- Hwang, P.A. Variable Spectral Slope and Nonequilibrium Surface Wave Spectrum. arXiv 2019, arXiv:1906.07998. [Google Scholar]
- Yan, Q.; Fan, C.; Zhang, J.; Meng, J. Understanding Ku-Band Ocean Radar Backscatter at Low Incidence Angles under Weak to Severe Wind Conditions by Comparison of Measurements and Models. Remote Sens. 2020, 12, 3445. [Google Scholar] [CrossRef]
- Garrison, J.L.; Katzberg, S.J.; Hill, M.I. Effect of sea roughness on bistatically scattered range coded signals from the Global Positioning System. Geophys. Res. Lett. 1998, 25, 2257–2260. [Google Scholar] [CrossRef]
- Rodriguez-Alvarez, N.; Akos, D.M.; Zavorotny, V.U.; Smith, J.A.; Camps, A.; Fairall, C.W. Airborne GNSS-R wind retrievals using delay–Doppler maps. IEEE Trans. Geosci. Remote Sens. 2012, 51, 626–641. [Google Scholar] [CrossRef]
- Gleason, S. Space based GNSS scatterometry: Ocean wind sensing using empirically calibrated model. IEEE Trans. Geosci. Remote Sens. 2013, 51, 4853–4863. [Google Scholar] [CrossRef]
- Katzberg, S.J.; Dunion, J.; Ganoe, G.G. The use of reflected GPS signals to retrieve ocean surface wind speeds in tropical cyclones. Radio Sci. 2013, 48, 371–387. [Google Scholar] [CrossRef]
- Wang, F.; Yang, D.; Zhang, B.; Li, W.; Darrozes, J. Wind speed retrieval using coastal ocean-scattered GNSS signals. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2016, 9, 5272–5283. [Google Scholar] [CrossRef]
- Newman, J.N.; Landweber, L. Marine Hydrodynamics; MIT Press: Cambridge, MA, USA, 1977. [Google Scholar]
- Yan, Q.; Zhang, J.; Fan, C.; Wang, J.; Meng, J. Study of sea-surface slope distribution and its effect on radar backscatter based on Global Precipitation Measurement Ku-band precipitation radar measurements. J. Appl. Remote Sens. 2018, 12, 016006. [Google Scholar] [CrossRef]
- Cox, C.; Munk, W. Slopes of the sea surface deduced from photographs of sun glitter. Bull. Scripps Inst. Oceanogr. 1956, 6, 401–487. [Google Scholar]
- Hanson, J.L.; Phillips, O.M. Automated analysis of ocean surface directional wave spectra. J. Atmos. Ocean. Technol. 2001, 18, 277–293. [Google Scholar] [CrossRef]
- Glazman, R.E.; Pilorz, S.H. Effects of sea maturity on satellite altimeter measurements. J. Geophys. Res. Ocean. 1990, 95, 2857–2870. [Google Scholar] [CrossRef]
- Sousa, M.C.; Alvarez, I.; Vaz, N.; Gomez-Gesteira, M.; Dias, J.M. Assessment of wind pattern accuracy from the QuikSCAT satellite and the WRF model along the Galician coast (Northwest Iberian Peninsula). Mon. Weather. Rev. 2013, 141, 742–753. [Google Scholar] [CrossRef]
- Wang, H.; Zhu, J.; Lin, M.; Zhang, Y.; Chang, Y. Evaluating Chinese HY-2B HSCAT ocean wind products using buoys and other scatterometers. IEEE Geosci. Remote Sens. Lett. 2019, 17, 923–927. [Google Scholar] [CrossRef]
- Remmers, T.; Cawkwell, F.; Desmond, C.; Murphy, J.; Politi, E. The potential of advanced scatterometer (ASCAT) 12.5 km coastal observations for offshore wind farm site selection in Irish waters. Energies 2019, 12, 206. [Google Scholar] [CrossRef]
- Cox, C.; Munk, W. Measurement of the roughness of the sea surface from photographs of the sun’s glitter. J. Opt. Soc. Am. 1954, 44, 838–850. [Google Scholar] [CrossRef]
- Fung, A.K.; Chen, K.S. Kirchhoff model for a skewed random surface. J. Electromagn. Waves Appl. 1991, 5, 205–216. [Google Scholar] [CrossRef]
- Ulaby, F.T.; Long, D.G.; Blackwell, W.; Elachi, C.; Fung, A.; Ruf, C.; Sarabandi, K.; Zebker, H.H.; van Zyl, J.J. Microwave Radar and Radiometric Remote Sensing; University of Michigan Press: Ann Arbor, MI, USA, 2014. [Google Scholar]
- Phillips, O.M. The equilibrium range in the spectrum of wind generated waves. J. Fluid Mech. 1958, 4, 785–790. [Google Scholar] [CrossRef]
- Phillips, O.M. Spectral and statistical properties of the equilibrium range in wind-generated gravity waves. J. Fluid Mech. 1985, 156, 505–531. [Google Scholar] [CrossRef]
- Lenain, L.; Melville, W.K. Measurements of the directional spectrum across the equilibrium saturation ranges of wind-generated surface waves. J. Phys. Oceanogr. 2017, 47, 2123–2138. [Google Scholar] [CrossRef]
- Takagaki, N.; Takane, K.; Kumamaru, H.; Suzuki, N.; Komori, S. Laboratory measurements of an equilibrium-range constant for wind waves at extremely high wind speeds. Dyn. Atmos. Ocean. 2018, 84, 22–32. [Google Scholar] [CrossRef]
- Young, I.R. Directional spectra of hurricane wind waves. J. Geophys. Res. Ocean. 2006, 111, 1–14. [Google Scholar] [CrossRef]
- Hwang, P.A.; Fan, Y.; Ocampo-Torres, F.J.; García-Nava, H. Ocean surface wave spectra inside tropical cyclones. J. Phys. Oceanogr. 2017, 47, 2293–2417. [Google Scholar] [CrossRef]
- Donelan, M.A.; Hamilton, J.; Hui, W.H. Directional spectra of wind-generated waves. Philos. Trans. R. Soc. 1985, 315, 509–562. [Google Scholar]
No. | Item | Detail |
---|---|---|
1 | Attitude parameters | pitch, roll, heading |
2 | Acceleration parameters | x-, y-, z-acceleration, right-hand coordinate system |
3 | Range of angular velocity | +/−1833°/s |
4 | Resolution of angular velocity | 0.05°/s |
5 | Range of linear acceleration | +/−4 g |
6 | Resolution of linear acceleration | 6 mg |
7 | Sampling rate | 125 Hz |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Zhong, Y.-Z.; Chien, H.; Chang, H.-M.; Cheng, H.-Y. Ocean Wind Observation Based on the Mean Square Slope Using a Self-Developed Miniature Wave Buoy. Sensors 2022, 22, 7210. https://doi.org/10.3390/s22197210
Zhong Y-Z, Chien H, Chang H-M, Cheng H-Y. Ocean Wind Observation Based on the Mean Square Slope Using a Self-Developed Miniature Wave Buoy. Sensors. 2022; 22(19):7210. https://doi.org/10.3390/s22197210
Chicago/Turabian StyleZhong, Yao-Zhao, Hwa Chien, Huan-Meng Chang, and Hao-Yuan Cheng. 2022. "Ocean Wind Observation Based on the Mean Square Slope Using a Self-Developed Miniature Wave Buoy" Sensors 22, no. 19: 7210. https://doi.org/10.3390/s22197210
APA StyleZhong, Y.-Z., Chien, H., Chang, H.-M., & Cheng, H.-Y. (2022). Ocean Wind Observation Based on the Mean Square Slope Using a Self-Developed Miniature Wave Buoy. Sensors, 22(19), 7210. https://doi.org/10.3390/s22197210