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Abstract An ionogram is a graph of the time that a vertically transmitted wave takes to return to the earth as a function of frequency. Time is typically represented as virtual height, which is the time divided by the speed of light. The ionogram is shaped by making a trace of this height against the frequency of the transmitted wave. Along with the echoes of the ionosphere, ionograms usually contain a large amount of noise and interference of different nature that must be removed in order to extract useful information. In the present work, we propose a method based on convolutional neural networks to extract ionospheric echoes from digital ionograms. Extraction using the CNN model is compared with extraction using machine learning techniques. From the extracted traces, ionospheric parameters can be determined and electron density profile can be derived. 

Abstract Results from a dynamo electric field model are presented to examine the consistency of the widely used empirical models of low‐latitude plasma drifts and thermospheric neutral winds. The sector defined by the Jicamarca Radar measured plasma drifts is used due to the greater certainty of the empirical vertical plasma drifts. The plasma drifts produced by the Horizontal Wind Model (HWM) in a coupled ionosphere‐electric field model for geomagnetically quiet and moderate solar conditions are compared against empirical models of equatorial plasma drifts for the Peruvian sector. The HWM generates reasonable sunset prereversal enhancement of the vertical drift in all but May, June, July, and August when no prereversal enhancement exists in the empirical results. The daytime vertical drifts are deficient during all seasons. A solar diurnal and semi‐diurnal tidal forcing are required in the E region (100–150 km) to bring the HWM into better agreement as a dynamo driver for the daytime electric fields associated with the broad Solar Quiet current system. 

Abstract In this research, we present data‐driven forecasting of ionospheric total electron content (TEC) using the Long‐Short Term Memory (LSTM) deep recurrent neural network method. The random forest machine learning method was used to perform a regression analysis and estimate the variable importance of the input parameters. The input data are obtained from satellite and ground based measurements characterizing the solar‐terrestrial environment. We estimate the relative importance of 34 different parameters, including the solar flux, solar wind density, and speed the three components of interplanetary magnetic field, Lyman‐alpha, the Kp, Dst, and Polar Cap (PC) indices. The TEC measurements are taken with 15‐s cadence from an equatorial GPS station located at Bogota, Columbia (4.7110° N, 74.0721° W). The 2008–2017 data set, including the top five parameters estimated using the random forest, is used for training the machine learning models, and the 2018 data set is used for independent testing of the LSTM forecasting. The LSTM method as applied to forecast the TEC up to 5 h ahead, with 30‐min cadence. The results indicate that very good forecasts with low root mean square (RMS) error (high correlation) can be made in the near future and the RMS errors increase as we forecast further into the future. The data sources are satellite and ground based measurements characterizing the solar‐terrestrial environment.