Ionospheric modeling for short- and long-term predictions of F region parameters over Indian zone

The equatorial and low latitudinal F region ionosphere is highly dynamic and unpredictable because of various geophysical mechanisms operating therein. In the present study, two HF prediction models for short and long term are developed for equatorial and low‐latitude F region ionosphere. In the first approach, multiple regression analysis (MRA) for the dependence of F region parameters, namely, foF2 and M(3000)F2, on solar 2800 MHz flux (F10) and geomagnetic index Ap are generated, and in the second one, second‐degree (SD) coefficients are generated, both by fitting monthly median foF2 and M(3000)F2 with corresponding 12 monthly mean sunspot numbers (R12) using data over three solar cycles. For MRA, daily foF2 and M(3000)F2 values for each hour obtained from Delhi (28.6°N, 77.1°E) digital ionosonde for about half a solar cycle are used. MRA coefficients for foF2 and M(3000)F2 are obtained for every month over 2400 UT times using daily F10 and Ap values separately for quiet (Ap < 25) and disturbed (Ap > 25) periods. Similarly, SD coefficients are obtained each month at all local times for all the 14 stations covering a geographic latitude range from about 0°N to 45°N. In this way, once appropriate coefficients for each hour for all the 12 months are obtained, they are used by the computer‐based MRA and SD models to predict ionospheric hourly foF2 and hmF2 values for given inputs such as month, F10, Ap, and R12, as the case may be. Predicted model values of foF2 and hmF2, calculated on short‐ and long‐term basis, are then compared with the observed data over Delhi and also with those obtained using international reference ionosphere (IRI)‐2001 model. From our comparative studies it is observed that MRA and SD models show better agreement with observations compared to the IRI model for both long‐ and short‐term basis and among the two the MRA model provides best agreement with the observed ones, even during the magnetic storm periods. The SD model, on the other hand, which is based on monthly median values, is useful for providing long‐term predictions for HF communication applications.