Thermospheric Weather as Observed by Ground‐Based FPIs and Modeled by GITM
Thermospheric Weather as Observed by Ground‐Based FPIs and Modeled by GITM
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DOI：
10.1029/2018ja026032
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发表时间：
2019-02
期刊：
影响因子：
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通讯作者：
B. Harding;A. Ridley;J. Makela
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文献类型：
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作者：
B. Harding;A. Ridley;J. Makela
第一次在第一原理模式和数据之间提供了热层风逐日变化(即天气)的长期比较。这里采用的天气定义是同一UT的每日观测值和长期平均值之间的差异。全球电离层热层模型长达一年的运行是根据由中低纬度的六个法布里-珀罗涉仪汇编的夜间中性风数据集进行评估的。首先，对背景气候上方平静时间波动的时间持续性进行了评估，并发现解相关时间(自相关函数下降到e−1的时间滞后)在数据(1.8hr)和模型(1.9hr)之间有很好的一致性。接下来，进行站点之间的比较以确定去相关距离(互相关降到e−1的距离)。需要更大的法布里-珀罗涉仪网络来最终确定去相关距离，但当前的数据集表明它是∼1,000公里。模型中的去相关距离较大，表明模型结果包含的空间结构太少。测量的去相关时间和距离对调整同化模式是有用的，并且明显短于如果潮汐强迫对变化负责的预期尺度，这表明其他来源正在主导天气。最后，模型与数据的相关性很差(−0.07；ρ<；0.36)，模型低估了65%的天气强度。
The first long‐term comparison of day‐to‐day variability (i.e., weather) in the thermospheric winds between a first‐principles model and data is presented. The definition of weather adopted here is the difference between daily observations and long‐term averages at the same UT. A year‐long run of the Global Ionosphere Thermosphere Model is evaluated against a nighttime neutral wind data set compiled from six Fabry‐Perot interferometers at middle and low latitudes. First, the temporal persistence of quiet‐time fluctuations above the background climate is evaluated, and the decorrelation time (the time lag at which the autocorrelation function drops to e−1) is found to be in good agreement between the data (1.8 hr) and the model (1.9 hr). Next, comparisons between sites are made to determine the decorrelation distance (the distance at which the cross‐correlation drops to e−1). Larger Fabry‐Perot interferometer networks are needed to conclusively determine the decorrelation distance, but the current data set suggests that it is ∼1,000 km. In the model the decorrelation distance is much larger, indicating that the model results contain too little spatial structure. The measured decorrelation time and distance are useful to tune assimilative models and are notably shorter than the scales expected if tidal forcing were responsible for the variability, suggesting that some other source is dominating the weather. Finally, the model‐data correlation is poor (−0.07 < ρ < 0.36), and the magnitude of the weather is underestimated in the model by 65%.