Open AccessExamination of the relationship between riometer‐derived absorption and the integral proton flux in the context of modeling polar cap absorption
Author(s) -
Fiori R. A. D.,
Danskin D. W.
Publication year - 2016
Publication title -
space weather
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.254
H-Index - 56
ISSN - 1542-7390
DOI - 10.1002/2016sw001461
Subject(s) - riometer , polar , geomagnetic latitude , earth's magnetic field , computational physics , ionosphere , absorption (acoustics) , attenuation coefficient , flux (metallurgy) , ionospheric absorption , atmospheric sciences , physics , geophysics , magnetic field , chemistry , optics , astronomy , organic chemistry , quantum mechanics
Energetic protons can penetrate into the ionosphere increasing ionization in the D region causing polar cap absorption that may potentially block high‐frequency radio communications for transpolar flights. The protons are guided by the geomagnetic field into the high‐latitude polar cap region. Riometers monitor variations in ionospheric absorption by observing the level of background cosmic radio noise. Current polar cap absorption modeling techniques are based on the linear relationship between absorption and the square root of the integral proton flux, which has previously only been demonstrated using data from a single high‐latitude polar station. The proportionality constant describing this relationship is evaluated for two different polar cap absorption events occurring 7–11 March 2012 and 23 January 2012 to 1 February 2012. Examination of the proportionality constant using data from riometers distributed between 60° and 90° magnetic latitude reveals a previously unreported latitudinal dependence for data at magnetic latitudes of ≤66.8° on the dayside and ≤70.8° on the nightside. Incorporating the latitudinal dependence into the current D Region Absorption Prediction absorption model improves the agreement between measurement‐derived and modeled parameters by increasing the correlation coefficient between data sets, reducing the root‐mean‐square error, and reducing the bias.