  In this paper, a method is described for using 8-20MHz noise absorption
effect for realtime detecting radiowave absorption periods. The method is based
on two empirical autoregression models of noise dynamics. The first (rough)
prediction model is based on radar measurements of daily minimal noise dynamics
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
dataset (2013-2018). The rough model allows one to estimate the boundary beyond
which the noise variations can be associated with absorption periods with a
high degree of certainty. A joint analysis of simultaneous data on neighboring
radar beams and at several frequencies reduces the detection errors, and allows
to identify absorption events with a higher degree of confidence. Use of fine
model allows 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 the order of -1.5, which is in good
agreement with the literature data and the data obtained earlier in analysis of
solar X-ray flares. The use of the detection technique at EKB radar shows that
most probable absorption over absorption events is about -0.65dB. Analysis of
absorption of different amplitudes shows that low-intensity absorption events
(0..-1.3dB) have slight local time dependence and mostly observed at north
directions. For the storng absorption events (stronger than -1.3dB) the local
time dynamics correlates well with noise level dynamics, and usually fills the
whole radar field-of-view.
