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

TL;DR
This paper presents a real-time noise absorption detection method using empirical autoregression models for ionospheric studies, validated with frequency-dependent measurements and analysis of absorption event characteristics.
Contribution
It introduces a novel two-model approach for detecting ionospheric absorption periods using radar noise data, improving accuracy and confidence in identifying absorption events.
Findings
Frequency dependence of absorption with an exponent around -1.5
Most probable absorption during events is about -0.65dB
Low-intensity events are geographically localized and less time-dependent
Abstract
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…
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