【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。更多还原

【Abstract】 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.更多还原