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Abstract

The dynamics and morphology of the thermosphere and ionosphere is strongly controlled by thermospheric neutral winds. However, direct observations of thermospheric winds are historically limited both temporally and spatially. For example, interferometric methods for measuring thermospheric winds are restricted to nighttime observations and cloudless conditions and are limited to relatively few locations. Space-based observations made by cross-track measurements from accelerometers as well as onboard interferometers and spectrometers provide valuable information of thermospheric winds, but coverage is sparse over a given location for all local times. Ionospheric observations, on the hand, are abundant and embed information about the underlying thermosphere. Data assimilation is a technique that combines information from observations with a physical model. Observed data are assimilated into the model as a constraint for the physical equations that describe the dynamics of the system, which allows estimates of unobserved driving forces, e.g., the thermospheric neutral wind. The Global Assimilation of Ionospheric Measurements Full Physics (GAIM-FP) model can assimilate a multitude of ionospheric observations to estimate magnetic meridional winds at low- and mid-latitudes. The Thermospheric Wind Assimilation Model (TWAM) combines these magnetic meridional wind estimates with the equation of motion of the neutral gas using a Kalman filter technique to provide estimates of the thermospheric wind components. We will present the month-to-month progression of the TWAM thermospheric wind estimates for an entire year and compare our estimates with those obtained from the climatological Horizontal Wind Model (HWM14).