Predictability of Ionosphere using Assimilative Empirical Model IRTAM
Authors: Galkin Ivan , Vesnin Artem , Reinisch Bodo , Bilitza Dieter 
Published: March 2021

&lt;p&gt;Real-time assimilative &lt;em&gt;empirical &lt;/em&gt;models based on the International Reference Ionosphere (IRI) [1], a 3D quiet-time climatology model of the ionospheric plasma density, provide prompt weather specification by adjusting IRI definitions into a better match with the available measurements and geospace activity indicators [2]. The IRI-based Real-Time Assimilative Model (IRTAM) [3] is one of such Real-Time IRI operational ionospheric weather models based on the low-latency sensor inputs from the Global Ionosphere Radio Observatory (GIRO) [4].&lt;/p&gt;&lt;p&gt;IRTAM leverages predictive properties of the underlying IRI expansion basis formalism [5] that treats dynamics of the ionospheric plasma in terms of its harmonics, both temporal and spatial. It uses Non-linear Error Compensation Technique with Associative Restoration (NECTAR) technique [6] to first detect multi-scale inherent diurnal periodicity of the differences between GIRO measurements and the underlying IRI climatology. Then, under the assumption that variations in time at periodic, planetary-scale &lt;em&gt;Eigen&lt;/em&gt;&amp;#160;scales (diurnal, half-diurnal, 8-hour, etc.) translate to their spatial properties, it globally interpolates and extrapolates each diurnal harmonic individually. This approach allowed NECTAR to associate observed fragments of the activity at GIRO locations with the unveiling grand-scale weather processes of the matching variability scales, as the ground observatories co-rotate with the Earth.&lt;/p&gt;&lt;p&gt;Predictive properties of IRTAM are discussed in order to establish the baseline predictability of the ionospheric dynamics that analyzes only the latest 24-hour history of its deviation from the expected behavior. Concepts for the next generation empirical forecast models are outlined that would leverage the same principle of associative restoration to evaluate the geospace activity timeline and its subtle associations with subsequent storm-time behavior of the ionosphere.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;References&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;[1] Bilitza, D. (ed.) (1990), International Reference Ionosphere 1990, 155 pages, National Space Science Data Center, NSSDC/WDC-A-R&amp;S 90-22, Greenbelt, Maryland, November 1990.&lt;/p&gt;&lt;p&gt;[2] Bilitza, D., D. Altadill, V. Truhlik, V. Shubin, I. Galkin, B. Reinisch, and X. Huang (2017), International Reference Ionosphere 2016: From ionospheric climate to real-time weather predictions, Space Weather, 15, 418-429, doi:10.1002/2016SW001593.&lt;/p&gt;&lt;p&gt;[3] Galkin, I. A., B. W. Reinisch, X. Huang, and D. Bilitza (2012), Assimilation of GIRO Data into a Real-Time IRI, Radio Sci., 47, RS0L07, doi:10.1029/2011RS004952.&lt;/p&gt;&lt;p&gt;[4] Reinisch, B.W. and I.A. Galkin (2011), Global Ionospheric Radio Observatory (GIRO), Earth Planets Space, vol. 63 no. 4 pp. 377-381, doi:10.5047/eps.2011.03.001&lt;/p&gt;&lt;p&gt;[5] International Telecommunications Union (2009), ITU-R reference ionospheric characteristics, Recommendation P.1239-2 (10/2009). Retrieved from http://www.itu.int/rec/R-REC-P.1239/en.&lt;/p&gt;&lt;p&gt;[6] Galkin, I. A., B. W. Reinisch, A. Vesnin, et al., (2020) Assimilation of Sparse Continuous Near-Earth Weather Measurements by NECTAR Model Morphing, Space Weather, 18, e2020SW002463, doi:10.1029/2020SW002463.&lt;/p&gt;