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Forecasting Solar Cycle 25 with Physical Model-Validated Recurrent Neural Networks
KTH, Centres, Nordic Institute for Theoretical Physics NORDITA. Stockholm Univ, Hannes Alfvens vag 12, SE-10691 Stockholm, Sweden..
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.ORCID iD: 0000-0001-6612-6923
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.ORCID iD: 0000-0003-2638-6047
KTH, Centres, Nordic Institute for Theoretical Physics NORDITA. Stockholm Univ, Hannes Alfvens vag 12, SE-10691 Stockholm, Sweden..ORCID iD: 0000-0001-6162-7112
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2023 (English)In: Solar Physics, ISSN 0038-0938, E-ISSN 1573-093X, Vol. 298, no 1, article id 8Article in journal (Refereed) Published
Abstract [en]

The Sun's activity, which is associated with the solar magnetic cycle, creates a dynamic environment in space known as space weather. Severe space weather can disrupt space-based and Earth-based technologies. Slow decadal-scale variations on solar-cycle timescales are important for radiative forcing of the Earth's atmosphere and impact satellite lifetimes and atmospheric dynamics. Predicting the solar magnetic cycle is therefore of critical importance for humanity. In this context, a novel development is the application of machine-learning algorithms for solar-cycle forecasting. Diverse approaches have been developed for this purpose; however, with no consensus across different techniques and physics-based approaches. Here, we first explore the performance of four different machine-learning algorithms - all of them belonging to a class called Recurrent Neural Networks (RNNs) - in predicting simulated sunspot cycles based on a widely studied, stochastically forced, nonlinear time-delay solar dynamo model. We conclude that the algorithm Echo State Network (ESN) performs the best, but predictability is limited to only one future sunspot cycle, in agreement with recent physical insights. Subsequently, we train the ESN algorithm and a modified version of it (MESN) with solar-cycle observations to forecast Cycles 22 - 25. We obtain accurate hindcasts for Solar Cycles 22 - 24. For Solar Cycle 25 the ESN algorithm forecasts a peak amplitude of 131 +/- 14 sunspots around July 2024 and indicates a cycle length of approximately 10 years. The MESN forecasts a peak of 137 +/- 2 sunspots around April 2024, with the same cycle length. Qualitatively, both forecasts indicate that Cycle 25 will be slightly stronger than Cycle 24 but weaker than Cycle 23. Our novel approach bridges physical model-based forecasts with machine-learning-based approaches, achieving consistency across these diverse techniques.

Place, publisher, year, edition, pages
Springer Nature , 2023. Vol. 298, no 1, article id 8
Keywords [en]
Solar cycle, Sunspots, Statistics
National Category
Astronomy, Astrophysics and Cosmology
Identifiers
URN: urn:nbn:se:kth:diva-323588DOI: 10.1007/s11207-022-02104-3ISI: 000913507700001Scopus ID: 2-s2.0-85146268485OAI: oai:DiVA.org:kth-323588DiVA, id: diva2:1735144
Note

QC 20230208

Available from: 2023-02-08 Created: 2023-02-08 Last updated: 2023-02-08Bibliographically approved

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Fontcuberta, Aleix EspunaGhosh, AnubhabChatterjee, SaikatMitra, Dhrubaditya

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Fontcuberta, Aleix EspunaGhosh, AnubhabChatterjee, SaikatMitra, Dhrubaditya
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Solar Physics
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