Examining methods used in extracting long-term thermospheric density trends
Examining methods used in extracting long-term thermospheric density trends
复制标题
检查用于提取长期热层密度趋势的方法
DOI：
10.1016/j.jastp.2013.03.004
复制
发表时间：
2013
影响因子：
1.9
通讯作者：
T. Kane
中科院分区：
文献类型：
--
作者：
Tai;T. Kane
大量密度趋势研究表明，热层密度正在下降，下降趋势归因于低层大气中人为气体排放的增加。由于太阳变化的幅度要大得多，而且相对而言，其时间尺度与可用数据的时间范围（约 40 年）相当，因此提取趋势并非易事。构建模型来研究这些方法是否能有效消除或最小化趋势分析中的太阳影响。太阳周期的相位和幅度以及可用数据的时间范围是我们模型中考虑的三个参数。研究发现，由于太阳影响而引起的密度变化幅度对趋势结果的影响最大，当三个参数的不确定性或变化较小时，差值法和比率法可以推断出可靠的趋势。我们的误差传播分析表明，比率方法产生的传播误差比差异方法更小。我们应用这两种方法来推断卫星阻力测量得出的质量密度的趋势，发现它们产生了相同的趋势。然而，值得注意的是，当排除前 100 个数据点时，差异法比比率法产生更大的趋势。这说明趋势分析的数据选择也会影响趋势结果。
Numerous density trend studies have shown that thermospheric density is decreasing, and the declining trends were attributed to the increase of anthropogenic gas emissions in the lower atmosphere. Since solar variations are of much larger magnitude and on a time scale that is, relatively speaking, comparable to the time range of available data (~40 years), extracting the trends is not trivial. A model is constructed to investigate if the methods were effective to remove or minimize solar influences in their trend analysis. The phase and amplitude of the solar cycle and the time range of available data are the three parameters considered in our model. It is found that the amplitude of density variation due to solar influence affects the trend results the most and that the Difference and the Ratio method can deduce a reliable trend when there is small uncertainty or variation in the three parameters. Our error propagation analysis shows that the Ratio method would yield smaller propagated errors than the Difference method. We applied the two methods to deduce the trend in the mass densities derived from satellite drag measurements and found that they yielded the same trend. However, it is noted that the Difference method yielded a larger trend than the Ratio method when the first 100 data points were excluded. This indicates that data selection for trend analysis also affects the trend result.