A neural network-based ionospheric model for the auroral zone |
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| Institution: | 1. Hermanus Magnetic Observatory, Hermanus, South Africa;2. Department of Physics and Electronics, Rhodes University, Grahamstown, South Africa;3. Institute of Communication Networks and Satellite Communications, Graz University of Technology, Graz, Austria;1. Space Weather Monitoring Center (SWMC), Faculty of Science, Helwan University, Egypt;2. LPP/Polytechnique/UPMC, CNRS, 4 Avenue de Neptune, 94107 Saint-Maur des fossés, France;3. ICTP, Trieste, Italy;4. Lab-STICC UMR 6285 Mines-Télécom Télécom Bretagne, CS 83818, 29288 Brest, Cedex 3, France;1. Department of Physical and Chemical Sciences, University of L’Aquila, 67100 L’Aquila, Italy;2. Istituto Nazionale di Geofisica e Vulcanologia, 00143 Rome, Italy;3. Università della Calabria, Dipartimento di Fisica, Rende, CS 87036, Italy;4. Consorzio Area di Ricerca in Astrogeofisica, University of L’Aquila, 67100 L’Aquila, Italy;5. SpacEarth Techonology, 00143 Rome, Italy;1. Centre for Atmospheric Research, National Space Research & Development Agency, Anyigba, Nigeria;2. Centre for Space Research, North-West University, South Africa;3. Sorbonne Universités, UPMC Université. Paris 06, UMR 7648, Laboratoire de Physique des Plasmas, F-75005 Paris, France;4. T/ICT4D, ICTP, Strada Costiera 11, I-34151 Trieste, Italy;1. School of Science, Xidian University, Xi''an 710071, China;2. SOA Key Laboratory for Polar Science, Polar Research Institute of China, Shanghai 200136, China;3. Institute of Space Weather, Nanjing University of Information Science and Technology, Nanjing 210044, China;1. School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, 11155-4563 Tehran, Iran;2. Department of Geodesy and Surveying Engineering, Tafresh University, 39518-79611 Tafresh, Iran;1. National-Local Joint Engineering Laboratory of Geo-Spatial Information Technology, Hunan University of Science and Technology, Xiangtan 411201, PR China;2. Xinjiang Institute of Ecology and Geography, Chinese Academy of Science, Urumqi 830011, PR China;3. Institute of Space Sciences, Shandong University, Weihai 264209, PR China;4. State Key Laboratory of Geo-information Engineering, Xi’an 710054, PR China;5. Hunan Province Mapping and Science and Technology Investigation Institute, Changsha 410007, PR China;6. Department of Surveying Engineering, School of Transportation, Southeast University, Nanjing 210096, PR China;7. Key Laboratory of Virtual Geographic Environment, Minister of Education, Nanjing Normal University, Nanjing 210046, PR China |
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| Abstract: | A new empirical model for the lower ionosphere in the auroral zone, called IMAZ, has been developed, tested and refined for use in the International Reference Ionosphere (IRI) global model. Available ionospheric data have been used to train neural networks (NNs) to predict the high latitude electron density profile. Data from the European Incoherent Scatter Radar (EISCAT), based near Tromsø, Norway (69.58°N, 19.23°E), combined with rocket-borne measurements (from 61° to 69° geomagnetic latitude) make up the database of reliable D- and E-region data.NNs were trained with different combinations of the following input parameters: day number, time of day, total absorption, local magnetic K index, planetary Ap index, 10.7 cm solar radio flux, solar zenith angle and pressure surface. The output that the NNs were trained to predict was the electron density for a given set of input parameters. The criteria for determining the optimum NN are (a) the root mean square (RMS) error between the measured and predicted output values, and (b) the ability to reproduce the absorption they are meant to represent. An optimum input space was determined and then adapted to suit the requirements of the IRI community. In addition, the true quiet electron densities were simulated and added to the database, thus allowing the final model to be valid for riometer absorptions down to 0 dB.This paper discusses the development of a NN-based model for the high-latitude, lower ionosphere, and presents results from the version developed specifically for the IRI user community. |
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