The issue of modeling the Earth's lower ionosphere in calm and disturbed conditions remains one of the most urgent issues in atmospheric research. The difficulty of obtaining experimental data at heights of the D-region (50-90 km) hinders the construction of good quality empirical models of ionospheric parameters; therefore, the dynamics of the component concentrations under conditions of disturbances (e.g., X-ray flares) are typically described by theoretical models of varying complexity. Considering the dynamics of a large number of charged and small neutral components allows, a more detailed and interconnected description of the photochemistry of the medium (

This study presents the results obtained from modeling the lower ionosphere response to C‐, M‐ and X‐class solar X‐ray flares. This model is based on a 5‐component scheme for the ionization‐recombination cycle of the ionospheric D‐region. Input parameters for the plasma‐chemical model under different heliogeophysical conditions corresponding to selected X‐ray flares were determined by using data received from the AURA, SDO, and GOES satellites. Verification of the obtained results was carried out with use of ground‐based radiophysical measurements taken at the geophysical observatory Mikhnevo. The results obtained from a comparison of the calculated and experimental radio wave amplitude variations along six European very low frequency (VLF) paths show that the average normalized root mean square error is ∼7%, 14%, and 18% for C‐, M‐, and X‐class flares, respectively. Qualitative and quantitative analysis of the verification results for the VLF signal amplitude show good predictive capability of the model built for describing weak and moderate ionospheric disturbances.

It is demonstrated that it is required to create probabilistic statistical models of the ionosphere for calculating radio propagation in a wide frequency range. This, in fact, presents a new type of ionospheric modeling. These models are classified into pure statistical and deterministic stochastic. We describe the key principles of building such models, present some examples of their construction, and discuss some difficulties arising from them.