Research Article

Statistical models of the variability of plasma in the topside ionosphere: 2. Performance assessment

¹ Istituto Nazionale di Geofisica e Vulcanologia, Via di Vigna Murata 605, 00143 Rome, Italy
² SpacEarth Technology, Via di Vigna Murata 605, 00143 Rome, Italy
³ Department of Physics, University of Oslo, Sem Sæland svei 24, 0371 Oslo, Norway
⁴ Institute of Atmospheric Physics of the Czech Academy of Sciences, Boční II 1401, 141 00 Prague, Czech Republic
⁵ Space Environment and Radio Engineering (SERENE) Group, University of Birmingham, B15 2TT Birmingham, UK
⁶ Department of Physics & Mathematics, Nottingham Trent University, NG7 2RD Nottingham, UK
⁷ Università Degli Studi Dell’Aquila, Via Vetoio 1, 67100 L’Aquila, Italy
⁸ GMV Innovating Solutions, ul Hrubieszowska 2, 01-209 Warsaw, Poland

^* Corresponding author: luca.spogli@ingv.it

Received: 2 May 2023
Accepted: 10 January 2024

Abstract

Statistical models of the variability of plasma in the topside ionosphere based on the Swarm data have been developed in the “Swarm Variability of Ionospheric Plasma” (Swarm-VIP) project within the European Space Agency’s Swarm+4D-Ionosphere framework. The models can predict the electron density, its gradients for three horizontal spatial scales – 20, 50 and 100 km – along the North-South direction and the level of the density fluctuations. Despite being developed by leveraging on Swarm data, the models provide predictions that are independent of these data, having a global coverage, fed by various parameters and proxies of the helio-geophysical conditions. Those features make the Swarm-VIP models useful for various purposes, which include the possible support for already available ionospheric models and proxy of the effect of ionospheric irregularities of the medium scales that affect the signals emitted by Global Navigation Satellite Systems (GNSS). The formulation, optimisation and validation of the Swarm-VIP models are reported in Paper 1 (Wood et al. 2024. J Space Weather Space Clim. in press). This paper describes the performance assessment of the models, by addressing their capability to reproduce the known climatological variability of the modelled quantities, and the ionospheric weather as depicted by ground-based GNSS, as a proxy for the ionospheric effect on GNSS signals. Additionally, we demonstrate that, under certain conditions, the model can better reproduce the ionospheric variability than a physics-based model, namely the Thermosphere-Ionosphere-Electrodynamics General Circulation Model (TIE-GCM).