@proceedings{bibcite_285,
  author = {Reynaldo Rojas and Enrique Rojas and Jhassmin Aricoch{\'e} and Marco Milla},
  title = {Forecasting Spread F at Jicamarca},
  abstract = {Spread F is a phenomenon that occurs in the F layer of the Ionosphere and is characterized by plasma depletions. It can have a negative impact on radio communication systems and because of this, it is of interest to develop a model that can predict its occurrence. Radars like digisondes and JULIA (Jicamarca Unattended Long-term Investigations of the Ionosphere and Atmosphere) have observed the Ionosphere at Jicamarca for decades. The datasets that resulted from a collection of these observations joined with geophysical parameters measurements were harnessed to train a Machine Learning model that predicts Spread F. In addition, we compared our model to FIRST (Forecasting Ionospheric Real-time Scintillation Tool) and obtained promising results. Although our model has only been validated with Jicamarca{\textquoteright}s dataset it may be used for other longitudes. Furthermore, since the only local measurements used during training were Spread F occurrences and the virtual height of the F layer, the retraining process can easily be done on a single station with an ionosonde receiver.},
  year = {2022},
  journal = {HamSCI Workshop 2022},
  month = {03/2022},
  publisher = {HamSCI},
  address = {Huntsville, AL},
  language = {eng},
}