Abstract

To facilitate F2-layer peak density (NmF2) modeling, a nonlinear polynomial model approach based on global NmF2 observational data from ionospheric radio occultation (IRO) measurements onboard the CHAMP, GRACE, and COSMIC satellites, is presented in this paper. We divided the globe into 63 slices from 80°S to 80°N according to geomagnetic latitude. A Nonlinear Polynomial Peak Density Model (NPPDM) was constructed by a multivariable least squares fitting to NmF2 measurements in each latitude slice and the dependencies of NmF2 on solar activity, geographical longitude, universal time, and day of year were described. The model was designed for quiet and moderate geomagnetic conditions (Ap ≤ 32). Using independent radio occultation data, quantitative analysis was made. The correlation coefficients between NPPDM predictions and IRO data were 0.91 in 2002 and 0.82 in 2005. The results show that NPPDM performs better than IRI2016 and Neustrelitz Peak Density Model (NPDM) under low solar activity, while it undergoes performance degradation under high solar activity. Using data from twelve ionosonde stations, the accuracy of NPPDM was found to be better than that of NPDM and comparable to that of IRI2016. Additionally, NPPDM can well simulate the variations and distributions of NmF2 and describe some ionospheric features, including the equatorial ionization anomaly, the mid-latitude trough, and the wavenumber-four longitudinal structure.

Highlights

  • The ionosphere is composed of several layers, namely the D-layer (50–90 km), E-layer (90–140 km), F1-layer (140–210 km), and F2-layer (210–1000 km) [1]

  • The results show that Nonlinear Polynomial Peak Density Model (NPPDM) performs better than IRI2016 and Neustrelitz Peak Density Model (NPDM) under low solar activity, while it undergoes performance degradation under high solar activity

  • As mentioned by Sai and Tulasi [24], Artificial Neural Network-Based Ionospheric Model (ANNIM) underestimated the NmF2 at high latitudes and the mean relative deviation increased to ~20–25% when compared with ionospheric radio occultation (IRO) data

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Summary

Introduction

The ionosphere is composed of several layers, namely the D-layer (50–90 km), E-layer (90–140 km), F1-layer (140–210 km), and F2-layer (210–1000 km) [1]. Tulasi et al [25] presented an improved version of ANNIM by including CHAMP (Challenging Minisatellite Payload) and GRACE (Gravity Recovery And Climate Experiment) IRO measurements as well as ionosonde data as data sources They assessed the performance of the ANNIM by comparing it with the IRI-2016 model, and showed that the ANNIM well reproduced the spatial and temporal variations of NmF2 and captured the ionospheric anomalies such as the equatorial ionosphere anomaly (EIA) and the mid-latitude summer night-time anomaly (MSNA). Hoque and Jakowski [27] made an inspiring and successful attempt to develop a nonlinear model (Neustrelitz Peak Density Model—NPDM) with only 13 coefficients and a few empirically fixed parameters, which described the dependencies of NmF2 on local time, geographic/geomagnetic location, and solar irradiance and activity.

Model Performance
Modeling Results
Independent test with occultation data
Ionospheric Features Simulation
Discussion
Conclusions
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