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In this paper, a method is described for using 8-20MHz noise absorptioneffect for realtime detecting radiowave absorption periods. The method is basedon two empirical autoregression models of noise dynamics. The first (rough)prediction model is based on radar measurements of daily minimal noise dynamicsaveraged over 28-days with specially calculated weight coefficients. The second(fine) prediction model uses real-time scaling of rough model. The scaling isbased on the comparison of this model with the experimental noise observationsduring previous 5 days. The models are based on the whole EKB ISTP SB RAS radardataset (2013-2018). The rough model allows one to estimate the boundary beyondwhich the noise variations can be associated with absorption periods with ahigh degree of certainty. A joint analysis of simultaneous data on neighboringradar beams and at several frequencies reduces the detection errors, and allowsto identify absorption events with a higher degree of confidence. Use of finemodel allows to estimate absorption. The technique is validated by frequencydependence of absorption during two-frequency measurements. The found frequencydependence has an average exponent of the order of -1.5, which is in goodagreement with the literature data and the data obtained earlier in analysis ofsolar X-ray flares. The use of the detection technique at EKB radar shows thatmost probable absorption over absorption events is about -0.65dB. Analysis ofabsorption of different amplitudes shows that low-intensity absorption events(0..-1.3dB) have slight local time dependence and mostly observed at northdirections. For the storng absorption events (stronger than -1.3dB) the localtime dynamics correlates well with noise level dynamics, and usually fills thewhole radar field-of-view.

Noise level forecasts at 8-20MHz and their use for morphological studies of ionospheric absorption variations at EKB ISTP SB RAS radar