Taking elevation component time series of GNSS permanent stations as examples, effects of data de fect and noise level on results of GNSS time series analyzing are discussed based on both simulation and observation data. The results show that the higher the noise level is, the greater the effect will be. If the days of data defect are the same, the effect of continuous data defect is much greater than the effect of discrete data defect. The interpola tion can' t remedy the data defect effectively in a high noise level if days of data defect are more than a certain num ber of days. On the basis, the request of observation data in different reliability requirements is discussed. It is pointed out when annual and shorter periodic variations are considered and to ensure deviation of linear velocity be less than 10% , for example, when the noise lever is 9mm, then the defect days for continuous data losing should be less than 11 days and for discrete data losing, the defect days should be less than 12 days. Last but not least, an advice is given that in the study of vertical crustal movement using elevation component time series of GNSS CORS, the operation status of GNSS permanent station should be strictly screened.%以GNSS连续站高程分量观测时间序列为例,讨论了不同噪声水平和数据缺失情况对GNSS时间序列分析结果的影响.结果表明,同样的数据缺失,噪声水平越高,对分析结果的影响越大；同样的数据缺失总数,数据缺失连续对分析结果的影响最大；在观测噪声较大的情况下,如果缺数天数超过一定的数量,通常无法用数据插补的方法弥补得到与观测时间长度相当的周期信号的较为可靠的结果.在此基础上,讨论了不同的可靠性需求下对观测数据的要求.并指出,如果顾及年及更短周期变化的情况下,要保证用一年的时间序列观测值求得的线性速率偏差在10％内,例如当噪声水平为9 mm时,则连续缺数应不超过11天,离散缺数应不超过12天.因此在用GNSS连续站高程时间序列研究地壳垂直运动时,应对GNSS连续站的运行状况进行严格的筛选.
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