基于CMONOC GPS数据的SSA电离层预测模型研究

Ionospheric Prediction of SSA Model Based on CMONOC GPS

利用基于CMONOC的GPS观测数据反演中国大陆区域高精度的RIM,并将奇异谱分析(SSA)方法应用于TEC预报,判断不同序列长度对预测结果的影响,并根据w-correlation选取合适的RC迭代阶数和窗口长度。结果发现,当TEC时间序列长度为27d,窗口长度为序列长度的1/3、迭代SSA分解的前5项时,预测效果最好。提取RIM中心网格点的TEC数据,分别以年积日1~27、101~127、201~227、301~327等4个时段的TEC序列为原始数据,基于SSA进行7d的预测,同时建立ARMA预测模型。结果显示,相较于ARMA预测,SSA方法总体预测精度提高约10%,预测时段更长。进一步对4个时段RIM中2911个网格点处的TEC进行预测,发现RMSE随着纬度减小而增大,预测相对精度呈现中纬度比高、低纬度略高的特点,但无论哪种精度指标,SSA预测模型均优于ARMA预测模型。

Based on GPS observations from CMONOC,the RIM over China is derived precisely.We introduce a new method of singular spectrum analysis(SSA)to estimate the prediction models of TEC extracted from RIM.A suitable length of original TEC time series of 27 days is selected.By the w-correlation,the RC and window size of prediction model can be also determined.It is found that when the size of window is set as 1/3 of original series,the iteration of the first 5 RC decomposed by SSA has the best effects.The TEC data at center grid point of RIM from 1 to 27 d,101 to 127 d,201 to 227 d,301 to 327 d are extracted respectively,to predict TEC for 7 days by SSA method.Meanwhile,the ARMA prediction model is also found and the models are compared.The results show that,compared with the ARMA model,the relative accuracy of SSA model is improved 10%for 7 days,with better long-prediction and anti-magnetic abilities.Furthermore,the TEC data of 2911 points are predicted by both methods.It is found that the RMSE gradually rose along with the decline of latitude,while the relative accuracy of grids over mid-latitude is slightly higher than other regions.However,regardless of the evaluation index,the SSA prediction model is superior to ARMA model.