Home > Research > Publications & Outputs > Improving the twilight model for polar cap abso...

Associated organisational unit

Electronic data


Text available via DOI:

View graph of relations

Improving the twilight model for polar cap absorption nowcasts

Research output: Contribution to Journal/MagazineJournal articlepeer-review

<mark>Journal publication date</mark>11/2016
<mark>Journal</mark>Space Weather
Issue number11
Number of pages23
Pages (from-to)950-972
Publication StatusPublished
Early online date13/10/16
<mark>Original language</mark>English


During Solar Proton Events (SPE), energetic protons ionize the polar mesosphere causing HF radiowave attenuation, more strongly on the dayside where the effective recombination coefficient, αeff, is low. Polar cap absorption (PCA) models predict the 30 MHz cosmic noise absorption, A, measured by riometers, based on real-time measurements of the integrated proton flux-energy spectrum, J. However, empirical models in common use cannot account for regional and day-to-day variations in the day- and nighttime profiles of αeff(z) or the related sensitivity parameter, m=A/√J. Large prediction errors occur during twilight when m changes rapidly, and due to errors locating the rigidity cutoff latitude. Modeling the twilight change in m as a linear or Gauss error-function transition over a range of solar-zenith angles (χl < χ < χu) provides a better fit to measurements than selecting day or night αeff profiles based on the Earth-shadow height. Optimal model parameters were determined for several polar cap riometers for large SPEs in 1998-2005. The optimal χl parameter was found to be most variable, with smaller values (as low as 60°) post-sunrise compared with pre-sunset, and with positive correlation between riometers over a wide area. Day and night values of m exhibited higher correlation for closely spaced riometers. A nowcast simulation is presented in which rigidity boundary latitude and twilight model parameters are optimized by assimilating age-weighted measurements from 25 riometers. The technique reduces model bias, and root-mean-squared errors are reduced by up to 30% compared with a model employing no riometer data assimilation.

Bibliographic note

An edited version of this paper was published by AGU. Copyright 2016 American Geophysical Union.