A neural network-based ionospheric model for Arecibo

Details A neural network-based ionospheric model for Arecibo A neural network-based ionospheric model for Arecibo

Aug 2008

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2008adspr..42..776f&link_type=abstract

Advances in Space Research, Volume 42, Issue 4, p. 776-781.

Computer Science

Performance

Scientific paper

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.

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