Abstract:
Artificial Neural Networks (ANNs) are known to be capable of solving linear as well as highly
nonlinear problems. Using the long-term and high-quality data set of Formosa Satellite-3/Constellation
Observing System for Meteorology, Ionosphere, and Climate (FORMOSAT-3/COSMIC, in short F3/C) from 2006
to 2015, an ANN-based two-dimensional (2-D) Ionospheric Model (ANNIM) is developed to predict the
ionospheric peak parameters, such as NmF2 and hmF2. In this pilot study, the ANNIM results are compared
with the original F3/C data, GRACE (Gravity Recovery and Climate Experiment) observations as well as
International Reference Ionosphere (IRI)-2016 model to assess the learning efficiency of the neural networks
used in the model. The ANNIM could well predict the NmF2 (hmF2) values with RMS errors of 1.87 × 105 el/cm3
(27.9 km) with respect to actual F3/C; and 2.98 × 105 el/cm3 (40.18 km) with respect to independent
GRACE data. Further, the ANNIM predictions found to be as good as IRI-2016 model with a slightly smaller
RMS error when compared to independent GRACE data. The ANNIM has successfully reproduced the local
time, latitude, longitude, and seasonal variations with errors ranging ~15–25% for NmF2 and 10–15% for
hmF2 compared to actual F3/C data, except the postsunset enhancement in hmF2. Further, the ANNIM has also
captured the global-scale ionospheric phenomena such as ionospheric annual anomaly, Weddell Sea
Anomaly, and the midlatitude summer nighttime anomaly. Compared to IRI-2016 model, the ANNIM is found
to have better represented the fine longitudinal structures and the midlatitude summer nighttime
enhancements in both the hemispheres.
