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Last Updated: 05/26/2022


Version: v3.0

UMASEP is a set of event prediction tools. Each tool predicts SEP events of a specific energy range by using two models: a well-connected and a poorly-connected prediction model. These tools are also able to predict "all-clear" situations. Each well-connected prediction model makes use of the lag-correlation of solar electromagnetic flux with the particle flux at near-earth. If the correlation is high, the model infers that there is a magnetic connection through which particles are arriving. If, additionally, the intensity of the flux of the associated solar event is also high, then the well-connected prediction model issues an SEP event prediction. Each poorly-connected prediction model checks whether the differential proton flux behavior is similar to that in the beginning phases of previous historically events and thus deduce similar consequences. When a UMASEP tool predicts an SEP event, it also launches a machine-learning-based prediction of the SEP fluence for a specific energy bin.

The UMASEP prediction tools are:

  • UMASEP-10 (Núñez, 2011; Núñez, 2022), for predicting >10 MeV SEP proton events and the SEP fluence for the energy bin 15.1 - 21.9 MeV;
  • UMASEP-30 for predicting >30 MeV SEP proton events and the SEP fluence for the energy bin 31.6 - 45.7 MeV;
  • UMASEP-50 for predicting >50 MeV SEP proton events and the SEP fluence for the energy bin 45.7 - 66.1 MeV;
  • UMASEP-100 (Núñez, 2015) for predicting >100 MeV SEP proton events and the SEP fluence for the energy bin 95.6 - 138.3 MeV;
  • HESPERIA UMASEP-500 (Núñez et al, 2017) for predicting GLE and >500 MeV proton events.

The UMASEP-10 tool has a new model that uses machine learning techniques to predict the occurrence of >10 MeV SEP events from solar data only (without analyzing proton data). Currently, this component processes flare and radio burst data from the NOAA/SWPC Event Report to make predictions of the occurrence and the peak intensity of >10 MeV SEP events. This new model works together with the aforementioned well- and poorly-connected models.


GOES soft X-ray flux, GOES differential and integral proton flux. In the case of UMASEP-10, the model also processes Solar flare and radio burst data from the NOAA/SWPC Event Report.

The temporal cadence of the analyzed input data is 5 minutes in the case of UMASEP-10 and UMASEP-100, and 1 minute in the case of HESPERIA UMASEP-500.


The five models predict SEP/GLE event occurrences. UMASEP-10, UMASEP-30, UMASEP-50 and UMASEP-100 also predict the peak SEP flux (HESPERIA UMASEP_500 does not predict the peak flux). The forecast window of these predictions depends on the type of predicted situation:

In the case the tool recognizes precursors of a well-connected SEP event, the prediction window is: 7 hours for >10 MeV protons (UMASEP-10), 6 hours for >30 MeV protons (UMASEP-30), 5 hours for >50 MeV protons (UMASEP-50), 3 hours for >100 MeV protons (UMASEP-100), 1 hour for GLE/>500 MeV events (HESPERIA UMASEP-500), and 7 hours for the 15-138 MeV SEP fluence spectrum

In the case the expected SEP event is not well-connected, the prediction window is: Up to 80 hours for >10 MeV protons (UMASEP-10), 24 hours for >30 MeV protons (UMASEP-30), 20 hours for >50 MeV protons (UMASEP-50), 16 hours for >100 MeV protons (UMASEP-100)

Model is time-dependant.


  • Heliosphere / Inner Heliosphere

Space Weather Impacts

  • Near-earth radiation and plasma environment (aerospace assets functionality)
  • Solar energetic particles - SEPs (human exploration, aviation safety, aerospace assets functionality)



Code Languages: Java


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