Archive Access

Access to the archive is given through the following XML files. Archive data is generated when the corresponding XML file is clicked, to ensure it contains the latest available information.
year link      year link      year link
2013 flarecast_2013.xml 2018 flarecast_2018.xml 2023 flarecast_2023.xml
2014 flarecast_2014.xml 2019 flarecast_2019.xml 2024 flarecast_2024.xml
2015 flarecast_2015.xml 2020 flarecast_2020.xml 2025 flarecast_2025.xml
2016 flarecast_2016.xml 2021 flarecast_2021.xml 2026 flarecast_2026.xml
2017 flarecast_2017.xml 2022 flarecast_2022.xml latest flarecast_latest.xml


This product shows the prediction of a flare occurrence within the next 24 hours after the given date, and starting at 3AM. The probability is based on the best available machine learning algorithm tested in FLARECAST, Random Forest. The flare size assumed is either C, M, or X, following the GOES classification. The NOAA active region number(s) is or are indicated, and the diagram in the grey area emphasizes the different probability for each active region. If there are no active regions present, the system cannot make any prediction and issues a corresponding message.

The FLARECAST consortium developed an automated forecasting system for solar flares. The team integrated virtually every solar flare-predicting parameter into an open online application programming interface, flexible enough to facilitate future expansion. We identified the best performers by employing a variety of statistical and machine learning techinques, including standard methods such as Linear Discriminant Analysis, Clustering and Regression Analysis, Neural Networks, as well as innovative approches including Multi-Task Lasso, Simulated Annealing and Random Forest. A robust exploration work package identified promising new predictors and connected flare prediction to other manifestations of solar eruptive activity such as coronal mass ejections.

For more information please visit this page:

A simple REST API gives access to the FLARECAST flare forecasts of a whole year. You may use any common HTTP client such as wget, curl or Python requests. Make sure to pass a valid authentication cookie to the method.


Base URL:
System type: ['test'|'prod']
Command: /api/prediction/flarecast_${year}.xml
Year: four digit year after 2013 or 'latest'.


The top menu navigation bar allows the user to browse through the products (across the different "Solar Weather" providers) as follows: