Maintenance Notice

On Friday, 03/01/2024, the main CCMC website and all CCMC applications will be intermittently unavailable between 7 AM to 11 AM Eastern Time.  The main CCMC website and all CCMC services/applications (e.g., iSWA, Runs-On-Request (ROR), Instant-Run, online visualization services, all Scoreboards, DONKI, CAMEL, StereoCAT, etc.) will be impacted.

Please refrain from submitting run requests via ROR during the maintenance window.

Last Updated: 03/01/2024


Version: 1.0

ORIENT (Outer RadIation belt Electron Neural neT model) is a set of machine learning model of Earth’s radiation belt electron flux. The ORIENT estimates the spin-average electron flux at different energy levels (50 keV ~ 2 MeV) at the L shell ranging from 2.6 Re to 6.6 Re(outer radiation belt) and at different MLT and MLAT. The model is trained using data from MagEIS and REPT instrument onboard the Van Allen Probes. The model driver is the time history of solarwind condition (velocity: Vsw, dynamic pressure: Psw), geomagnetic indices (AL or AE, SYM-H) and location (L, MLT,MLAT). The time history is ranging from 3 days to 20 days which depends on the target electron energy level. The model is trained using TensorFlow( but can also be converted into different machine learning framework using ONNX( The model has been extensively tested and validated showing very high accuracy performance for out-of-sample data (R^2 ∼ 0.78–0.92). Importantly, the ORIENT model successfully captures electron dynamics over long- and short timescales for a range of different energies.


The real-time of geomagnetic indices might be inaccurate and not accessible. e.g., Real time AE & AL might be not accessible.


Time average of solar wind indices (Flow speed and Pressure), geomagnetic indices(AL or AE, SYM-H), location (MLT,MLAT,L)


Electron flux at specific energy

Model is time-dependent.


  • Magnetosphere / Inner Magnetosphere / RingCurrent
  • Magnetosphere / Inner Magnetosphere / RadiationBelt

Space Weather Impacts

  • Near-earth radiation and plasma environment (aerospace assets functionality)



Code Languages: python


Publication Policy

UCLA Atmospheric & Oceanic Science, Bortnik’s Group

In addition to any model-specific policy, please refer to the General Publication Policy.