区域平均海平面高度异常的统计预测
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摘要
本研究基于SODA(Simple Ocean Data Assimilation)的月平均海洋数据,提取出南海区域平均海平面高度异常(SSHA)的时间序列,并基于该时间序列开展了统计预测工作。研究中使用时间序列分解方法,将南海区域平均逐月SSHA时间序列分解为3个部分:年际变化项、季节项和扰动项。根据分解出的这3项时间序列变化特征,分别使用指数平滑法和自回归移动平均法去拟合时间序列中的年际变化项和扰动项,季节项将作为循环变化项叠加到前两项上。由此,建立了适用于该时间序列的预测模型,并且测试了该模型的预测能力。结果显示,研究建立的南海平均海平面高度异常模型的平均有效预报时间约为7个月,预报能力在春季和秋季较其余季节要强一些。另外,该模型在模拟时段内的预报技巧具有显著的十年际变化特征。
Based on the simple ocean data assimilation(SODA)data,this study analyzes and forecasts the monthly sea surface height anomaly(SSHA)averaged over the South China Sea(SCS).The approach to perform the analysis is a time series decomposition method,which decomposes monthly SSHAs in SCS to the following three parts:interannual,seasonal,and residual terms.To investigate the predictability of SCS SSHA,an exponential smoothing approach and an auto-regressive integrated moving average approach are first used to fit the interannual and residual terms of SCS SSHA while keeping the seasonal part invariant.Then,an array of forecast experiments are performed based on the prediction model which integrates the above two models and the time-independent seasonal term.Results indicate that the valid forecast time of SCS SSHA of the statistical model is about 7-month,and the predictability of SCS SSHA in Spring and Autumn is stronger than that in Summer and Winter.In addition,the prediction skill of SCS SSHA has remarkable decadal variability.
Based on the simple ocean data assimilation(SODA)data,this study analyzes and forecasts the monthly sea surface height anomaly(SSHA)averaged over the South China Sea(SCS).The approach to perform the analysis is a time series decomposition method,which decomposes monthly SSHAs in SCS to the following three parts:interannual,seasonal,and residual terms.To investigate the predictability of SCS SSHA,an exponential smoothing approach and an auto-regressive integrated moving average approach are first used to fit the interannual and residual terms of SCS SSHA while keeping the seasonal part invariant.Then,an array of forecast experiments are performed based on the prediction model which integrates the above two models and the time-independent seasonal term.Results indicate that the valid forecast time of SCS SSHA of the statistical model is about 7-month,and the predictability of SCS SSHA in Spring and Autumn is stronger than that in Summer and Winter.In addition,the prediction skill of SCS SSHA has remarkable decadal variability.
引文
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[27]Maryam Jahani ini sofla,Bromand Salahi,Mohammad Taghi Masomi.Germi County Seasonal PrecipitationRouting and Analysis,Using Holt-Winters Method for Time Series with Non-Seasonal Changes[J].Technical Journal of Engineering and Applied Sciences,2013,3(11):950-953.
[28]G Manoj j,Dholakia M B.Prediction of maximum/minimum temperatures using Holt Winters Method with Excel Spread Sheet for Junagadh Region[J].International Journal of Engineering Research&Technology,2012,1(6):1-8.
[29]D M Nickerson,B C Madsen.Nonlinear regression and ARIMA models for precipitation chemistry in East Central Florida from 1978 to 1997[J].Envrionmental Pollution,2005,135(3):371-379.
[30]Janhabi Meher,Ramakar Jha.Time-series analysis of monthly rainfall data for the Mahanadi River Basin[J].Sciences in Cold and Arid Regions,2013,5(1):73-84.
[31]Dake Chen Mark A Cane,Alexey Kaplan,Stephen E Zebiak.Predictability of El Nino over the past 148years[J].Nature,2004,428:733-736.
[32]Yuan Zhang,John M Wallace,David S Battisti.ENSOlike Interdecadal Variability:1900-93[J].Journal of Climate,1997,10:1 004-1 020.
[2]R J Nicholls,P P Wong,V R Burkett.Coastal systems and low-lying areas[M].Cambridge:Cambridge University Press,2007:315-356.
[3]J L Chen,C.R.Wilson,D P Chambers.Seasonal global water mass budget and mean sea level variations[J].Geophysical Research Letters,1998,25(19):3555-3558.
[4]Carmen Boening,Josh K Willis,Felix W Landerer.The2011 La Nina:so strong,the oceans fell[J].Geophysical Research Letters,2012,39:9602-9604.
[5]M Becker,B Meyssignac,C Letetrel.Sea level variations at tropical Pacific islands since 1950[J].Glob Planet Chang,2012,8:85-98.
[6]Ping Tung Shaw,Shenn Yu Chao,Lee Lueng Fu.Sea surface height variations in the South China Sea from satellite altimetry[J].Oceanologica Acta,1999,22(1):1-17.
[7]Caiyun Zhang,Bin Wang,Ge Chen.Annual sea level amphidromes in the South China Sea revealed by merged altimeter data[J].Geophysical Research Letters,2006,33:4 606-4 607.
[8]Guohong Fang,Haiying Chen,Zexun Wei.Trends and interannual variability of the South China Sea surface winds,surface height,and surface temperature in the recent decade[J].Journal of Geophysical Research:Oceans,2006,111:16-18.
[9]D J Peng,H Palanisamy,A Cazenave.Interannual sea level variations in the South China Sea over 1950-2009[J].Marine Geodesy,2013,36(2):164-182.
[10]Chung Ru Ho,Quanan Zheng,Yin S Soong.Seasonal vaiiability of sea surface height in the South China Sea observed with TOPEX/Poseidon altimeter data[J].Journal of Geophysical Research:Oceans,2000,105(C6):13 981-13 990.
[11]丁荣荣,左军成,杜凌,等.南海海平面变化及其比容高度和风场何的关系[J].2007,37(S2):23-30.
[12]C Haoliang,P M Rizzoli.Sea level rising trends in the South China Sea over 1993-2011[J].Proceeding of the7~(th)International Conference on Asia and Pacific Coasts,2013,4:979-983.
[13]Zhengyu Liu,Haijun Yang,Q Y Liu.Regional dynamics of seasonal variability in the South China Sea[J].Journal of Physical Oceanography,2000,31:272-284.
[14]刘秦玉,贾英来,杨海军,等.南海北部海面高度季节变化的机制[J].2002,24(S1):134-141.
[15]Xuhua Cheng,Yiquan Qi.On steric and mass-induced contributions to the annual sea-level variations in the South China Sea[J].Global and Planetary Change,2010,72(3):227-233.
[16]Jian Zhou,Peiliang Li,Haili Yu.Characteristics and mechanisms of sea surface height in the South China Sea[J].Global and Planetary Change,2012,8:20-31.
[17]Zengrui Rong,Yuguang Liu,Haibo Zong.Interannual sea level variability in the South China Sea and its response to ENSO[J].Global and Planetary Change,2007,55:257-272.
[18]Wei Zexun,Fang Guohong,Choi Byung Ho.Sea surface height and transport stream function of the South China Sea from a variable-grid global ocean circulation model[J].Science in China,2003,46(2):140-148.
[19]Chau Ron Wu,C W June Chang.Interannual variability of the South China Sea in a data assimilation model[J].Geophysical Re search Letters,2003,32:7611-7613.
[20]N S Timothy.Coupled Ocean-Atmocphere Forecasts in the Presence of Climate Drift[J].Monthly Weather Review,1997:125,809-818.
[21]Melisa Menendez,Fernando J Mendez,Inigo J Losada.Forecasting seasonal to interannual variability in extremesea levels[J].ICES J Mar.Sci.,2009,66(7):1 490-1 496.
[22]MD Rrshed Chowdhury,Pao Shin Chu,Thomas Schroeder.Seasonal sea-level forecasts by canonical correlation analysis-an operational scheme for the U.S.-affiliated Pacific Islands[J].Int J Climatology,2007,27:1 389-1 402.
[23]Moslem Imani,Rey Jer You,Chung Yen Kuo.Accurate forecasting of the satellite-derived seasonal Caspian Sea level anomaly using polynomial interpolation and holtwinters exponential smoothing[J].Terr.Atmos.Ocean.Sci.,2013,24(4):521-530.
[24]J A Carton,B.S.Giese.A Reanalysis of Ocean Climate Using Simple Ocean Data Assimilation(SODA)[J].American Meteorogical Society,2008,136:2 999-3 017.
[25]A Corberan-Vallet,J D Bermudez,E Vercher.Forecasting correlated time series with exponential smoothing models[J].Int J Forecast.,2011,27,252-265.
[26]Emmanuel Oyatoye,T V O Fabson.A comparative study of simulation and time series model in quantifying bullwhip effect in supply Chain[J].Serbian Journal of Management,2011,6(2):145-154.
[27]Maryam Jahani ini sofla,Bromand Salahi,Mohammad Taghi Masomi.Germi County Seasonal PrecipitationRouting and Analysis,Using Holt-Winters Method for Time Series with Non-Seasonal Changes[J].Technical Journal of Engineering and Applied Sciences,2013,3(11):950-953.
[28]G Manoj j,Dholakia M B.Prediction of maximum/minimum temperatures using Holt Winters Method with Excel Spread Sheet for Junagadh Region[J].International Journal of Engineering Research&Technology,2012,1(6):1-8.
[29]D M Nickerson,B C Madsen.Nonlinear regression and ARIMA models for precipitation chemistry in East Central Florida from 1978 to 1997[J].Envrionmental Pollution,2005,135(3):371-379.
[30]Janhabi Meher,Ramakar Jha.Time-series analysis of monthly rainfall data for the Mahanadi River Basin[J].Sciences in Cold and Arid Regions,2013,5(1):73-84.
[31]Dake Chen Mark A Cane,Alexey Kaplan,Stephen E Zebiak.Predictability of El Nino over the past 148years[J].Nature,2004,428:733-736.
[32]Yuan Zhang,John M Wallace,David S Battisti.ENSOlike Interdecadal Variability:1900-93[J].Journal of Climate,1997,10:1 004-1 020.
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