TY - JOUR A1 - Adolfs, Marjolijn A1 - Hoque, Mohammed Mainul A1 - Shprits, Yuri Y. T1 - Storm-time relative total electron content modelling using machine learning techniques JF - Remote sensing N2 - Accurately predicting total electron content (TEC) during geomagnetic storms is still a challenging task for ionospheric models. In this work, a neural-network (NN)-based model is proposed which predicts relative TEC with respect to the preceding 27-day median TEC, during storm time for the European region (with longitudes 30 degrees W-50 degrees E and latitudes 32.5 degrees N-70 degrees N). The 27-day median TEC (referred to as median TEC), latitude, longitude, universal time, storm time, solar radio flux index F10.7, global storm index SYM-H and geomagnetic activity index Hp30 are used as inputs and the output of the network is the relative TEC. The relative TEC can be converted to the actual TEC knowing the median TEC. The median TEC is calculated at each grid point over the European region considering data from the last 27 days before the storm using global ionosphere maps (GIMs) from international GNSS service (IGS) sources. A storm event is defined when the storm time disturbance index Dst drops below 50 nanotesla. The model was trained with storm-time relative TEC data from the time period of 1998 until 2019 (2015 is excluded) and contains 365 storms. Unseen storm data from 33 storm events during 2015 and 2020 were used to test the model. The UQRG GIMs were used because of their high temporal resolution (15 min) compared to other products from different analysis centers. The NN-based model predictions show the seasonal behavior of the storms including positive and negative storm phases during winter and summer, respectively, and show a mixture of both phases during equinoxes. The model's performance was also compared with the Neustrelitz TEC model (NTCM) and the NN-based quiet-time TEC model, both developed at the German Aerospace Agency (DLR). The storm model has a root mean squared error (RMSE) of 3.38 TEC units (TECU), which is an improvement by 1.87 TECU compared to the NTCM, where an RMSE of 5.25 TECU was found. This improvement corresponds to a performance increase by 35.6%. The storm-time model outperforms the quiet-time model by 1.34 TECU, which corresponds to a performance increase by 28.4% from 4.72 to 3.38 TECU. The quiet-time model was trained with Carrington averaged TEC and, therefore, is ideal to be used as an input instead of the GIM derived 27-day median. We found an improvement by 0.8 TECU which corresponds to a performance increase by 17% from 4.72 to 3.92 TECU for the storm-time model using the quiet-time-model predicted TEC as an input compared to solely using the quiet-time model. KW - ionosphere KW - relative total electron content KW - geomagnetic storms KW - neural KW - networks KW - NTCM KW - European storm-time model Y1 - 2022 U6 - https://doi.org/10.3390/rs14236155 SN - 2072-4292 VL - 14 IS - 23 PB - MDPI CY - Basel ER - TY - JOUR A1 - Pedatella, Nick M. A1 - Fang, T. -W. A1 - Jin, Hao A1 - Sassi, F. A1 - Schmidt, H. A1 - Chau, Jorge Luis A1 - Siddiqui, Tarique Adnan A1 - Goncharenko, L. T1 - Multimodel comparison of the ionosphere variability during the 2009 sudden stratosphere warming JF - Journal of geophysical research : Space physics N2 - A comparison of different model simulations of the ionosphere variability during the 2009 sudden stratosphere warming (SSW) is presented. The focus is on the equatorial and low-latitude ionosphere simulated by the Ground-to-topside model of the Atmosphere and Ionosphere for Aeronomy (GAIA), Whole Atmosphere Model plus Global Ionosphere Plasmasphere (WAM+GIP), and Whole Atmosphere Community Climate Model eXtended version plus Thermosphere-Ionosphere-Mesosphere-Electrodynamics General Circulation Model (WACCMX+TIMEGCM). The simulations are compared with observations of the equatorial vertical plasma drift in the American and Indian longitude sectors, zonal mean Fregion peak density (NmF2) from the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) satellites, and ground-based Global Positioning System (GPS) total electron content (TEC) at 75 degrees W. The model simulations all reproduce the observed morning enhancement and afternoon decrease in the vertical plasma drift, as well as the progression of the anomalies toward later local times over the course of several days. However, notable discrepancies among the simulations are seen in terms of the magnitude of the drift perturbations, and rate of the local time shift. Comparison of the electron densities further reveals that although many of the broad features of the ionosphere variability are captured by the simulations, there are significant differences among the different model simulations, as well as between the simulations and observations. Additional simulations are performed where the neutral atmospheres from four different whole atmosphere models (GAIA, HAMMONIA (Hamburg Model of the Neutral and Ionized Atmosphere), WAM, and WACCMX) provide the lower atmospheric forcing in the TIME-GCM. These simulations demonstrate that different neutral atmospheres, in particular, differences in the solar migrating semidiurnal tide, are partly responsible for the differences in the simulated ionosphere variability in GAIA, WAM+GIP, and WACCMX+TIMEGCM. KW - ionosphere KW - sudden stratosphere warming Y1 - 2016 U6 - https://doi.org/10.1002/2016JA022859 SN - 2169-9380 SN - 2169-9402 VL - 121 SP - 7204 EP - 7225 PB - American Geophysical Union CY - Washington ER - TY - JOUR A1 - Zolotov, Oleg V. A1 - Namgaladze, Alexander A. A1 - Prokhorov, Boris E. T1 - Specific features of ionospheric total electron content variations in the periods of preparation of the earthquakes on March 11, 2011 (Japan) and October 23, 2011 (Turkey) JF - Russian journal of physical chemistry : B, Focus on physics N2 - The main morphological features of variations of the total electron content (TEC) of the ionosphere before the earthquakes on March 11, 2011 (Japan) and October 23, 2011 (Turkey) are examined. The revealed features are compared to those of ionospheric TEC disturbances observed prior to several other large seismic events, as well as to those included in a list of the most frequently observed ionospheric TEC disturbances interpreted as possible ionospheric precursors of earthquakes. It is shown that, in the periods of preparation of the earthquakes under consideration, on March 8-11 and October 20-23, abnormal ionospheric TEC disturbances were observed as long-lived structures in a near-epicentral region and in the region magnetically conjugated to it. KW - total electron content KW - earthquakes KW - ionospheric precursors of earthquakes KW - ionosphere Y1 - 2013 U6 - https://doi.org/10.1134/S1990793113050266 SN - 1990-7931 SN - 1990-7923 VL - 7 IS - 5 SP - 599 EP - 605 PB - Pleiades Publ. CY - New York ER -