Characteristics of high‐latitude TIDs from different causative mechanisms deduced by theoretical modeling

Abstract

The high‐latitude ionosphere is a physically complex regime where traveling ionospheric disturbance signatures (TIDs) are caused not only by atmospheric gravity waves (AGWs) but also by other processes. Among these, wavelike variations in E × B drift strength, particle precipitation intensity, and ionospheric return current density are the most relevant ones; they can produce TIDs similar to AGW/TIDs (typical periods around 1 hour), frequently seen, for example, in incoherent scatter (IS) data. This study is concerned with elucidating the characteristics of TIDs from these different mechanisms in the high‐latitude F region, aiming at three major aspects: (1) the causative mechanism‐TID relationship, (2) the interpretation of TIDs in ionospheric data, especially IS data, in terms of causative mechanisms, and (3) the reliable recognition of AGW‐induced TIDs, which are of special interest. To study these aspects tailored to IS data exploitation, theoretical simulations of TIDs were performed for representative cases of the causative mechanisms, utilizing a realistic ionospheric model (“Graz Ionospheric Flux Tube Simulation (GIFTS) model”) which self‐consistently yields the fundamental TID quantities, electron density, ion drift, ion and electron temperature, and further mechanism‐dependent quantities. The physical understanding of the non‐AGW/TIDs could be significantly improved. A general finding was that because of nonlinear processes (e.g., frictional heating) and its coupling to the thermosphere, the ionosphere is capable of generating complicated TID waveforms (e.g., a dominant second harmonic) even for a monochromatic source mechanism. TID information derivable from IS data was found sufficiently unique for each mechanism to allow its identification. In particular, the unique features of AGW/TIDs allow their reliable recognition among other TIDs. The results provide a good basis for future automation of the recognition and quantification of causative mechanisms based on IS data.