An Artificial Neural Network‐Based Ionospheric Model to Predict N m F 2 and h m F 2 Using Long‐Term Data Set of FORMOSAT‐3/COSMIC Radio Occultation Observations: Preliminary Results

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 N m F 2 and h m F 2 . 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 N m F 2 ( h m F 2 ) values with RMS errors of 1.87 × 10 5  el/cm 3 (27.9 km) with respect to actual F3/C; and 2.98 × 10 5  el/cm 3 (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 N m F 2 and 10–15% for h m F 2 compared to actual F3/C data, except the postsunset enhancement in h m F 2 . 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.