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The Limits of Empirical Electron Density Modeling: Examining the Capacity of E‐CHAIM and the IRI for Modeling Intermediate (1‐ to 30‐Day) Timescales at High Latitudes
Author(s) -
Themens David R.,
Jayachandran P. T.,
Reid Benjamin,
McCaffrey Anthony M.
Publication year - 2020
Publication title -
radio science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.371
H-Index - 84
eISSN - 1944-799X
pISSN - 0048-6604
DOI - 10.1029/2018rs006763
Subject(s) - ionosphere , storm , geomagnetic storm , atmospheric sciences , amplitude , international reference ionosphere , empirical modelling , quiet , environmental science , latitude , meteorology , polar , geology , earth's magnetic field , physics , total electron content , geodesy , geophysics , computer science , tec , programming language , astronomy , quantum mechanics , magnetic field
The Empirical Canadian High Arctic Ionospheric Model (E‐CHAIM) is a new empirical 3‐D electron density model intended as an alternative to the use of conventional standards, such as the International Reference Ionosphere (IRI), at high latitudes (above 50°N). In this study, we have manually scaled a year of data from two Canadian High Arctic Ionospheric Network (CHAIN) ionosondes. Using this high‐quality data, we examine the behavior of the polar cap ionosphere under disturbed geomagnetic conditions and assess the capacity of E‐CHAIM to model polar cap F2‐peak electron density variability on “weather‐like,” intermediate timescales (1–30 days). This is a particularly challenging environment for monthly median empirical models due to the regular occurrence of variations about the monthly mean of up to 2 MHz. We demonstrate in this study that E‐CHAIM's storm model is capable of explaining 4 to 25% of polar cap foF2 variance at 1‐ to 30‐day timescales and 5 to 50% of the amplitude of that variability, while the IRI's Storm‐Time Ionospheric Correction Model (STORM) only explains 0.2 to 9% of the variance at these timescales and no more than 5% of their amplitude. While the IRI's STORM model provided no measurable improvement over the monthly median, E‐CHAIM's storm parameterization was able to improve overall root‐mean‐square errors by 0.05 to 0.1 MHz over its quiet time model. The overall improvement through the use of storm foF2 parameterizations is found to be limited, but measurable, particularly during storm periods, where an average improvement in root‐mean‐square error of 20% is observed.