Podladchikova, O ; Marque, C

Poster presented at 15th, European Space Weather Week on 2018-11-09

Abstract: Solar radio flux measurements at 10.7 cm provide a reliable monitoring dataset of the solar activity over the past six solar cycles. The radiation at 10.7 cm is coming from the upper chromospheric/low coronal layers of the Sun, and it is correlated with white light, sunspot number and UV radiation which impacts terrestrial atmospheric layers from the ionosphere till the stratosphere. The statistically analysis of F10.7 index data set is very robust due to the nature of the ground based measurements which are practically unaffected by the weather conditions. Slow modulations of highly non-stationary F10.7 index data series have strong impact on the terrestrial climate, while fast changes - related to energetic solar events - have immediate impact on high frequency communications and on the satellite drag effect, which is significant for small size satellites. In this work, we discuss and update 3 different approaches for the forecast of F10.7 index: 1. SASFF (Self-Adjusted Solar Flux Forecasting) algorithm: the radioflux is described by a non-stationary random walk model with variable drift and the forecast is performed using an adaptive Kalman Filter. 2. Random walk model with and unknown drift and also unknown variance.  The choice of the random walk model is justified by a very weak autocorrelation of the radioflux increment. Uncertainty of the model parameters (drift, variance) is evaluated dynamically and applied for the next forecasting step. 3. Linear regression which considers dependences of the F10.7 index to other solar indices (e.g sunspot number). Considering the fact that constant coefficients cannot reflect non-stationary radioflux behaviour, we correct the regression coefficients during observations using an adaptive Kalman Filter technique. A comparative analysis of the forecasting errors is performed as the function of the solar cycle for every method.

Keyword(s): Sun, space weather, models