Medium-term Predictions of F10.7 and F30 cm Solar Radio Flux with the Adaptive Kalman Filter

Abstract

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.

PINE-RT: An operational real-time plasmasphere model

“…In particular, it has a performance slightly worse than the one of the PINE model, which uses the geomagnetic indices AE, Kp, Sym-H, and F10.7 as inputs, and slightly better than the one of the VERB-CS model. The PAGER project is close to its end, but in a possible follow-up project we will explore the availability or the development of models predicting AE, Sym-H, and F10.7, such as Siciliano et al (2021); Iong et al (2022); Pallocchia et al (2008); Pallocchia et al (2021). In case of their availability, it will be interesting to compare the performances of the PINE-RT model with the one of the PINE model when driven by the respective predicted inputs.…”

Limiting Magnitudes of the Wide Field Survey Telescope (WFST)

“…The sky background spectrum of the Lenghu site is also calculated by the software SkyCalc (Noll et al 2012;Jones et al 2013;Moehler et al 2014). The monthly averaged solar radio flux is equal to 130.00 sfu, that is the solar 10.7 cm radio flux in the Sun median active level (Sparavigna 2008;Petrova et al 2021). Because the solar activities will affect the sky background brightness, it is necessary to take the solar activities into account when we estimate the sky background.…”

Automatic Spread‐F Detection Using Deep Learning

“…The data set is also chosen to coincide with the 24th solar cycle, which was from 2008 to 2019, as illustrated by the F10.7 solar flux index. The F10.7 index results from calculating hourly average of the flux of radiation, whose wavelength is 10.7 cm, issuing from the sun (Petrova et al., 2021). F10.7 has a long historical record and has been a good proxy for ultraviolet radiation while also correlating with sunspot number.…”