Applying artificial neural network to the short-term prediction of electron density structure using GPS occultation data

Details Applying artificial neural network to the short-term prediction of electron density structure using GPS occultation data Applying artificial neural network to the short-term prediction of electron density structure using GPS occultation data

May 2002

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2002georl..29i..35z&link_type=abstract

Geophysical Research Letters, Volume 29, Issue 9, pp. 35-1, CiteID 1321, DOI 10.1029/2001GL013656

Computer Science

Performance

4

Meteorology And Atmospheric Dynamics: Numerical Modeling And Data Assimilation, Radio Science: Radio Wave Propagation, Radio Science: Ionospheric Propagation (2487), Radio Science: Instruments And Techniques

Scientific paper

Artificial neural network (ANN) is used for assimilating of GPS ionospheric occulted data in order to take full advantage of the abundant GPS occulted data. A feedforward, full-connected network is chosen based on the back-propagation algorithm. Universal time, latitude, longitude, height, Kp index, and F10.7 solar flux are chosen as the input vectors of the network while the electron density as the output vectors. The GPS occultation data on May 24th, 1996 were taken as training samples to train an ANN, and then the well-trained ANN was used to predict the electron density on 25th. Comparison of the predicted results and observed data demonstrated that ANN is a promising method in assimilating the GPS occulted data to establish the ionospheric weather prediction model. Furthermore, the accurate and abundant observations are essential for ensuring the good performance of ANN.

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