Speaker
Description
The Earth’s ionosphere affects the propagation of signals from the Global Navigation Satellite Systems (GNSS). The part of the ionosphere above the F2-layer peak, known as the topside ionosphere, contains a major portion of the total electron content and is therefore crucial for both scientific and practical applications. One of the major challenges for modeling the topside ionosphere has been the sparse data coverage, primarily limited to in-situ observations along satellite orbits that are relatively fixed in time and do not provide comprehensive three-dimensional coverage. Over the past two decades, a large number of radio occultation (RO) electron density profiles have become available, offering a valuable data source for enhancing our modeling capabilities. In this study, we use RO, in-situ, and digisonde data, along with historical observations from topside sounder missions, to develop an updated version of the Neural Network model of Electron density in the Topside ionosphere (NET). The updated model is based on the data set spanning approximately 4 solar cycles and therefore covers a wide range of solar activity and geomagnetic conditions. We validate the model using data from several prominent ionospheric missions and demonstrate that it generalizes well to unseen observations, with approximately 90% of the predictions falling within a factor of two of the measurements. We analyze the scientific capabilities of the model during geomagnetic storms, by investigating the behavior of the topside ionosphere, particularly the storm-time enhancements of the F2-peak height (hmF2) at high latitudes and their equatorward propagation as depicted by the model. Additionally, we discuss the operational capabilities of the NET model, including its real-time operation within the European FLAG project.
| Numerical model | NET |
|---|
