Regional representation of F2 Chapman parameters based on electron density profiles

• Main Authors: M. Limberger, W. Liang, M. Schmidt, D. Dettmering, U. Hugentobler
• Format: Article
• Language: English
• Published: Copernicus Publications 2013-12-01
• Series: Annales Geophysicae
• Online Access: https://www.ann-geophys.net/31/2215/2013/angeo-31-2215-2013.pdf
• ISSN: 0992-7689; 1432-0576

Understanding the physical processes within the ionosphere is a key requirement to improve and extend ionospheric modeling approaches. The determination of meaningful parameters to describe the vertical electron density distribution and how they are influenced by the solar activity is an important topic in ionospheric research. In this regard, the F2 layer of the ionosphere plays a key role as it contains the highest concentration of electrons and ions. In this contribution, the maximum electron density <i>Nm</i>F2, peak height <i>hm</i>F2 and scale height <i>H</i>F2 of the F2 layer are determined by employing a model approach for regional applications realized by the combination of endpoint-interpolating polynomial B splines with an adapted physics-motivated Chapman layer. For this purpose, electron density profiles derived from ionospheric GPS radio occultation measurements of the satellite missions FORMOSAT-3/COSMIC, GRACE and CHAMP have been successfully exploited. Profiles contain electron density observations at discrete spots, in contrast to the commonly used integrated total electron content from GNSS, and therefore are highly sensitive to obtaining the required information of the vertical electron density structure. The spatio-temporal availability of profiles is indeed rather sparse, but the model approach meets all requirements to combine observation techniques implicating the mutual support of the measurements concerning accuracy, sensitivity and data resolution. For the model initialization and to bridge observation gaps, the International Reference Ionosphere 2007 is applied. Validations by means of simulations and selected real data scenarios show that this model approach has significant potential and the ability to yield reliable results.