Publication: Journal of Geophysical Research, Space Physics; First HAO Author: Nick Pedatella; Authors names listed in article: N. M. Pedatella, J. L. Anderson, C. H. Chen, K. Raeder, J. Liu, H.-L. Liu, and C.H. Lin
The Earth’s upper atmosphere impacts a wide range of technologies, including satellite communication and navigation signals. Specification and forecasting of the upper atmosphere are critical for mitigating these effects. Improved specification of the state of the upper atmosphere can also improve scientific understanding of this region.
(a) Root mean square error and (b) bias in WACCMX+DART experiments compared to ground-based Global Navigation Satellite System (GNSS) total electron content observations for the 1 hr forecast (F) and analysis (A). Results are presented for three experiments: (1) no ionosphere assimilation; (2) ionosphere assimilation with no impact on the thermosphere; and (3) ionosphere assimilation with impact on the thermosphere. The results demonstrate the significant reduction in RMSE and bias when assimilating ionosphere observations.
Data assimilation, which statistically combines a background model with observations, has recently been adopted as a means to improve upper atmosphere specification and forecasting. However, data assimilation systems focused on the upper atmosphere typically have lower boundaries near 100 km, and they do not consider the variability in wave forcing that originates at lower altitudes. This represents a limitation due to the known importance of lower atmosphere forcing on the day-to-day variability of the ionosphere. The current study presents initial results of the assimilation of ionosphere observations in a whole atmosphere model that extends from the surface to ~500-700 km. The model is based on the Whole Atmosphere Community Climate Model with thermosphere-ionosphere eXtension (WACCMX) with data assimilation provided by the Data Assimilation Research Testbed (DART) ensemble Kalman filter. The results demonstrate the positive impact of assimilating ionosphere observations on both short-term forecasts and analysis fields. This represents the first demonstration of a data assimilation system capable of assimilating observations from throughout the entire atmosphere (0-500 km).