Estimating the Daytime Vertical E × B Drift From Magnetometer and C/NOFS Measurements and Comparison With Empirical Models Over the East African Sector. Issue 2 (1st February 2021)

• Record Type: Journal Article
• Authors: Tilahun, Samson
• Is Part Of: Radio science. Volume 56:Issue 2(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-02-01
• Subjects: E × B drift -- PPF -- neural network; Radio meteorology -- Periodicals; Radio wave propagation -- Periodicals; 621.38405
• DOI: 10.1029/2019RS007037
• Languages: English
• ISSNs: 0048-6604

Abstract

Abstract: This paper introduces a new approach of estimating the daytime horizontal geomagnetic field (Δ H )‐induced variations of vertical E × B drift velocity and evaluates its performance using Communication/Navigation Outage Forecasting Systems (C/NOFS) over the East African sector. H field at AAE (Addis Ababa magnetometer station) and ETHI (Adigrat magnetometer station in Ethiopia) and their immediate difference alongside with C/NOFS E × B drift were used for periodic polynomial fitting (PPF) a periodic basis. Knowledge of E × B drift is of utmost importance in space weather‐related predictions since it can significantly affect ionospheric density structures and dynamics. The periodic fitting illustrates better agreement with C/NOFS observation during 16:00–19:00 LT than 10:00–16:00 LT whose root‐mean‐square error (RMSE) increased from 9.2 to 12.67 m/s, respectively. Comparison of PPF, recent regional E × B model made by Dubazane and Habarulema (2018, https://doi.org/10.1029/2018SW001820 ) (E × B D u b ) and E × B I R I (vertical E × B estimates of International Reference Ionosphere [IRI]) was carried out using C/NOFS observation. Independent model validation shows that C/NOFS E × B values are closer to PPF estimates than E × B D u b and E × B I R I over the region. Besides, the least squares model with physical inputs including local time (LT), day of the year (DOY), extreme ultraviolet (EUV) flux, and Δ H provides lowest RMSEs of about 5.36 and 7.41 m/s using a method of neural networking during low solar activity (LSA) and high solar activity (HSA), respectively. Key Points: The relationship between daytime Δ H and C/NOFS vertical E × B drift over the east African sector is done quantitatively on a periodic basis PPF model has shown better agreement with observations compared to other existing empirical models of vertical E × B Neural networking approach has shown its potential to estimate the daytime vertical E × B drift velocity in east African sector