Altimeter significant wave height data assimilation in the South China Sea using Ensemble Optimal Interpolation

Lei Cao; Yijun Hou; Peng Qi

doi:10.1007/s00343-014-4252-6

Your Location：

Home >

Browse articles >

Altimeter significant wave height data assimilation in the South China Sea using Ensemble Optimal Interpolation

OriginalPaper | Updated：2023-05-16

- Altimeter significant wave height data assimilation in the South China Sea using Ensemble Optimal Interpolation
- Altimeter significant wave height data assimilation in the South China Sea using Ensemble Optimal Interpolation
- 海洋湖沼学报（英文） 2015年33卷第5期页码：1309-1319
- Affiliations：
  
  1. Institute of Oceanology Chinese Academy of Sciences
  2. Key Laboratory of Ocean Circulation and Waves Chinese Academy of Sciences
  3. University of Chinese Academy of Sciences
- Author bio：
- Funds：
  
  Supported by the National Special Research Fund for Non-Profit Marine Sector (Nos. 201005033, 201105002), the National Natural Science Foundation of China (No. U1133001), the National High Technology Research and Development Program of China (863 Program) (No. 2012AA091801), and the NSFC-Shandong Joint Fund for Marine Science Research Centers (No. U1406401)
- DOI：10.1007/s00343-014-4252-6
  中图分类号：
- 网络首发：2015-09-08，
  
  纸质出版：2015
- Accepted：
Scan QR Code
Altimeter significant wave height data assimilation in the South China Sea using Ensemble Optimal Interpolation[J]. 海洋湖沼学报（英文）, 2015,33(5):1309-1319.

Cao, L., Hou, Y. & Qi, P. Altimeter significant wave height data assimilation in the South China Sea using Ensemble Optimal Interpolation., Chin. J. Ocean. Limnol. 33, 1309–1319 (2015). https://doi.org/10.1007/s00343-014-4252-6
Altimeter significant wave height data assimilation in the South China Sea using Ensemble Optimal Interpolation[J]. 海洋湖沼学报（英文）, 2015,33(5):1309-1319. DOI： 10.1007/s00343-014-4252-6.

Cao, L., Hou, Y. & Qi, P. Altimeter significant wave height data assimilation in the South China Sea using Ensemble Optimal Interpolation., Chin. J. Ocean. Limnol. 33, 1309–1319 (2015). https://doi.org/10.1007/s00343-014-4252-6 DOI：

摘要

Abstract

The application of ensemble optimal interpolation in wave data assimilation in the South China Sea is presented. A sampling strategy for a stationary ensemble is first discussed. The stationary ensemble is constructed by sampling from 24-h-interval significant wave height differences of model outputs over a long period

and is validated with altimeter significant wave height data

indicating that the ensemble errors have nearly the same probability distribution function. The background error covariance fields expressed by the ensemble sampled are anisotropic. Updating the static samples by season

the seasonal characteristics of the correlation coefficient distribution are reflected. Hindcast experiments including assimilation and control runs are conducted for the summer of 2010 in the South China Sea. The effect of ensemble optimal interpolation assimilation on wave hindcasts is validated using different satellite altimeter data (Jason-1 and 2 and ENVISAT) and buoy observations. It is found that the ensemble-optimal-interpolation-based wave assimilation scheme for the South China Sea achieves improvements similar to those of the previous optimal-interpolation-based scheme

indicating that the practical application of this computationally cheap ensemble method is feasible.

关键词

Keywords

references

Abdalla S, Janssen P A E M, Bidlot J R. 2011. Altimeter near real time wind and wave products: random error estimation. Mar. Geod., 34 (3-4): 393–406.

Bender L C, Glowacki T. 1996. The assimilation of altimeter data into the Australian wave model. Aust. Meteorol. Mag., 45 (1): 41–48.

Bretherthon F P, Garrett C J R. 1968. Wave trains in inhomogeneous moving media. Proceedings of the Royal Society of London. Series A, Mathematical and Physical Sciences, 302 (1471): 529–554.

Cao L, Qi P. 2015. Application of ensemble optimal interpolation method in wave assimilation in north Indian Ocean. Advances in Marine Science, 33 (1): 45–55. (in Chinese with English abstract)

Courtier P, Talagrand O. 1987. Variational assimilation of meteorological observations with the adjoint vorticity equation. i: numerical results. Quart. J. Roy. Meteor. Soc., 113 (478): 1329–1347.

Dimet F-X L, Talagrand O. 1986. Variational algorithms for analysis and assimilation of meteorological observations: theoretical aspects. Tellus A, 38 (2): 97–110.

Dumont J P, Rosmorduc V, Picot N, Bronner E, Desai S, Bonekamp H, Figa J, Lillibridge J, Scharroo R. 2011. OSTM/Jason-2 products handbook. CNES: SALP-MUM-OP-15815-CN, EUMETSAT: EUM/OPS-JAS/ MAN/08/0041, JPL: OSTM-29-1237, NOAA/NESDIS: Polar Series/OSTM J400.

Esteva D. 1988. Evaluation of preliminary experiments assimilating Seasat significant wave heights into a spectral wave model. J. Geophys. Res., 93 (C11): 14 099–14 105.

Evensen G. 2003. The ensemble Kalman filter: theoretical formulation and practical implementation. Ocean Dynam., 53 (4): 343–367.

Fu W W, Zhu J, Yan C X. 2009. A comparison between 3DVAR and EnOI techniques for satellite altimetry data assimilation. Ocean Model., 26 (3-4): 206–216.

Galanis G, Emmanouil G, Chu P C, Kallos G. 2009. A new methodology for the extension of the impact of data assimilation on ocean wave prediction. Ocean Dynam., 59 (3): 523–535.

Greenslade D J M, Schulz E W, Kepert J D, Warren G R. 2005. The impact of the assimilation of scatterometer winds on surface wind and wave forecasts. Journal of Atmospheric & Ocean Science, 10 (3): 261–287.

Greenslade D J M, Young I R. 2004. Background errors in a global wave model determined from altimeter data. J. Geophys. Res., 109 (C9), http://dx.doi.org/10.1029/2004JC002324.

Greenslade D J M, Young I R. 2005a. The impact of altimeter sampling patterns on estimates of background errors in a global wave model. J. Atmos. Oceanic Technol., 22 (12): 1895–1917.

Greenslade D J M, Young I R. 2005b. The impact of inhomogenous background errors on a global wave data assimilation system. Journal of Atmospheric & Ocean Science, 10 (2): 61–93.

Greenslade D J M. 2001. The assimilation of ERS-2 significant wave height data in the Australian region. J. Marine Syst., 28 (1-2): 141–160.

Günther H, Hasselmann S, Janssen P A E M. 1992. Wamodel Cycle 4. DKRZ Report No. 4, Hamburg.

Guo Y Y, Hou Y J, Zhang C M, Yang J. (2012. A background error covariance model of significant wave height employing Monte Carlo simulation. Chin. J. Ocean. Limnol., 30 (5): 814–821.

Hasselmann S, Brüning C, Hasselmann K, Heimbach P. 1996. An improved algorithm for the retrieval of ocean wave spectra from synthetic aperture radar image spectra. J. Geophys. Res., 101 (C7): 16 615–16 629.

Lionello P, Günther H, Hansen B. 1995. A sequential assimilation scheme applied to global wave analysis and prediction. J. Marine Syst., 6 (1-2): 87–107.

Lionello P, Günther H, Janssen P A. 1992. Assimilation of altimeter data in a global third-generation wave model. J. Geophys. Res., 97 (C9): 14 453–14 474.

Longuet-Higgins M S, Stewart R W. 1961. The changes in amplitude of short gravity waves on steady non-uniform currents. Journal of Fluid Mechanics, 10 (4): 529–549.

Ren Q F. 2010. Statistical analysis of background error covariance structure and Envisat ASAR wave spectrum data assimilation research. Institute of Oceanology, Chinese Academy of Sciences, Qingdao. (in Chinese)

Sun M, Yin X Q, Yang Y Z. 2014. Construction and application in global wave data assimilation of static sample set. Oceanologia et Limnologia Sinica, 45 (5): 918–927. (in Chinese with English abstract)

Thomas J P. 1988. Retrieval of energy spectra from measured data for assimilation into a wave model. Quart. J. Roy. Meteor. Soc., 114 (481): 781–800.

Tolman H L. 2009. User manual and system documentation of WAVEWATCH III TM version 3.14. http://polar.ncep.noaa.gov/mmab/papers/tn276/MMAB_276.pdf.

Voorrips A C, Makin V K, Hasselmann s. (1997. Assimilation of wave spectra from pitch-and-roll buoys in a North Sea wave model. J. Geophys. Res., 102 (C3): 5829–5849.

Wang Y, Yu Z W. 2009. Validation of impact of assimilation of altimeter satellite significant wave height on wave forecast in the northwest Pacific. Acta Oceanologica Sinica, 31 (6): 1–8. (in Chinese with English abstract)

Whitham G B. 1965. A general approach to linear and nonlinear dispersive waves using a lagrangian. J. Fluid Mech., 22 (2): 273–283.

Xie J, Counillon F, Zhu J, Bertino L. 2011. An eddy resolving tidal-driven model of the South China Sea assimilating along-track SLA data using the EnOI. Ocean Sci., 7 (5): 609–627.

Yan C X, Zhu J. (2011. Choice of ensemble members for ensemble optimal interpolation. Climatic and Environmental Research, 16 (4): 452–458. (in Chinese with English abstract)

Zhang Z X, Li C W, Li Y S, Qi Y Q. 2006. Incorporation of artificial neural networks and data assimilation techniques into a third-generation wind-wave model for wave forecasting. J. Hydroinform., 7: 65–76.

Zhang Z X, Qi Y Q, Shi P, Li C W, Li Y s. (2003. Application of an optimal interpolation wave assimilation method in South China Sea. Journal of Tropical Oceanography, 22 (4): 34–41. (in Chinese with English abstract)

浏览量

Downloads

CSCD

文章被引用时，请邮件提醒。

Submit

工具集

关联资源

暂无数据