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Title: An Artificial Neural Network-Based Ionospheric Model to Predict NmF2 and hmF2 Using Long-Term Data Set of FORMOSAT-3/COSMIC Radio Occultation Observations: Preliminary Results
Authors: Gowtam, V. Sai
Tulasi Ram, S.
Keywords: Artificial Neural Networks
Ionospheric NmF2
Ionospheric hmF2
Issue Date: 2017
Citation: JGR, 122, 11,743–11,755, doi: 10.1002/2017JA024795
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.
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