Summary
The Sun is an enigmatic star that produces the most powerful explosive events in our solar system - solar flares and coronal mass ejections. Studying these phenomena can provide a unique opportunity to develop a deeper understanding of fundamental processes on the Sun, and critically, to better forecast space weather.
The Active Region Classification and Flare Forecasting (ARCAFF) project will develop a beyond state-of-the-art flare forecasting system utilising end-to-end deep learning (DL) models to significantly improve upon traditional flare forecasting capabilities. ARCAFF will increase the accuracy and timeliness of current operational flare forecast products and create new time series flare forecasts. Furthermore, ARCAFF forecasts will include forecast uncertainties, another major improvement over current systems.
The large amount of available space-based solar observations are an ideal candidate for this type of analysis, given DL effectiveness in modelling complex relationships. DL has already been successfully developed and deployed in weather forecasting, financial services, and health care domains, but has not been fully exploited in the solar physics domain. Solar flare forecasts from ARCAFF will be benchmarked against current systems using international community standards, and will demonstrate ARCAFF’s superior forecasting capabilities. The datasets, codes and DNNS developed for ARCAFF will be made openly available to support further research efforts and encourage their re-use.
ARCAFF is relevant to the work program as it will exploit currently available data space weather data to train DL models to improve forecast accuracy. DL itself is an innovation enabling technology and analysis of the DL models will improve scientific understanding of solar flares. Through the creation of new forecast products it will develop and mature new concepts for both scientific and monitoring purposes, following the best-practices of meteorological services.
The Active Region Classification and Flare Forecasting (ARCAFF) project will develop a beyond state-of-the-art flare forecasting system utilising end-to-end deep learning (DL) models to significantly improve upon traditional flare forecasting capabilities. ARCAFF will increase the accuracy and timeliness of current operational flare forecast products and create new time series flare forecasts. Furthermore, ARCAFF forecasts will include forecast uncertainties, another major improvement over current systems.
The large amount of available space-based solar observations are an ideal candidate for this type of analysis, given DL effectiveness in modelling complex relationships. DL has already been successfully developed and deployed in weather forecasting, financial services, and health care domains, but has not been fully exploited in the solar physics domain. Solar flare forecasts from ARCAFF will be benchmarked against current systems using international community standards, and will demonstrate ARCAFF’s superior forecasting capabilities. The datasets, codes and DNNS developed for ARCAFF will be made openly available to support further research efforts and encourage their re-use.
ARCAFF is relevant to the work program as it will exploit currently available data space weather data to train DL models to improve forecast accuracy. DL itself is an innovation enabling technology and analysis of the DL models will improve scientific understanding of solar flares. Through the creation of new forecast products it will develop and mature new concepts for both scientific and monitoring purposes, following the best-practices of meteorological services.
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/101082164 |
| Start date: | 01-12-2022 |
| End date: | 30-11-2025 |
| Total budget - Public funding: | 604 111,25 Euro - 604 111,00 Euro |
Cordis data
Original description
The Sun is an enigmatic star that produces the most powerful explosive events in our solar system - solar flares and coronal mass ejections. Studying these phenomena can provide a unique opportunity to develop a deeper understanding of fundamental processes on the Sun, and critically, to better forecast space weather.The Active Region Classification and Flare Forecasting (ARCAFF) project will develop a beyond state-of-the-art flare forecasting system utilising end-to-end deep learning (DL) models to significantly improve upon traditional flare forecasting capabilities. ARCAFF will increase the accuracy and timeliness of current operational flare forecast products and create new time series flare forecasts. Furthermore, ARCAFF forecasts will include forecast uncertainties, another major improvement over current systems.
The large amount of available space-based solar observations are an ideal candidate for this type of analysis, given DL effectiveness in modelling complex relationships. DL has already been successfully developed and deployed in weather forecasting, financial services, and health care domains, but has not been fully exploited in the solar physics domain. Solar flare forecasts from ARCAFF will be benchmarked against current systems using international community standards, and will demonstrate ARCAFF’s superior forecasting capabilities. The datasets, codes and DNNS developed for ARCAFF will be made openly available to support further research efforts and encourage their re-use.
ARCAFF is relevant to the work program as it will exploit currently available data space weather data to train DL models to improve forecast accuracy. DL itself is an innovation enabling technology and analysis of the DL models will improve scientific understanding of solar flares. Through the creation of new forecast products it will develop and mature new concepts for both scientific and monitoring purposes, following the best-practices of meteorological services.
Status
SIGNEDCall topic
HORIZON-CL4-2022-SPACE-01-62Update Date
06-02-2023
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