Thermospheric Weather as Observed by Ground-Based FPIs and Modeled by GITM

Abstract

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%.