What is it about?
Solar activity is the most dominant driver of space weather. In this paper, we investigate the impact of different solar activity levels on forecasting the quantities important for space weather (e.g. electron and neutral densities). The results are useful to explain the inherent model bias, to understand the limitations of the data, and to demonstrate the capability of the assimilation technique.
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Why is it important?
In this work, we assimilate globally abundant radio occultation-derived electron density into a physics-based model. RO-derived electron density is one of the most promising means to test the eﬀect of assimilation on the model forecasted state on a global scale.
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This page is a summary of: The Impact of Solar Activity on Forecasting the Upper Atmosphere via Assimilation of Electron Density Data, Space Weather, March 2021, American Geophysical Union (AGU), DOI: 10.1029/2020sw002660.
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The Impact of Solar Activity on Forecasting the Upper Atmosphere via Assimilation of Electron Density Data
We present a comprehensive comparison of the impact of solar activity on forecasting the ionosphere and thermosphere. Here we investigate the response of physics-based TIE-GCM (thermosphere-ionosphere-electrodynamics general circulation model) in a data assimilation scheme through assimilating radio occultation (RO)-derived electron density (Ne) using an ensemble Kalman filter (KF). Constellation observations of Ne profiles offer opportunities to assess the accuracy of the model forecasted state on a global scale. In this study, we emphasise the importance of understanding how the assimilation results vary with solar activity, which is one of the primary drivers of thermosphere-ionosphere dynamics. We validate the assimilation results with independent RO-derived GRACE (Gravity Recovery and Climate Experiment mission) Ne data. The main result is that the forecast Ne agree best with data during the solar minimum compared to solar maximum. The results also show that the assimilation scheme significantly adjusts both the nowcast and forecast states during the two solar activity periods. The results show that TIE-GCM significantly underestimate Ne in low altitudes below 250 km and the assimilation of Ne is not as effective in these lower altitudes compared to higher altitudes. The results demonstrate that assimilation of Ne significantly impacts the neutral mass density estimates via the KF state vector. This impact is larger during solar maximum than solar minimum relative to a control run. The results also demonstrate that the impact of assimilation of Ne on neutral mass density state persists through to forecast state better during solar minimum compared to solar maximum. The results are useful to explain the inherent model bias, to understand the limitations of the data, and to demonstrate the capability of the assimilation technique.
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