Effects of Forcing Uncertainties on the Thermospheric and Ionospheric Ensemble-based data assimilation.

Ensemble Data assimilation is a state-of-the-art method that can combine the observation information into a numerical model and further improve the performance of numerical weather prediction of the thermosphere and ionosphere.  The thermospheric and ionospheric weather are highly sensitive to the variation of forcings from above, including the solar irradiance and the magnetospheric forcings, and forcings from below, including waves and tides from the lower atmosphere. However, these forcings are hard to quantify. Building up the connection between the uncertainty of the forcings and the variability of a numerical model of the thermosphere and ionosphere that is used in a data assimilation system is a critical issue in thermospheric and ionospheric weather prediction.

This study aims to advance our understanding of how solar irradiance variability and tide and wave variability drive the variability of Earth's thermosphere and ionosphere and improve our capability to represent this driver-response relationship in physics-based models using ensemble data assimilation. This study focuses on the National Center for Atmospheric Research’s (NCAR’s) Whole Atmosphere Community Climate Model – eXtended (WACCM-X). In the WACCM-X, the solar irradiance is determined by an empirical model, such as EUVAC, or by a real data set, and the waves and tides are generated self-consistently from the lower atmosphere in the model.

We first try to quantify the solar irradiance variability in different wavelengths based on real data, including data from the Extreme Ultraviolet Variability Experiment (EVE) on Solar Dynamics Observatory (SDO) and the X-ray Photometer System (XPS)and the Solar Stellar Irradiance Comparison Experiment (SOLSTICE) on Solar Radiation and Climate Experiment (SORCE). Then, we quantify and qualify the response of the thermosphere and ionosphere in the WACCM-X to both the variation of solar irradiance and waves and tides by launching a set of ensemble simulation experiences. This will help prove the predictability of the thermospheric and ionospheric weather.