A neural network-based ionospheric model for Arecibo

The Arecibo Observatory (18°N, 66°W) has the world’s largest single dish antenna (300 m diameter). Beyond radio astronomy it can also operate as an incoherent scatter radar and in that mode its figure-of-merit makes it also one of the most powerful world-wide. For the present purpose all electron density data available on the web, from the beginning with the first erratic measurements in 1966 up to 2004 inclusive, were downloaded. The measurements range from about 100 km to beyond 700 km and are essentially evenly distributed, i.e. not dedicated to measure specific geophysical events. From manually edited/inspected data a neural network (NN) was established with season, hour of the day, solar activity and Kp as the input parameters. The performance of this model is checked against a – likewise NN based – global model of foF2, a measure of the maximum electron density of the ionosphere. Considering the diverse data sources and assumptions of the two models it can be concluded that they agree remarkably well.     

Time constants in the ionosphere from neural network models

Neural network (NN) models for the low latitude and the polar ionosphere from the D- to the F-region were developed which are based on incoherent scatter radar data from Arecibo and EISCAT Svalbard, respectively. The various geophysical input parameters defining the NN are not only the ones that represent the time one wants to predict, but also the geophysical conditions prior to the time of the prediction. The optimum length of these preceding periods are derived for the two models are different, but a period of 60 days is a compromise acceptable for both latitudes. Furthermore from the Arecibo data time constants of electron density decay after sundown are derived which – arguably – are also relevant elsewhere, including the polar latitudes. Whereas at all altitudes the electron densities decay exponentially after sundown, below 300 km there is an additional variation with solar zenith angle.