The ionosphere is still considered one of the most significant error sources in precise Global Navigation Satellite Systems (GNSS) positioning. On the other hand, new satellite signals and data processing methods allow for a continuous increase in the accuracy of the available ionosphere models derived from GNSS observables. Therefore, many research groups around the world are conducting resear...

This paper presents the development of an Artificial Neural Network electron density profiler based on electron density profiles collected from radio occultation (RO) measurements from LEO (Low Earth Orbit) satellites to improve the spatial and temporal modeling of ionospheric electron density over Europe. The significance in the accurate determination of the electron density profile lies on th...

Total electron content (TEC) data, obtained from GPS satellites can be used as a tool for extracting earthquake precursor. In this paper, a pattern matching technique is used for monitoring TEC variation during and prior to the earthquake. For the experiment two major earthquake events i.e. Bhutan earthquake on 21st September 2009 and Nepal earthquake on 25th April 2015 are considered for analy...

Tsunamis have been a significant threat to humans living in coastal regions throughout recorded history. The recent tsunami of September 29, 2009 was a tragic reminder of this fact, as the waves caused over 180 deaths on the islands of Samoa, American Samoa, and Apia in the south Pacific. Recent modeling results and observations have demonstrated that the ionospheric signature of an ocean tsuna...

In this paper, we proposed an innovative encoder-decoder structure with a convolution long short-term memory (ED-ConvLSTM) network to forecast global total electron content (TEC) based on the International GNSS Service (IGS) TEC maps from 2005 2018 1-hr time cadence. The ED-ConvLSTM model is used 1–7 days in advance through iterations. To investigate model's performance, compared Reference Iono...