Space weather dependence of decameter-scale ionospheric irregularities spatial distribution

This paper describes an approach to constructing nowcast and forecast predictive models for the occurrence of high-latitude decameter ionospheric irregularities based on SuperDARN and SECIRA radar data and space weather parameters. The method relies on self-learning pattern recognition in the dynamics of space weather parameters, that influence the irregularities formation. The proposed approach is a data-driven, multiscale method in which the spatial distribution of irregularities is approximated using a neural network of an original architecture, inspired by graph neural networks. The model was trained and tested using data from 11 radars during the period 2010-2025 (SuperDARN: CVW, ICE, ICW, INV, PGR, PYK, RKN, SAS, STO; SECIRA: EKB, MAGW). It is shown that the model achieves optimal performance when using geomagnetic coordinates MLT,MLAT. The optimal set of parameters for the best nowcast and forecast over studied set of parameters is a combination of geomagnetic and solar disturbance indices (a p, K p, AE, F10.7) from ground-based observations, and satellite-measured solar wind and interplanetary magnetic field parameters (P sw, E sw,, T sw). Hourly mean values for all the parameters were used in the analysis. The results indicate that acceptable forecast quality is achieved with a lead time of up to 7-9 hours, with the highest quality reached for nowcast (zero lead time). It is shown that the parameters and F10.7 are significant for nowcasting only and can be neglected for other lead times. It is shown that AE and T sw are significant for mid-term forecast (2hours ahead) and can be neglected for short-term predictions. Statistical relationships between the maximum latitude of the equatorial boundary of the irregularities and space weather parameters are also discussed.