CCMC SEP Scoreboard
About
CCMC has implemented a beta version of the "SEP scoreboard". Scoreboard planning was initiated together with Mark Dierckxsens (BIRA-IASB), Mike Marsh (UK Met Office) and the international research community. In 2018, Johnson Space Center's Space Radiation Analysis Group became involved in the SEP scoreboard as part of a multi-year project called ISEP.
The SEP Scoreboard builds upon the flare scoreboard and CME scoreboard. It is an automated system such that model/method developers can have their predictions automatically uploaded to an anonymous ftp in a pre-defined JSON format, which is parsed and databased by the system.
SEP forecasts can be roughly divided into three categories: (1) Continuous/Probabilistic (2) Solar event triggered (3) Physics-based/complex. The SEP scoreboard will focus on real-time forecasts but will also coordinate with the SEP validation team to evaluate different models for a set of historical events. This is particularly useful for some physics-based models in the third category that are not yet relevant for real-time modeling.
Click here to go to the SEP Validation Team page.
Please email Mark Dierckxsens, Mike Marsh, Masha Kuznetsova, and Leila Mays with your feedback which will be shared with the SEP scoreboard planning group.
Latest News
➡ See the up-to-date change log of the SEP web applications.
➡ 2024-04: iPATH data now appearing on the intensity web application.
➡ 2024-03: Intensity web app released with new layout.
➡ 2023-11: Probability web app released with new layout.
➡ 2023-08: New documentation detailing how the values in the Intensity web app's heat map are selected for each model is now available here.
➡ 2023-05: Working with Georgia State University to add their All Clear forecast to the SEP Scoreboard probability web application.
➡ 2023-02: MagPy has been added to the SEP Scoreboard probability web application.
➡ 2022-09: MLSO/K-Cor alerts have been added to the SEP Scoreboard probability and intensity web applications.
➡ 2022-06: SPRINTS has been added to the SEP Scoreboard probability web application.
➡ 2022-05: SAWS-ASPECS has been added to the SEP Scoreboard probability and intensity web applications.
➡ 2021-08: Combined probability and intensity all clear web application went live.
➡ 2021-03: SEPSTER2D data has been added to the SEP Scoreboard intensity web application.
➡ 2020-12: The SEP Scoreboard web applications are now live!
➡ 2020-12: ESWS 2020 presentation and screenshots [PDF]
➡ 2019-02: Final SEP Scoreboard JSON schema and helper script released
➡ 2020-10: Latest prototypes developed
➡ See the agenda/materials of the SHINE and ESWW15 2018 community campaign.
➡ The SEP scoreboard is part of the the SEP Validation Team in the Community-wide International Forum for Space Weather Modeling Capabilities Assessment.
➡ See the agenda of ESWW13 working meeting: Community-wide space weather Scoreboards: Research assessment of real-time forecasting models and techniques.
SEP scoreboard planning group
Please contact us to join
Leads: Mark Dierckxsens (BIRA-IASB), Mike Marsh (UK Met Office)
JSC SRAG, Ian Richardson (UMD/GSFC), Jesse Adries, Veronique Delouille (SIDC), Nathan Schwadron (UNH), Marlon Nunez (U Malaga), Anastasios Anastasiadis, Olga Malandraki (National Observatory of Athens), A. Posner (NASA HQ), B. Heber (Univ. of Kiel), J. Labrenz (Univ. of Kiel), Masha Kuznetsova (CCMC), M. Leila Mays (CCMC)
Participating partners
SEP Scoreboard and Data Access
SEP Scoreboard displays are now available publicly:
SEP Scoreboard forecast JSON file access:
- ISWA Webservice API, examples (URLs updated 2026-03-11):
CCMC Rules of the Road [PDF] apply: CCMC requests that users notify the CCMC, SEP model/technique developers and submitting forecasters before performing validation studies with the SEP Scoreboard database. It is recommended that such validation studies be performed with the knowledge and collaboration of developers and submitting forecasters/researchers.
List of SEP Events, with links to SEP Scoreboard applications
Submission File Format Information
SEP scoreboard JSON:
- visual schema (PDF) (last updated on 2026.03.11)
- example intensity profile text file (note format and units string in header)
A few tips on writing your data to to a JSON file.
Helper python script to create and validate JSON files:
- sep_json_writer.py (last updated on 2023.09.22)
- for a mapping of visual schema keys to helper script command line arguments, see the visual schema (PDF)
Contact CCMC software developer Joycelyn Jones with any JSON and script questions
View the SEP Scoreboard Change Log.
Reports & Presentations
How to get started with the SEP Scoreboard
- Participating modelers will fill in the registration form below and the CCMC model on-boarding questionnaire and email both to Leila Mays. We will then create a folder for the model on our anonymous ftp site and register your model metadata on the CCMC model registry.
- Participants will produce SEP forecasts conforming to the file format (JSON; see above).
- SEP forecast .json files will be accepted by anonymous ftp, each model has an assigned folder.
- All information will be stored in a database and the JSONs are made accessible to users via an API
- SEP forecasts will be displayed side by side on the "SEP Scoreboard" website - currently in development (beta version is live now).
CCMC Rules of the Road [PDF] apply: CCMC requests that users notify the CCMC, SEP model/technique developers and submitting forecasters before performing validation studies with the SEP Scoreboard database. It is recommended that such validation studies be performed with the knowledge and collaboration of developers and submitting forecasters/researchers.
Model/Technique Registration
(*=required)
Information about your group/project:
- Name(s) and e-mail (s) (please list primary contact first)*
- Associated Institution/Project name/Group name*
- Website URL(s)
- Logo(s)
Information about your method:
- Forecasting method name*
- Shorthand unique identifier for your method (methodname_version, e.g. ModelName_1 or ModelName_201201)
- Short description*
- Model Inputs*
- Model Outputs*
- References
Further Model Details:
- Is the forecast made continuously (e.g. probability for the next 24 hours, or a time series), or event-triggered (e.g. by a flare or CME).
- Is the forecast human generated, human generated but model-based, model-based, or other.
- If model-based: is the model empirical, physics-based or both.
SEP Models in the Community and Literature
Initially compiled by Mike Marsh; updated on 2026-03-11 based on Whitman et al. (2023).
| Model Type | Model Name | Principal Developer(s) | Observational Inputs | Outputs |
|---|---|---|---|---|
| Empirical | ADEPT | Stephen White, Stephen Kahler (AFRL), Alan Ling (AER) | GOES proton flux, flare location | E>10 MeV peak flux and fluence with uncertainty, event duration |
| Empirical | AER SEP model | Lisa Winter (LANL) | Type II, Type III, and Langmuir wave properties measured from Wind/WAVES | Probability of a > 10 MeV proton event (> 10 pfu) |
| Empirical | AFRL PPS | Don Smart, Margaret Shea (AFRL) | Flare (peak flux and time, location, onset time), solar wind speed | E>5, >10, and >50 MeV time profiles, including onset, peak, and end time, peak intensity, and fluence |
| Machine learning | Aminalragia-Giamini model | Sigiava Aminalragia-Giamini (SPARC, NKUA) | GOES x-ray, flare location | Probability of SEP during and after flare |
| Physics | AMPS | Valeryi Tenishev (NASA/MSFC) | Coupled with SWMF MHD | Energy spectrum at specified locations |
| Empirical/Machine learning | Boubrahimi model | Soukaina Boubrahimi (GSU) | GOES x-ray and proton flux | E>100 MeV intensities |
| Physics | EPREM | Matthew Young, Nathan Schwadron (UNH) | Can be driven by in-situ proton observations, can be coupled with MHD | User defined flux range, also dose calculations within EMMREM framework |
| Empirical/Machine learning | ESPERTA | Monica Laurenza(INAF), Edward Cliver (NSO), Alan Ling (AER), Tommaso Alberti (INAF), Mirko Stumpo (Roma, INAF), Simone Benella (INAF) | Flare location, SXR fluence, radio fluence, >10 MeV protons | Alert for >10 MeV protons |
| Empirical | FORSPEF | Anastasios Anastasiadis (NOA) | Magnetograms, x-ray flares, or CMEs | E > 30,60,100 MeV integral proton energy flux and fluence |
| Machine learning | Georgia State University | Berkay Aydin, Manolis Georgoulis, Anli Ji, Dustin Kempton, Chetraj Pandey, Rafal Angyk, Petrus Martens (GSU) | Near-real-time HARP | Probability for E>100 MeV protons exceeding 10 pfu in the next 24 hours |
| Physics | iPATH | Junxiang Hu (NASA/GSFC, UAH), Gang Li, Gary Zank (UAH) | Solar wind plasma, magnetic field, and turbulence parameters, CME parameters, suprathermal seed particle flux | Time profiles for differential energy channels at specified locations |
| Physics | Kota SEP (SWMF) | University of Michigan | SWMF module coupled with MHD | |
| Machine learning | Lavasa model | |||
| Physics | SEPMOD | Janet Luhmann (UCB SSL) | Coupled with WSA-ENLIL+Cone (magnetograms, coronagraphs) | User defined flux range |
| Empirical | MAG4 | David Falconer (NASA/MSFC, UAH) | Magnetograms, x-ray flares | 24 hour event probabilistic forecast |
| Empirical | MagPy | |||
| Machine learning | MEMPSEP | |||
| Physics | M-FLAMPA | |||
| Physics | PARADISE | Nicolas Wijsen, Angels Aran | Coupled with the EUHFORIA MHD model | |
| Empirical | PCA (Papaioannou) model | |||
| Machine learning | PHSVM | |||
| Physics & Empirical | PREDICCS | Nathan Schwadron (UNH) | (coupled version of EMMREM and REleASE) | |
| Empirical | PROTONS | |||
| Empirical | REleASE | Arik Posner | SOHO/COSTEP-EPHIN high energy electron flux. ACE/EPAM in new version | E=4-9, 9-16, 16-40, 28-50 MeV proton flux |
| Machine learning | Sadykov model | |||
| Empirical | SAWS-ASPECS | |||
| Empirical | SEPForecast (COMESEP) | Mark Dierckxsens (BIRA IASB) | GOES x-ray peak flux & location, CME width & velocity, GLE observations | E>10 MeV and >60 MeV integral proton energy peak flux and probability |
| Physics | SEPMOD | |||
| Empirical | SEPSTER | |||
| Empirical | SEPSTER2D | |||
| Machine learning | SMARP model | |||
| Physics | SOLPENCO | Angels Aran (Univ. Barcelona) | CME/Flare location & shock velocity estimate | User defined flux range |
| Physics | South African model | |||
| Physics | SPARX | Silvia Dalla (UCLan) Mike Marsh (UK Met Office) | Flare location, peak x-ray flux | User defined flux range |
| Physics | SPREAdFAST | |||
| Empirical/Machine learning | SPRINTS | Alec Engel (NextGen Federal Systems) | x-ray flares | 10 MeV onset and peak flux |
| Physics | STAT | |||
| Empirical | SWPC PPM | Christopher Balch (NOAA/SWPC) | GOES x-ray, SEON radio burst, H-alpha/EUV imaging | E>10 MeV integral peak proton flux, peak time, and probability |
| Empirical | SWPC | NOAA/SWPC | Day 1-3 event probabilistic forecast | |
| Empirical | UMASEP | Marlon Nuñez (Univ. Malaga) | Goes x-ray & proton fluxes | E>10 MeV integral proton flux. E>100 MeV proton flux in new version. |
| Empirical | UK Met Office | UK Met Office | Day 1-4 event probabilistic forecast | |
| Physics | Zhang model | Ming Zhang (FIT) |
Short Documentation on Models in SEP Scoreboard (a.k.a., One Pagers)
Publication Policy
For tracking purposes for our government sponsors, we ask that you notify the CCMC whenever you use any CCMC tools/software systems in any scientific publications and/or presentations. Follow the steps on the publication submission page
See our full publication policy for a sample 'acknowledgement statement' to be included in your publication.