Effects of inferring unobserved thermospheric and ionospheric state variables by using an Ensemble Kalman Filter on global ionospheric specification and forecasting

Abstract

This paper demonstrates the significance of ion-neutral coupling to ionospheric data assimilation for ionospheric specification and forecast. Ensemble Kalman Filter (EnKF) is used to assimilate synthetic electron density profiles sampled according to the Formosa Satellite 3/Constellation Observing System for Meteorology, Ionosphere, and Climate into the Thermosphere-Ionosphere-Electrodynamics General Circulation Model (TIEGCM). The combination of the EnKF and first-principles TIEGCM allows a self-consistent treatment of thermosphere and ionosphere coupling in the data assimilation and forecast. Because thermospheric variables affect ionospheric electron densities, different combinations of an observed ionospheric state variable (electron density), and unobserved ionospheric and thermospheric state variables (atomic oxygen ion density, neutral temperature, winds, and composition) are included as part of the EnKF state vector in experiments. In the EnKF, the unobserved state variables are estimated and made dynamically and chemically consistent with the observed state variable, thus improving the performance of the data assimilation system. The impact on ensemble forecast is further examined by initializing the TIEGCM with the assimilation analysis. The main findings are the following: (1) by incorporating ion-neutral coupling into the EnKF, the ionospheric electron density analysis, and forecast can be considerably improved. (2) Thermospheric composition is the most significant state variable that affects ionospheric analysis and forecast. (3) Thermospheric variables have a much longer impact on ionospheric forecast (>24 h) than ionospheric variables (2 to 3 h). (4) In the TIEGCM, the effect of assimilating electron densities is not completely transmitted to the forecast step unless the densities of ion species are estimated.

EnKF Ionosphere and Thermosphere Data Assimilation Algorithm Through a Sparse Matrix Method

“…Figure 2 shows the overall global mean RMSE of electron density in comparison with the simulated truth for each case. It implies that the effect of updating neutral winds is insignificant for ionosphere forecast on a global scale, which was also concluded by Hsu et al (2014). In the OSSEs 1-7, in which the observed data were assimilated at the beginning, the RMSEs are much lower than that in the control experiment.…”

“…However, the majority of previous studies were conducted on supercomputers, such as NCAR Yellowstone (Chartier et al, 2016;Hsu et al, 2014;Lee et al, 2012;Matsuo et al, 2013) or Cheyenne supercomputer (Pedatella et al, 2018). However, the majority of previous studies were conducted on supercomputers, such as NCAR Yellowstone (Chartier et al, 2016;Hsu et al, 2014;Lee et al, 2012;Matsuo et al, 2013) or Cheyenne supercomputer (Pedatella et al, 2018).…”

Ionospheric data assimilation with thermosphere‐ionosphere‐electrodynamics general circulation model and GPS‐TEC during geomagnetic storm conditions

“…[] and Hsu et al . [] have examined the impact of initializing the thermospheric states on long‐term ionospheric forecasting. Their results suggest that a longer electron density forecasting performance can be achieved by accurately initializing thermospheric state variables, especially the neutral composition, in a coupled thermosphere‐ionosphere model.…”

The fast development of solar terrestrial sciences in Taiwan

“…Liu et al (2016) surveyed the worst-case GPS scintillations on the ground estimated from radio occultation observations of F3/C during 2007-2014 and are ready to develop an empirical model for the ionospheric S4 scintillation. Currently, the nowcast and forecast data assimilation models with the neutral atmosphere have been developed (Lee et al 2012c;Hsu et al 2014;Sun et al 2015).…”