Recent approaches to modeling ionospheric weather Original Research Article

The ionosphere is a complex and dynamic medium that exhibits weather features at all latitudes and longitudes. The weather features are driven by internal processes as well as by interplanetary and magnetospheric phenomena. Because ionospheric weather can have a detrimental effect on both ground-based and space-based systems, a large international effort has been devoted to developing both specification and forecast models, with the aim of predicting the occurrence, duration, and intensity of weather features. Currently, the most promising ionospheric weather models are the physics-based data-driven models that use Kalman filter data assimilation techniques. The data assimilation is used both to obtain the inputs needed by the physics-based models and to adjust the model outputs, i.e., the calculated electron density distribution. The data sources used in the physics-based assimilation models include ground-based GPS/TEC data, bottomside electron density profiles obtained from digisondes, in situ DMSP satellite measurements, satellite-to-satellite occultation data, TECs obtained from satellites with radio beacons, and UV data obtained via remote sensing. The status of the modeling will be reviewed.