Modeling of Topside Ionospheric Vertical Scale Height Based on Ionospheric Radio Occultation Measurements. Issue 6 (23rd June 2019) - Hu, Andong; Carter, Brett; Currie, Julie; Norman, Robert; Wu, Suqin; Wang, Xiaoming; Zhang, Kefei
Abstract: An artificial neural network (ANN) method for the modeling of global topside ionospheric vertical scale height (VSH) using electron density profiles retrieved from Global Navigation Satellite Systems radio occultation (RO) data is proposed in this study. The data for this study are 80, 124 VSHs derived from the events randomly selected from 9 years of Constellation Observing System for Meteorology, Ionosphere, and Climate RO measurements and 144, 530 VSHs derived from the events randomly selected from 16 years of topside sounder measurements of both Aluoette‐1/2 and ISIS‐1/2 satellites during 1962–1978 are used for comparison. VSHs from the International Reference Ionosphere are also used for the comparison. Results showed that: (1) the median of the relative residuals of the new ANN regression approach/model (which was based on RO measurements) was 8.5% less than that of the traditional approach/model (which was based on the topside sounder data); (2) the median of the relative residuals of the ANN model when longitude was used as a variable was 1.1% less than the one without longitude; and substantial error in the polar region was shown to be mitigated by taking the variable longitude into consideration; (3) compared to International Reference Ionosphere, the accuracy of the new ANN model was improved by around 14%; (4) the new ANN model outperforms the traditional base vector‐based least squares model by around 10% when incoherent scatter radar measurements are used as a reference; and (5) the characteristics of global VSHs generated from the new model during geomagnetic storms better agree with measurements than that of the base vector‐based least squares. Key Points: A new approach for vertical scale height modeling from ionospheric radio occultation data using artificial neural network is proposed The new approach significantly outperforms the traditional model in accuracy The new approach agrees with the characteristic of VSH in equatorial region better than traditional approach during geomagnetic storm times