CHARACTERISTICS OF HIGH-LATITUDE TIDS FROM DIFFERENT CAUSATIVE MECHANISMS DEDUCED BY THEORETICAL MODELING

The high-latitude ionosphere is a physically complex regime where trav eling ionospheric disturbance signatures (TIDs) are caused not only by atmospheric gravity waves (AGWs) but also by other processes. Among t hese, wavelike variations in E x B drift strength, particle precipitat ion intensity, and ionospheric return current density are the most rel evant 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 relat ionship, (2) the interpretation of TIDs in ionospheric data, especiall y IS. data, in terms of causative mechanisms, and (3) the reliable rec ognition of AGW-induced TIDs, which are of special interest. To study these aspects tailored to IS data exploitation, theoretical simulation s of TIDs were performed for representative cases of the causative mec hanisms, utilizing a realistic ionospheric model (''Graz Ionospheric F lux Tube Simulation (GIFTS) model'') which self-consistently yields th e fundamental TID quantities, electron density, ion drift, ion and ele ctron temperature, and further mechanism-dependent quantities. The phy sical understanding of the non-AGW/TIDs could be significantly improve d. A general finding was that because of nonlinear processes (e.g., fr ictional 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 inform ation derivable from IS data was found sufficiently unique for each me chanism to allow its identification. In particular, the unique feature s of AGW/TIDs allow their reliable recognition among other TIDs. The r esults provide a good basis for future automation of the recognition a nd quantification of causative mechanisms based on IS data.