Measurements of F1- region ionosphere state variables at Arecibo through quasi height-independent exhaustive fittings of the incoherent scatter ion-line spectra
We discuss an exhaustive search approach to fit the incoherent scatter spectrum (ISS) in the F1-region for molecular ion fraction (fm), ion temperature (Ti), and electron temperature (Te). The commonly used “full profile” approach for F1-region measurements parameterizes the molecular ion fraction as a function of altitude and fits all the available heights for the state variables. In our approach, we fit the ISS at each height for fm, Ti, Te, and ion velocity (Vi) independently. Our exhaustive search method finds all the major local minima at each altitude. Although a parameterized function is used to guide the algorithm to find the best solution, the fitting parameters retain their local characteristics. Despite that fitting fm, Ti, and Te without constraints requires Doppler shift to be accurately determined and the ISS signal-to-noise ratio higher than the full-profile method, simulations show that Ti, Te, and fm can be recovered within a few percent accuracy with a moderate signal-to-noise ratio. We apply the exhaustive approach to the Arecibo high-resolution incoherent scatter radar data taken on September 13th, 2014. The derived ion and electron temperatures are sensitive enough to reveal thermosphere gravity waves that are commonly seen in the electron density previously. Our method is more robust than previous height-independent fitting methods. Comparison with another Arecibo low-resolution program indicates our results are likely more accurate. Simultaneous high-resolution measurements of Ti, Te, fm, Vi, and electron concentration (Ne) in our approach open new opportunities for synergistic studies of the F1-region dynamics and chemistry.
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Work Title | Measurements of F1- region ionosphere state variables at Arecibo through quasi height-independent exhaustive fittings of the incoherent scatter ion-line spectra |
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License | CC BY 4.0 (Attribution) |
Work Type | Dataset |
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Publication Date | 2024 |
Deposited | March 23, 2024 |
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