Forecasting the solar cycle using variational data assimilation: validation on cycles 22 to 25
L. Jouve, C.P. Hung, A. S. Brun, S. Hazra, A. Fournier, O. Talagrand, B. Perri, A. Strugarek

TL;DR
This paper introduces a novel 4D variational data assimilation method applied to a solar dynamo model, successfully forecasting the amplitude and timing of solar cycle maxima and minima using real observational data.
Contribution
It is the first application of 4D variational data assimilation to real solar data for solar cycle prediction, integrating a physics-based model with observations to improve forecast accuracy.
Findings
Validated on cycles 22-24 with satisfactory results
Predicted cycle 25 maximum between mid-2024 and early 2025 with sunspot number ~143
Forecasted a solar minimum around late 2029 with notable uncertainty
Abstract
Forecasting future solar activity has become crucial in our modern world, where intense eruptive phenomena mostly occurring during solar maximum are likely to be strongly damaging to satellites and telecommunications. We present a 4D variational assimilation technique applied for the first time to real solar data. Our method is tested against observations of past cycles 22, 23, 24 and on the ongoing cycle 25 for which we give an estimate of the imminent maximum value and timing and also provide a first forecast of the next solar minimum. We use a variational data assimilation technique applied to a solar mean-field Babcock-Leighton flux-transport dynamo model. Ensemble predictions are produced in order to obtain uncertainties on the timing and value of the maximum of cycle , when data on cycle is assimilated. We study in particular the influence of the phase during which data…
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