A sequential calibration approach based on the ensemble Kalman filter (C-EnKF) for forecasting total electron content (TEC)

Abstract

Ionospheric models are applied for computing the Total Electron Content (TEC) in ionosphere to reduce its effects on the Global Navigation Satellite System (GNSS)-based Standard Point Positioning (SPP) applications. However, the accuracy of these models is limited due to the simplified model structures and their dependency on the calibration period. In this study, we present a sequential Calibration approach based on the Ensemble Kalman Filter (C-EnKF) to improve TEC estimations. Its advantage, over the frequently implemented state-of-the-art, is that a short period of GNSS network measurements is needed to calibrate model parameters. To demonstrate the results, the International Reference Ionosphere (IRI)-2016 model is used as reference and the Vertical TEC (VTEC) estimates from 53 IGS stations in Europe are applied as observation. The C-EnKF is applied to calibrate four selected model parameters (i.e., IG 12 , U RSI(771), U RSI(1327) and U RSI(1752) related to the ionospheric activity as well as height and density peak-modelling in the F2 layer), which are identified by performing a sensitivity analysis. The calibrated model, called 'C-EnKF-IRI', is localized within Europe and can be used for near-real time TEC estimations and forecasting of the next day (at least). Validation against the dual frequency GNSS measurements of three

Ionospheric response modeling under eclipse conditions: Evaluation of 14 December 2020, total solar eclipse prediction over the South American sector

“…Moreover, the study of Prietrella et al ( 2016) used the RMSE metric to compare the performance of different ionospheric models in this same eclipse. Additionally, both the RMSE and NRMSE metrics are commonly used to evaluate ionospheric model studies (e.g., Araujo-Pradere & Fuller-Rowell, 2002;Matsuo & Araujo-Pradere, 2011;Pignalberi et al, 2021;Kosary et al, 2022).…”

Assimilating Space‐Based Thermospheric Neutral Density (TND) Data Into the TIE‐GCM Coupled Model During Periods With Low and High Solar Activity

“…Also, the advantage of this DA implementation for the near-real time orbit prediction and point positioning applications can be further explored. To improve the forecasting ability, it will be helpful to conduct further research on the calibration of external forcing (Forootan et al, 2020(Forootan et al, , 2022Kosary et al, 2022) using multi-sensor measurements.…”

“…Previous studies have shown that the now-casting and forecasting capabilities of current empirical and physicsbased models are limited (i.e., one can find periods with over and underestimations) due to the lack of information about external drivers, simplified model structures, and the sensitivity to the calibration period (Forootan et al, 2020(Forootan et al, , 2022Kosary et al, 2022;Miyoshi et al, 2011;Palmroth et al, 2021;Vielberg et al, 2018). Thus, further enhancements, by taking advantage of the thermosphere-ionosphere observations, can improve characterizing and predicting thermosphere-ionosphere variability as accurately and efficiently as possible.…”

Inferring Mass Loss by Measuring Contemporaneous Deformation around the Helheim Glacier, Southeastern Greenland, Using Sentinel-1 InSAR

“…where 𝑑 , 𝑑 , and 𝑑 are the three components of the displacement vector 𝑑 in the north, east, and up directions, respectively. 𝜃 is the incidence angle and 𝛼 is the In order to consider the absolute difference between the observation and model in Figure 10, we used the average of absolute percentage deviation (AAPD) [70] as follows:…”