Abstract Atmospheric temperature has both variations with time constants of order ½ year or longer, such as trends, annual and semiannual oscillation as well as variations with shorter time constants such as two-day waves, diurnal and semidiurnal tides, and gravity waves. Long-term amplitudes, such as trends and solar flux variations, have been determined from the time series of observed sodium lidar temperatures averaged over a night or a fixed 2-h interval by ignoring the short time-scale variations. Since during the twenty-eight years of the CSU/USU sodium lidar experiments diurnal and semi-diurnal temperature tides could be as much as 9 K and 18 K, respectively, it is particularly important to estimate their effects. We accept the Climatological Tidal Model of the Thermosphere (CTMT) and show that, for linear models, the tidal shift of trends depends upon (1) the times of observations, (2) averages of the short-time variations, and (3) the basis functions of the long-time variations, such as t, sin(2πt/1yr) and solar flux, but (4) not on observed temperatures. This allows planning experimental campaigns and analyses to reduce the tidal effects. We compare the tidal shifts of temperature trends and tidal shifts in the annual and semiannual amplitudes for CSU/USU experiments using 2-h averages (_2 MN) and nightly averages (_Ngt) using data from all times during the year and those restricted to different seasons. For example, the maximum tidal shifts in trends in temperatures, winds, and air density are predicted to occur at altitudes around 100 km. For the CSU/USU experiments the tidal shifts of temperature trends vary between 0 and 1.5 K/decade depending on altitude and annual/seasonal data used; between 85 and 100 km, they are much smaller for 2-h means (_2 MN) than for nightly means (_Ngt).

中文翻译：

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从激光雷达观测确定中层-低热层长期趋势的潮汐影响

摘要 大气温度既有趋势、年、半年振荡等时间常数在1/2 年以上的变化，也有更短时间常数的变化，例如两天波、日潮和半日潮、重力波等。长期振幅，例如趋势和太阳通量变化，是通过忽略短时间尺度变化，从一夜或固定 2 小时间隔内观察到的钠激光雷达温度的时间序列确定的。由于在 CSU/USU 钠激光雷达实验的 28 年间，昼夜和半昼温潮汐可能分别高达 9 K 和 18 K，因此估计它们的影响尤为重要。我们接受热层气候潮汐模型 (CTMT) 并表明，对于线性模型，趋势的潮汐变化取决于 (1) 观测次数，(2) 短期变化的平均值，以及 (3) 长期变化的基函数，例如 t, sin(2πt/1yr)和太阳通量，但 (4) 不是观测到的温度。这允许规划实验活动和分析以减少潮汐效应。我们使用 2 小时平均值 (_2 MN) 和夜间平均值 (_Ngt) 比较了 CSU/USU 实验的年度和半年度振幅中温度趋势的潮汐变化和使用来自一年中所有时间的数据和仅限于不同季节。例如，温度、风和空气密度趋势的最大潮汐变化预计发生在 100 公里左右的高度。对于 CSU/USU 实验，温度趋势的潮汐变化在 0 和 1 之间变化。5 K/decade，取决于使用的海拔高度和年度/季节性数据；在 85 到 100 公里之间，2 小时平均值 (_2 MN) 比夜间平均值 (_Ngt) 小得多。