用户名: 密码: 验证码:
背景误差相关结构的统计分析与Envisat ASAR海浪谱资料同化研究
详细信息    本馆镜像全文|  推荐本文 |  |   获取CNKI官网全文
摘要
海浪资料同化能够改进海浪的模拟和预报水平,影响海浪同化效果的重要因素是观测资料的选择和背景误差协方差矩阵(或背景误差相关函数矩阵)的表达。本文提出了一种新的背景误差构造方法,开展了有效波高背景误差相关结构的统计分析和参数化拟合研究;实现了Envisat ASAR海浪谱资料的最优插值同化,提出了两种各向异性的背景误差相关函数,开展了多种情形下的海浪谱同化实验并比较它们的同化效果。
     首先,基于第三代海浪数值模式LAGFD-WAM,将间隔24小时的有效波高预报之差作为有效波高背景误差的近似。基于模式预报的时间序列,构造了有效波高背景误差的时间序列,进而得到有效波高的误差协方差矩阵和背景误差相关函数,统计和分析了有效波高背景误差的相关结构。结果表明,有效波高背景误差是各向异性和非均质的。提出了4种解析形式的椭圆形状各向异性背景误差相关函数,给出了椭圆三个参数的拟合结果,讨论了预报间隔对有效波高背景误差结构统计的影响。
     其次,为了评估本文所用的海浪谱资料,对2003年至2008年的Envisat ASAR波模式海浪谱产品作了数据筛选和质量控制,并与太平洋海域的浮标实测资料进行了比对分析。结果表明:(1)ASAR一维频率谱的谱形测量较准,但谱值大小差异较大,频率谱的低频部分与浮标实测吻合更好;(2)ASAR有效波高比浮标实测略低,两者的均值偏差为-0.05m,均方根偏差为0.62m(3)ASAR平均周期比浮标实测偏高,两者的均值偏差为0.97秒,均方根偏差为1.42秒。
     最后,开展了多种情形下的海浪同化实验。主要结论是:(1)传统的四种各向同性背景误差相关函数的同化效果相差不大,关键仍是对相关距离尺度的选取;相同相关距离尺度下的不同背景误差相关函数,可近似等效成不同相关距离尺度下的某一种背景误差相关函数。(2)存在最优的背景误差相关距离尺度,针对自回归形式的背景误差相关函数,本文结果表明相关距离尺度量级取400km至500km时同化效果最好,此时同化后的模式有效波高均方根误差比未同化时减小了26.0%。(3)各向异性背景误差相关函数的同化效果优于传统的各向同性误差相关函数;基于模式预报输出统计得到的“原始”各向异性背景误差相关函数的同化效果最好,椭圆形状各向异性的背景误差相关函数的同化效果次之,传统的各向同性背景误差相关函数的同化效果相对最差,它们使同化后的模式有效波高均方根误差比未同化时分别减小了26.4%、24.5%和23.4%。(4)同化实验表明波谱资料的最优插值同化效果优于单纯的有效波高资料最优插值。
Ocean wave data assimilation can improve the simulation and forecast level of the realistic wave field. The key factors influencing wave data assimilation effects are the choice of observation data and the presentation of background error covariance matrix (or background error correlation function). One new method to construct the background errors was given. The statistical analysis and parameteration of the correlation structure of significant wave height (SWH) background error were studied. The optimal interpolation (OI) assimilation of Envisat ASAR ocean wave data was carried out. And two different anisotropic background error correlation functions were presented. Finally, several groups of assimilation experiments under different settings were run to check their assimilation effects.
     Firstly, based on the third-generation wave model named LAGFD-WAM, the difference between the SWH forecasts with 24 hours interval is considered as the approximation of SWH background errors. The time series of SWH background errors were constructed from the time series of the wave model forecasts. And the background error covariance matrices and the background error correlation function matrices were also constructed. Then the correlation structure of SWH background errors was statistically analyzed. The results indicated that the SWH background error is anisotropic and inhomogeneous. Hence, four different ellipse-type anisotropic analytic forms of background error correlation functions were presented. The three parameters of error ellipses were fitted. And the influence of the forecast interval on the background error structure statistic was discussed too.
     Secondly, Envisat ASAR wave mode ocean wave spectra products from year 2003 to 2008 which had been filtered and quality-controlled were compared with buoy observation data in Pacific in order to assess the wave spectra data. The results show that: (1) ASAR one-dimensional frequency spectra agree well with buoy observations in spectral shapes and bad in spectral values. The agreement in the low frequency domain is better than that in high frequency domain. (2) ASAR SWH is a little lower than SWH of buoy observations. The mean bias of SWH is -0.05m and the root mean square (RMS) error is 0.62m. (3) The mean wave period of ASAR is higher than that of buoy observations. The mean bias is 0.97s and RMS error is 1.42s.
     Finally, several ocean wave assimilation experiments under different situations and settings were run. The main conclusions are that (1) the difference of assimilation effects among traditional four isotropic background error correlation functions is not obvious and the key is still the choice of the correlation length scale. Different background error correlation functions under the same background error correlation length scale can be approximated by the one background error correlation function under different correlation length scales. (2) The optimal background error correlation length scale exists. For the auto-regressive background error correlation function, the assimilation effect is found to be best when the correlation length scale is assumed to be from 400km to 500km. And the modeled SWH RMS error reduced relatively 26% than that with no data assimilation in the best case. (3) The assimilation effect of anisotropic background error correlation functions is better than that of isotropic background error correlation functions. The effect of the experiment with the original anisotropic background error correlation function from wave model output statistics is the best, followed by the experiment with the ellipse-type anisotropic background error correlation function, and the last is the experiment with the traditional isotropic background error correlation function. The SWH RMS errors reduced relatively 26.4%, 24.5% and 23.4% respectively than that with no data assimilation in these three cases. (4) The assimilation experiment here also showed that assimilation effect of the wave spectra data OI was better than the only SWH OI.
引文
[1] Abdalla S, Bidlot JR, Janssen P. Global validation and assimilation of Envisat ASAR wave mode spectra[C]. Proc. SEASAR 2006, Frascati, Italy, 2006, p8.1.
    [2] Amante C, Eakins BW. ETOPO1 1 arc-minute global relief model: procedures, data sources and analysis[R]. NOAA Technical Memorandum NESDIS NGDC-24, 2009, pp19.
    [3] Aouf L, Lefèvre JM, Chapron B, Hauser D. Some recent improvements of the assimilation of upgraded ASAR L2 wave spectra[C]. Proc. SEASAR 08, ESA Worshop, Frascati, January, 2008.
    [4] Aouf L, Lefèvre JM, Hauser D, Chapron B. On the combined assimilation of RA-2 altimeter and ASAR wave data for the improvement of wave forecasting[C]. Proc. 15 Years of Radar Altimetry Symp., Venice, March, 2006a, pp13-18.
    [5] Aouf L, Lefèvre JM, Hauser D. Assimilation of directional wave spectra in the wave model WAM: An impact study from synthetic observations in preparation for the SWIMSAT satellite mission[J]. J. Atmos. Ocean Technol., 2006b, 23: 448-463.
    [6] Baker WE, Bloom SC, Woollen JS, Nestler MS, Brin E, Schlatter TW, Branstator GW. Experiments with a three-dimensional statistical objective analysis scheme using FGGE data[J]. Mon. Wea. Rev., 1987, 115: 272-296.
    [7] Bauer E, Hasselmann K, Young IR, Hasselmann S. Assimilation of wave data into the wave model WAM using an impulse response function method[J]. J. Geophys. Res., 1996, 101: 3801–3816.
    [8] Bauer E, Hasselmann S, Hasselmann K, Graber HC. Validation and assimilation of Seasat altimeter wave heights using the WAM wave model[J]. J. Geophys. Res., 1992, 97: 12671–12682.
    [9] Bender LC, Glowacki T. The assimilation of altimeter data into the Australian wave model[J]. Aust. Meteorol. Mag., 1996, 45: 41-48.
    [10] Bengtsson L, Gustavsson N. An experiment in the assimilation of data in dynamical analysis[J]. Tellus, 1971, 23: 328-336.
    [11] Bergthorsson P, D?? BR. Numerical weather map analysis[J]. Tellus, 1955, 7: 329-340.
    [12] Booij N, Ris RC, Holthuijsen LH. A third generation wave model for coastal regions; Part I: model description and validation[J]. J. Geophysical Res., 1999, 104: 7649–7666.
    [13] Bouttier F, Courtier P. Data assimilation concepts and methods[G]. Meteorological Training Course Lecture Series, ECMWF, Mar. 1999, pp1-59.
    [14] Bratseth AM. Statistical interpolation by means of successive corrections[J]. Tellus. Ser. A, 1986, 38: 439-447.
    [15] Breivik LA, Reistad M, Schyberg H, Sunde J, Krogstad HE, Johnsen H. Assimilation of ERS SAR wave spectra in an operational wave model[J]. J. Geophys. Res., 1998, 103: 7887-7900.
    [16] Breivik LA, Reistad M. Assimilation of ERS-1 altimeter wave heights in an operational numerical wave model[J]. Weather and Forecasting, 1994, 9: 440-451.
    [17] Brüning C, Hasselmann S, Hasselmann K, Lehner S, Gerling T. First evaluation of ERS-1 synthetic aperture radar wave mode data[J]. Global Atmos. Ocean Syst., 1994, 2: 61-98.
    [18] Burgers G, Gao QD, Heras MM de las. Wave data assimilation in the operational North Sea wave model Ndewam[C]. Proc. Int. Symp. on Assimilation of Observations in Meteorology and Oceanography, World Meteorol. Org., Geneva, Switzerland, 1990, pp623-625.
    [19] Burgers G, Makin VK, Gao QD, Heras MM de las. Wave data assimilation for operational wave forecasting at the North Sea[C]. Presented at the 3rd Int. Workshop on Wave Hindcasting Forecasting, Environment Canada, Montreal, Quebec, Canada, 1992, pp19-22.
    [20] Caires S, Sterl A, Gommenginger CP. Global ocean mean wave period data: validation and description[J]. J. Geophys. Res., 2005, 110: C02003, doi:10.1029/2004JC002631.
    [21] Cressman GP. An operational objective analysis system[J]. Monthly Weather Review, 1959, 87: 367-374.
    [22] Daley R. Atmospheric data analysis[M].: Cambridge University Press, Cambridge, 1991, pp457.
    [23] de Valk CF, Calkoen CJ. Wave data assimilation in a third generation wave prediction model for the North Sea: An optimal control approach[R]. Delft Hydraul. Lab., Delft, Netherlands, 1989, Rep. X38, pp123.
    [24] Dee DP. On-line estimation of error covariance parameters for atmospheric data assimilation[J]. Mon. Wea. Rev., 1995, 123: 1128-1145.
    [25] Dunlap EM, Olsen RB, Wilson L, De Margerie S, Lalbeharry R. The effect of assimilating ERS-1 fast delivery wave data into the north Atlantic WAM model[J]. J. Geophys. Res., 1998, 103: 7901-7915.
    [26] Durrant TH, Greenslade DJM, Simmonds I. Validation of Jason-1 and Envisat remotely sensed wave heights[J]. J. Atmos. Oceanic Technol., 2009, 26(1): 123-134.
    [27] Durrant TH, Woodcock F, Greenslade DJM. Consensus forecasts of modeled wave parameters[J]. Weather and Forecasting, 2009, 24: 492-503.
    [28] Engen G, Barstow SF, Johnson H, Krogstad HE. Directional wave spectra by inversion of ERS-1 synthetic aperture radar ocean imagery[J]. IEEE Trans. Geosci. Remote Sens., 1994, 32(2): 340-352.
    [29] Engen G, Johnsen H. SAR ocean wave inversion using image cross spectra[J]. IEEE Trans. Geoscience and Remote Sensing, 1995, 33(4): 1047-1056.
    [30] ESA. Envisat ASAR Product Handbook[G]. European Space Agency, Issue 2.1, Mar. 2006.
    [31] Esteva DC. Evaluation of preliminary experiments assimilating Seasat significant wave height into a spectral wave model[J]. J. Geophys. Res., 1988, C93: 14099–14105.
    [32] Foreman SJ, Holt MW, Kelsall S. Preliminary assessment and use of ERS-1 altimeter wave data[J]. J. Atmos. Oceanic Technol., 1994, 11: 1370-1380.
    [33] Francis PE, Stratton RA. Some experiments to investigate the assimilation of Seasat altimeter wave height data into a global wave model[J]. Q. J. R. Meteorol. Soc., 1990, 116: 1225-1251.
    [34] Gelci R, CazaléH, Vassal J. Bull. Inform. ComitéCentral Océanogr. Etude C?tes, 1956, 8: 170-187.
    [35] Gelci R, CazaléH, Vassal J. Prévision de la houle. La méthode des densités spectroangulaires[J]. Bull. Inform. ComitéCentral Océanogr. Etude C?tes, 1957, 9: 416-435.
    [36] Gerling TW. Partitioning sequences and arrays of directional ocean wave spectra into component wave systems[J]. J. Atmos. Oceanic Technol., 1992, 9: 444-458.
    [37] Greenslade DJM, Young IR. Background errors in a global wave model determined from altimeter data[J]. J. Geophys. Res., 2004, 109, C09007, doi:10.1029/2004JC002324.
    [38] Greenslade DJM, Young IR. Forecast divergence of a global wave model[J]. Mon. Wea. Rev., 2005b, 133: 2148-2162.
    [39] Greenslade DJM, Young IR. The impact of altimeter sampling patterns on estimates of background errors in a global wave model[J]. J. Atmos. Oceanic Technol., 2005a, 22: 1895-1917.
    [40] Greenslade DJM, Young IR. The impact of inhomogenous background errors on a global wave data assimilation system[J]. Journal of Atmospheric and Ocean Science, 2005c, 10: 61-93.
    [41] Greenslade DJM. The assimilation of ERS-2 significant wave height data in the Australian region[J]. J. Mar. Syst., 2001, 28: 141-160.
    [42] Günther H, Lionello P, Hanssen B. The impact of the ERS-1 altimeter on the wave analysis and forecast[R]. GKSS Forschungszentrum Geesthacht, Geesthacht, Germany, Rep. GKSS 93/E/44, 1993, pp56.
    [43] Hasselmann K, Barnett TP, Bouws E, Carlson H, Cartwright DE, Enke K, Ewing JA, Gienapp H, Hasselmann DE, Kruseman P, Meerburg A, Müller P, Olbers DJ, Richter K, Sell W, Walden H. Measurements of wind-wave growth and swell decay during the JOint North Sea WAve Project (JONSWAP)[J]. Dtsch. Hydrogr. Z. Suppl., 1973, A8(12), pp95.
    [44] Hasselmann K, Hasselmann S, Bauer E, Brüning C, Lehner S, Graber H, Lionello P. Development of a satellite SAR image spectra and altimeter wave height data assimilation system for ERS-1[R]. ESA Report, Max-Planck-Institute für Meteorologie, Nr. 19 Hamburg, 1988, pp155.
    [45] Hasselmann K, Hasselmann S. On the nonlinear mapping of an ocean wave spectrum into a SAR image spectrum and its inversion[J]. J. Geophys. Res., 1991, 96: 10713-10729.
    [46] Hasselmann K. On the non-linear energy transfer in a gravity-wave spectrum, part 1: General theory[J]. J. Fluid Mech., 1962, 12: 481-500.
    [47] Hasselmann K. On the non-linear energy transfer in a gravity-wave spectrum, part 2: Conservation theorems, wave-particle analogy, irrevesibility[J]. J. Fluid Mech., 1963, 15: 273-281.
    [48] Hasselmann K. On the non-linear energy transfer in a gravity-wave spectrum, part 3: Evaluation of the energy flux and swell-sea interaction for a Neumann spectrum[J]. J. Fluid Mech., 1963, 15: 385-398.
    [49] Hasselmann S, Brüning C, Hasselmann S, Heimbach P. An improved algorithm for the retrieval of ocean wave spectra from synthetic aperture radar image spectra[J]. J. Geophys. Res., 1996, 101: 16615-16629.
    [50] Hasselmann S, Brüning C, Lionello P. Towards a generalized optimal interpolation method for the assimilation of ERS-1 SAR retrieved wave spectra in a wave model[C]. Proc. Second ERS-1 Symposium, Hamburg, Germany, Eur. Space Agency Spec. Publ. ESA SP-361, 1994, pp21-25.
    [51] Hasselmann S, Hasselmann K, Allender JH, Barnett TP. Computations and parameterizations of the nonlinear energy transfer in a gravity-wave spectrum, part 2: Parameterizations of the nonlinear transfer for application in wave models[J]. J. Phys. Oceangr., 1985, 15: 1378-1391.
    [52] Hasselmann S, Hasselmann K. Computations and parameterizations of the nonlinear energy transfer in a gravity-wave spectrum, part 1: A new method for efficient computations of the exact nonlinear transfer integral[J]. J. Phys. Oceangr., 1985, 15: 1369-1377.
    [53] Hasselmann S, Lionello P, Hasselmann K. An optimal interpolation scheme for the assimilation of spectral wave data[J]. J. Geophys. Res., 1997, 102: 15823-15836.
    [54] Heras MM de las, Burgers G, Janssen PAEM. Variational wave data assimilation in a third-generation wave model[J]. J. Atmos. Ocean Technol., 1994, 11: 1350-1369.
    [55] Heras MM de las, Burgers G, Janssen PAEM. Wave data assimilation in the WAM wave model[J]. J. Mar. Syst., 1995, 6: 77-85.
    [56] Heras MM de las, Janssen PAEM. Data assimilation with a coupled wind-wave model[J]. J. Geophys. Res., 1992, 97: 20261-20270.
    [57] Heras MM de las. On variational data assimilation in ocean wave models[D]. Ph.D. Thesis, University of Utrecht, 1994.
    [58] Hersbach L. Application of the adjoint of the WAM model to inverse wave modeling[J]. J. Geophys. Res., 1998, 103: 10469-10488.
    [59] Hollingsworth A, L?nnberg P. The statistical structure of short-range forecast errors as determined from radiosonde data: I. The wind field[J]. Tellus, Ser. A, 1986, 38: 111-136.
    [60] Hollingsworth A. Objective analysis for numerical weather prediction[J]. J. Meterol. Soc. Jpn., 1987, 65: 11-60.
    [61] Holthuijsen LH, Booij N, Endt M van, Caires S, Soares CG. Assimilation of buoy and satellite data in wave forecasts with integral control variables[J]. J. Mar. Syst., 1997, 13: 21-31.
    [62] Janssen PAEM, Abdalla S, Bidlot JR. Envisat wind and wave products: monitoring, validation and assimilation[C]. Proc. Envisat Symp. 2007, Montreux, Switzerland, ESA, 2007, SP-636.
    [63] Janssen PAEM, Abdalla S, Hersbach H. Error estimation of buoy, satellite and model wave height data[R]. ECMWF Tech. Memo., 402, Reading, England, 2003.
    [64] Janssen PAEM, Lionello P, Reistad M, Hollingsworth A. Hindcasts and data assimilation studies with the WAM model during the Seasat period[J]. J. Geophys. Res., 1989, 94: 973-993.
    [65] Johnsen H, Chapron B, Walker N, Desnos YL. The ASAR wave mode: level 1 and level 2 algorithms and products[C]. Proc. Envisat Calibration Review, Noordwijk, Netherlands, ESA, 2002a, SP-520.
    [66] Johnsen H, Collard F. Comparison of reprocessed ASAR WM ocean wave spectra with WAM and buoy spectra[C]. Proc. Envisat Symp. 2007, Montreux, Switzerland, ESA, 2007, SP-636.
    [67] Johnsen H, Engen G, Chapron B, Walker N, Closa J. Validation of ASAR wave mode level 2 product[C]. Proc. Envisat Validation Workshop, Frascati, Italy, 2002b, p9.
    [68] Johnsen H, Engen G, Collard F, Kerbaol V, Chapron B. Envisat ASAR wave mode products-quality assessment and algorithm upgrade[C]. Proc. SEASAR 2006, Frascati, Italy, ESA, 2006, SP-613.
    [69] Kerbaol V, Johnsen H, Chapron B, Rosich B. Quality assessment of Envisat ASAR wave mode products based on regional and seasonal comparisons with WAM model outputs[C]. Proc. ENVISAT/ERS Symp., Salzburg, Austria, 2004, ESA-572.
    [70] Komen G J. Introduction to wave models and assimilation of satellite data in wave models[C]. Proc. Alpbach Conference, ESA Spec. Publ., ESA SP-244, 1985, pp21-26.
    [71] Komen GJ, Cavaleri L, Donelan M, Hasselmann K, Hasselmann S, Janssen PAEM. Dynamics and modelling of ocean waves[M]. Cambridge University Press, London, 1994.
    [72] Komen GJ, Hasselmann K, Hasselmann S. On the existence of a fully develped windsea spectrum[J]. J. Phys. Oceanogr., 1984, 14: 1271-1285.
    [73] Krogstad HE. A simple derivation of Hasselmann’s nonlinear ocean-sar transformation[J]. J. Geophys. Res., 1992, 97: 2421-2425.
    [74] Li JG, Holt M. Comparison of Envisat ASAR ocean wave spectra with buoy and altimeter data via a wave model[J]. J. Atmos. Oceanic Technol., 2009, 26(3): 593-614.
    [75] Lionello P, Günther H, Hansen B. A sequential assimilation scheme applied to global wave analysis and prediction[J]. J. Mar. Syst., 1995, 6: 87-107.
    [76] Lionello P, Günther H, Janssen PAEM. Assimilation of altimeter data in a global third-generation wave model[J]. J. Geophys. Res., 1992, 97: 14453-14474.
    [77] Lionello P, Janssen PAEM. Assimilation of altimeter measurements to update swell spectra in wave models[C]. Proc. Int. Symp. Assimilation of Observations in Meteorology and Oceanography, Clermond-Ferrand, France, 1990, pp241-248.
    [78] Lorenc AC. A global three-dimensional multivariate statistical interpolation scheme[J]. Monthly Weather Review, 1981, 109: 701-721.
    [79] Lorenc AC. A practical approximation to optimal four-dimensional objective analysis[J]. Monthly Weather Review, 1988, 116: 730-745.
    [80] Lorenc AC. Analysis methods for numerical weather prediction[J]. Quart. J. R. Met. Soc., 1986, 112: 1177-1194.
    [81] Mastenbroek C, Makin VK, Voorrips AC, Komen GJ. Validation of ERS-1 altimeter wave height measurements and assimilation in a North Sea wave model[J]. Global Atmos. Ocean Syst., 1994, 2: 143-161.
    [82] Miles JW. On the generation of surface waves by shear flows[J]. J. Fluid Mech., 1957, 3: 185-204.
    [83] Mitsuyasu H. On the growth of the spectrum of wind-generated waves. 1[J]. Rep. Res. Inst. Appl. Mech., Kyushu Univ., 1968, 16: 251-264.
    [84] Mitsuyasu H. On the growth of the spectrum of wind-generated waves. 2[J]. Rep. Res. Inst. Appl. Mech., Kyushu Univ., 1969, 17: 235-243.
    [85] Parrish DF, Derber JC. The National Meteorological Center’s spectral statistical- interpolation analysis system[J]. Mon. Wea. Rev., 1992, 120: 1747-1763.
    [86] Phillips OM. On the generation of waves by turbulent wind[J]. J. Fluid Mech., 1957, 2: 417-445.
    [87] Phillips OM. Spectral and statistical properties of the equilibrium range in wind-generated gravity waves[J]. J. Fluid Mech., 1985, 156: 505-531.
    [88] Phillips OM. The equilibrium range in the spectrum of wind-generated water waves[J]. J. Fluid Mech., 1958, 4: 426-434.
    [89] Pierson WJ, Neumann G, James RW. Practical methods for observing and forecasting ocean waves by means of wave spectra and statistics[R]. H.O. Pub. 603, US Navy Hydrographic Office, Washington DC, 1955.
    [90] Pinto JP, Bernardino MC, Pires SA. A Kalman filter application to a spectral wave model[J]. Nonlinear Processes Geophys, 2005, 12: 775-782.
    [91] Plant WJ. A relation between wind stress and wave slope[J]. J. Geophys. Res., 1982, C87: 1961-1967.
    [92] Rabier F, McNally A, Andersson E, Courtier P, Undén P, Eyre J, Hollingsworth A, Bouttier F. The ECMWF implementation of three-dimensional variational assimilation (3D-Var). II: Structure functions[J]. Quart. J. Roy. Meteor. Soc., 1998, 124: 1809-1829.
    [93] Sannasiraj SA, Babovic V, Chan ES. Wave data assimilation using ensemble error covariances for operational wave forecast[J]. Ocean Modelling, 2006, 14: 102-121.
    [94] Seaman R, Bourke W, Steinle P, Hart T, Embery G, Naughton M, Rikus L. Evolution of the Bureau of Meteorology’s global assimilation and prediction system: 1. Analyses and initialization[J]. Aust. Meteorol. Mag., 1995, 44: 1–18.
    [95] Snyder RL, Dobson FW, Elliott JA, Long RB. Array measurements of atmospheric pressure fluctuations above surface gravity waves[J]. J. Fluid Mech., 1981, 102: 1-59.
    [96] Steinle P, Seaman R, Bourke W, Hart T. A generalized statistical interpolation scheme[C]. Proc. Second WMO Int. Symp. on Assimilation of Observations in Meteorology and Oceanography, WMO/TD No. 651, Tokyo, Japan, WMO, 1995, pp205-208.
    [97] Sverdrup HU, Munk WH. Wind sea and swell: Theory of relation for forecasting[R]. H.O. Pub. 601, US Navy Hydrographic Office, Washington DC, 1947, pp44.
    [98] SWAMP group: Allender JH, Barnett TP, Bertotti L, Bruinsma J, Cardone VJ, Cavaleri L, Ephraums J, Golding B, Greenwood A, Guddal J, Günther H, Hasselmann K, Hasselmann S, Joseph P, Kawai S, Komen GJ, Lawson L, LinnéH, Long RB, Lybanon M, Maeland E, Rosenthal W, Toba Y, Uji T, de Voogt WJP. Sea wave modeling project (SWAMP). An intercomparison study of wind wave predictions models, part 1: Principal results and conclusions[R], in: Ocean wave modeling; Plenum, New York, 1985, pp256.
    [99] Thomas JP. Retrieval of energy spectra from measured data for assimilation into a wave model[J]. Q. J. R. Meteorol. Soc., 1988, 114: 781-800.
    [100] Tolman HL, Balasubramaniyan B, Burroughs LD, Chalikov DV, Chao YY, Chen HS, Gerald VM. Development and implementation of wind generated ocean surface wave models at NCEP[J]. Wea. Forecasting, 2002, 17: 311-333.
    [101] Tolman HL. User manual and system documentation of WAVEWATCH III version 2.22[R]. Tech. Note 222, NOAA/NWS/NCEP/MMAB, 2002.
    [102] van der Vooren MA. Application of hierarchical control theory to wave-data assimilation[D]. Ph.D. Thesis, Twente University of Technology, 1994.
    [103] Voorrips AC, de Valk C. A comparison of two operational wave assimilation methods[J]. Global Atmos. Ocean Syst., 1997, 7: 1-46.
    [104] Voorrips AC, Heemink AW, Komen GJ. A Kalman filter for wave data assimilation in WAM[C]. Proc. Waves 97, Vol. 1, Virginia Beach, USA, 1997b, pp668-682.
    [105] Voorrips AC, Heemink AW, Komen GJ. Wave data assimilation with the Kalman filter[J]. J. Mar. Syst., 1999, 19: 267-291.
    [106] Voorrips AC, Makin VK, Hasselmann S. Assimilation of wave spectra from pitch-and-roll buoys in a North Sea wave model[J]. J. Geophys. Res., 1997a, C102(3): 5829-5849.
    [107] Voorrips AC, Mastenbroek C, Hansen B. Validation of two algorithms to retrieve ocean wave spectra from ERS synthetic aperture radar[J]. J. Geophys. Res., 2001, 106(C8): 16825-16840.
    [108] Voorrips AC. Optimal interpolation of partitions: a data assimilation scheme for NEDWAM-4[R]. KNMI Scientific Report WR97-02, 1997.
    [109] Voorrips AC. Sequential data assimilation methods for ocean wave models[D]. Ph.D. Thesis, Technical University Delft, the Netherlands, 1998.
    [110] Voorrips AC. Spectral wave data assimilation for the prediction of waves in the North Sea[J]. Coastal Engineering, 1999, 37: 455-469.
    [111] WAMDI group: Hasselmann S, Hasselmann K, Bauer E, Janssen PAEM, Komen GJ, Bertotti L, Lionello P, Guillaume A, Cardone VC, Greenwood JA, Reistad M, Zambresky L, Ewing JA. The WAM model– a third generation ocean wave prediction model[J]. J. Phys. Oceanogr., 1988, 18: 1775-1810.
    [112] Wen SC, Qian CC, Ye AL, Wu KJ, Wu ZM, Guan CL, Zhao DL. Wave modeling based on an adopted wind-wave directional spectrum[J]. J. Ocean Univ. Qingdao, 1999, 29(3): 345-397.
    [113] Wen SC, Zhang DC, Chen BH. A hybrids model for numerical wave forecasting and its implementation: part I, the wind wave model[J]. Acta Oceanologica Sinica, 1989, 8: 1-14.
    [114] Woodcock F, Greenslade DJM. Consensus of numerical model forecasts of significant wave heights[J]. Weather and Forecasting, 2007, 22: 792-803.
    [115] Yang YZ, Qiao FL, Pan ZD. Wave assimilation and numerical prediction[J]. Chin. J. Oceanol. Limnol., 2000, 4: 301-308.
    [116] Yang YZ, Yuan YL, Zhang J, Qiao FL. Wave data assimilation using the adjoint variational method in the LAGFD-WAM wave numerical model[C]. Proc. International Conference on Marine Disasters: Forecast and Reduction, Beijing: China Ocean Press, 1998, pp77-82.
    [117] Yang YZ, Zhao W, Teng Y. A temporal sliding procedure for ocean wave variational data assimilation and its application[J]. Chin. J. Oceanol. Limnol., 2006, 24(1): 35-41.
    [118] Yin BS, Wang T, MI EI-Sabh. A third generation shallow water wave numerical model–YE-WAM[J]. Chin. J. Oceanol. Limnol., 1996, 14(2): 106-112.
    [119] Yin XQ, Oey LY. Bred-ensemble ocean forecast during Katrina: Loop current and ring[J]. Ocean Modeling, 2007, 17: 300-326.
    [120] Young IR, Glowacki TJ. Assimilation of altimeter wave height data into a spectral wave model using statistical interpolation[J]. Ocean Eng., 1996, 23(8): 667-689.
    [121] Yuan YL, Hua F, Pan ZD, Sun LT. LAGFD-WAM numerical wave model-I. Basic physical model[J]. Acta Oceanol. Sinica, 1991a, 10: 483-488.
    [122] Yuan YL, Pan ZD, Hua F, Sun LT. LAGFD-WAM numerical wave model-II. Characteristics inlaid scheme and its application[J]. Acta Oceanol. Sinica, 1991b, 11: 13-23.
    [123] Yuan YL, Tung CC, Huang N. Statistical characteristics of breaking waves[A],‘Wave Dynamics and Radio Probing of the Ocean Surface’Edited by O M Phillips and K Hasselmann, Plenum Publishing Corporation Press, 1986, pp265-272.
    [124]高全多.一个耦合离散海浪模式[J].海洋预报, 1990, 7(3): 1-9.
    [125]管长龙.我国海浪理论及预报研究的回顾与展望[J].青岛海洋大学学报, 2000, 30(4): 549-556.
    [126]郭衍游,侯一筠,杨永增,韩玲玲.利用WaveWatchⅢ建立东中国海区域海浪同化系统[J].高技术通讯, 2006, 16(10): 1092-1096.
    [127]郭衍游.东中国海区域海浪同化系统设计与研究[D].青岛:中国科学院海洋研究所, 2006.
    [128]乔方利,杨永增.共轭变分资料同化方法及海洋大气模型数值试验II模式参数优化[J].海洋与湖沼, 1997, 28(增刊): 43-48.
    [129]王跃山,黄润恒.用插入观测法将高度计观测同化到海浪模式WAM中[J].海洋预报, 1999, 16(2): 1-17.
    [130]王跃山.数据同化—它的缘起、含义和主要方法[J].海洋预报, 1999, 16(1): 11-20.
    [131]文圣常,余宙文.海浪理论与计算原理[M].北京:科学出版社, 1984.
    [132]杨永增,乔方利,潘增弟,张杰.共轭变分资料同化方法及海洋大气模型数值试验I初值优化[J].海洋与湖沼, 1997, 28(增刊): 36-42.
    [133]杨永增,于卫东.特征线计算格式下共轭方程两种导出途径的比较[J].海洋湖沼通报, 2002, 2: 10-16.
    [134]杨永增,袁业立,张杰.海浪有效波高资料同化与试验分析[J].水动力学研究与进展(A辑), 1999, 14(4B): 181-188.
    [135]杨永增,张杰,潘增弟.海浪同化预报模型理论基础[J].黄渤海海洋, 2001, 19(3): 26-38.
    [136]杨永增,张杰. SAR资料的海浪方向谱同化研究[C].卫星微波遥感技术研讨会论文集, 1998, pp206-211.
    [137]杨永增.海浪初始谱误差演变初步分析[J].海洋预报, 2000, 17(4): 21-27.
    [138]杨永增.海浪谱能量方程稳定性、敏感性分析与海浪变分同化研究[D].青岛:中国科学院海洋研究所, 2001.
    [139]尹宝树,王涛,范顺庭. YW-SWP海浪数值预报模式及其应用[J].海洋与湖沼, 1994, 25(3): 350-353.
    [140]尹训强.全球大洋环流卫星高度计资料同化研究及其结果分析[D].青岛:国家海洋局第一海洋研究所, 2005.
    [141]于卫东,袁业立,潘增弟,华锋.关于第三代海浪模式控制方程的导出[J].海洋与湖沼, 1997, 28(增刊): 13-20.
    [142]袁业立,潘增弟,华锋,孙乐涛. LAGFD-WAM海浪数值模式I:基本物理模型[J].海洋学报, 1992a, 14(5): 1-7.
    [143]袁业立,潘增弟,华锋,孙乐涛. LAGFD-WAM海浪数值模式II:区域性特征线嵌入格式及其应用[J].海洋学报, 1992b, 14(6): 12-24.
    [144]张志旭,齐义泉,施平,李志伟,李毓湘.卫星高度计波高资料的同化试验分析[J].海洋学报, 2003a, 25(5): 21-28.
    [145]张志旭,齐义泉,施平,李志伟,李毓湘.最优化插值同化方法在预报南海台风浪中的应用[J].热带海洋学报, 2003b, 22(4): 34-41.
    [146]朱立娟.背景场误差协方差估计技术的应用研究[D].南京:南京信息工程大学, 2005.

© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号

地址:北京市海淀区学院路29号 邮编:100083

电话:办公室:(+86 10)66554848;文献借阅、咨询服务、科技查新:66554700