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The solar radio flux at F10.7 cm and F30 cm is required by most modelscharacterizing the state of the Earth's upper atmosphere, such as thethermosphere and ionosphere to specify satellite orbits, re-entry services,collision avoidance maneuvers and modeling of space debris evolution. Wedevelop a method called RESONANCE ("Radio Emissions from the Sun: ONlineANalytical Computer-aided Estimator") for the prediction of the 13-monthsmoothed monthly mean F10.7 and F30 indices 1-24 months ahead. The predictionalgorithm includes three steps. First, we apply a 13-month optimized runningmean technique to effectively reduce the noise in the radio flux data. Second,we provide initial predictions of the F10.7 and F30 indices using theMcNish-Lincoln method. Finally, we improve these initial predictions bydeveloping an adaptive Kalman filter with the error statistics identification.The root-mean-square-error of predictions with lead times from 1 to 24 monthsis 5-27 sfu for the F10.7 and 3-16 sfu for F30 index, which statisticallyoutperforms current algorithms in use. The proposed approach based on Kalmanfilter is universal and can be applied to improve the initial predictions of aprocess under study provided by any other forecasting method. Furthermore, wepresent a systematic evaluation of re-entry forecast as an application to testthe performance of F10.7 predictions on past ESA re-entry campaigns forpayloads, rocket bodies, and space debris that re-entered from June 2006 toJune 2019. The test results demonstrate that the predictions obtained byRESONANCE in general also lead to improvements in the forecasts of re-entryepochs.

Medium-term predictions of F10.7 and F30 cm solar radio flux with the adaptive Kalman filter