The solar radio flux at F10.7 and F30 cm is required by most models characterizing the state of the Earth’s upper atmosphere, such as the thermosphere and ionosphere, to specify satellite orbits, re-entry services, collision avoidance maneuvers, and modeling of the evolution of space debris. We develop a method called RESONANCE (Radio Emissions from the Sun: ONline ANalytical Computer-aided Estimator) for the prediction of the 13-month smoothed monthly mean F10.7 and F30 indices 1–24 months ahead. The prediction algorithm has three steps. First, we apply a 13-month optimized running mean technique to effectively reduce the noise in the radio flux data. Second, we provide initial predictions of the F10.7 and F30 indices using the McNish–Lincoln method. Finally, we improve these initial predictions by developing an adaptive Kalman filter with identification of the error statistics. The rms error of predictions with lead times from 1 to 24 months is 5–27 solar flux units (sfu) for the F10.7 index and 3–16 sfu for F30, which statistically outperforms current algorithms in use. The proposed approach based on the Kalman filter is universal and can be applied to improve the initial predictions of a process under study provided by any other forecasting method. Furthermore, we present a systematic evaluation of re-entry forecast as an application to test the performance of F10.7 predictions on past ESA re-entry campaigns for payloads, rocket bodies, and space debris that re-entered from 2006 to 2019 June. The test results demonstrate that the predictions obtained by RESONANCE in general also lead to improvements in the forecasts of re-entry epochs.

大多数描述地球高层大气状态（例如热层和电离层）的模型都需要 F10.7 和 F30 cm 处的太阳射电通量，以指定卫星轨道、再入服务、避撞机动和建模空间碎片的演变。我们开发了一种称为 RESONANCE（来自太阳的无线电发射：在线分析计算机辅助估计器）的方法，用于预测未来 1-2​​4 个月的 13 个月平滑月平均 F10.7 和 F30 指数。预测算法分为三个步骤。首先，我们应用了 13 个月的优化运行平均技术来有效降低无线电通量数据中的噪声。其次，我们使用 McNish-Lincoln 方法提供 F10.7 和 F30 指数的初始预测。最后，我们通过开发具有误差统计识别的自适应卡尔曼滤波器来改进这些初始预测。提前期为 1 到 24 个月的预测的均方根误差对于 F10.7 指数为 5-27 太阳通量单位 (sfu)，对于 F30 为 3-16 sfu，这在统计上优于当前使用的算法。所提出的基于卡尔曼滤波器的方法是通用的，可用于改进任何其他预测方法提供的所研究过程的初始预测。此外，我们提出了再入预测的系统评估，作为测试 F10.7 预测在过去的 ESA 再入活动中对 2006 年至 2019 年 6 月重新进入的有效载荷、火箭体和空间碎片的性能的应用。