LAUSR

Abstract In this paper, a method is described for using 8–20 MHz noise absorption effect for real time detecting radio wave absorption periods. The method is based on two empirical autoregression models of noise variations. The first (rough) prediction model is based on radar measurements of daily minimum noise variations averaged over 28-days with specially calculated weight coefficients. The second (fine) prediction model uses real-time scaling of rough model. The scaling is based on the comparison of this model with the experimental noise observations during previous 5 days. The models are based on the whole EKB ISTP SB RAS radar data set (2013–2018). The rough model allows one to estimate the boundary beyond which the noise variations can be associated with absorption periods. A joint analysis of simultaneous data on neighboring radar beams and at several frequencies reduces the detection errors, and allows one to identify noise absorption incidents with a higher degree of confidence. Use of the fine model allows us to estimate absorption. The technique is validated by frequency dependence of absorption during two-frequency measurements. The found frequency dependence has an average exponent of about −1.5, which is in good agreement with the literature data and the data obtained earlier in the analysis of solar X-ray flares. The use of the detection technique at EKB radar shows that the mode of vertical absorption at 10 MHz over noise absorption incidents is about 0.65 dB. Analysis of absorption of different amplitudes shows that low-intensity noise absorption incidents (0..1.3 dB) have slight local time dependence and mostly observed at north directions. For the strong noise absorption incidents (stronger than 1.3 dB) the analysis of local time dependence shown that the mean absorption level over all the noise absorption incidents correlates well with the mean noise level over all the available experiments, and usually is observed at every azimuth within the radar field of view.