Model‐Free Approach for Regional Ionospheric Multi‐Instrument Imaging.

The article proposes a straightforward Kalman filter‐based method for computationally efficient ionospheric electron density multi‐instrument imaging. The approach uses direct ionospheric measurements, such as ionosondes, and general physical assumptions to estimate the uncertainty associated with the previous reconstructed time step. Therefore the method does not require any electron density model of the ionosphere as a background. The uncertainty is represented by an inverse covariance matrix constructed with Gaussian Markov random fields, allowing the problem to be solved directly with relatively high resolution. The experiments utilize measurements from dense ground‐based GNSS and low Earth orbit beacon satellite receiver networks as well as ionosondes. A synthetic simulation verification and real data validation with a specific European Incoherent Scatter Scientific Association incoherent scatter radar measurement campaign is carried out over Northern European sector. The method can be controlled using parameters with probabilistic and physically realistic interpretations that can be applied to both simulated and real‐world data. The results show that the approach is feasible for near real‐time regional ionospheric imaging. Especially, the method can be seen as an expansion to local profile measurements field of view, but with sufficient measurement coverage, it also provides information further away from the specific instrument. Plain Language Summary: The ionosphere is a region of the atmosphere with a large number of electrically charged particles. Earth's ionosphere extends typically from 100 to 1,000 km altitude. Electron density that is, the number of free electrons per volume is a commonly used quantity to describe the structure of the ionosphere. The electron density and its variations affects for example, satellite navigation and radio broadcasting. The ionosphere can be studied locally with a high degree of accuracy by different radar measurements such as ionosondes and incoherent scatter radars. Three‐dimensional imaging of larger regions requires the use of ground‐based satellite measurements. Despite today's numerous satellite systems and extensive receiver networks, electron density imaging is very difficult and therefore methods commonly use ionospheric models as a background. Hence, the imaging result is a compromise between the model and the satellite measurements. In this study, imaging is performed using local ionosonde measurements for eliminating the need for a background model. In addition, the use of so called Gaussian Markov random fields allows efficient computation of the resulting large numerical systems. The results obtained with both simulated and real measurements show that the approach is feasible for near real‐time regional ionospheric imaging even with a modern laptop. Key Points: A Kalman filter application with Gaussian Markov random field priors enabling fast computationNo external ionospheric electron density model is used to generate the background mean or covarianceVerification with three‐dimensional simulation model, as well as validation with incoherent scatter radars and ionosonde measurements [ABSTRACT FROM AUTHOR]