scholarly journals Assimilation of autoscaled data and regional and local ionospheric models as input sources for real-time 3-D International Reference Ionosphere modeling

Radio Science ◽  
2011 ◽  
Vol 46 (5) ◽  
pp. n/a-n/a ◽  
Author(s):  
M. Pezzopane ◽  
M. Pietrella ◽  
A. Pignatelli ◽  
B. Zolesi ◽  
L. R. Cander
Keyword(s):  
Real Time ◽  
International Reference Ionosphere ◽  
International Reference ◽  
Ionosphere Modeling ◽  
Ionospheric Models

scholarly journals International Reference Ionosphere 2016: From ionospheric climate to real-time weather predictions

Space Weather ◽  
2017 ◽  
Vol 15 (2) ◽  
pp. 418-429 ◽  
Author(s):  
D. Bilitza ◽  
D. Altadill ◽  
V. Truhlik ◽  
V. Shubin ◽  
I. Galkin ◽  
...  
Keyword(s):  
Real Time ◽  
International Reference Ionosphere ◽  
International Reference

scholarly journals Modification of the solar activity indices in the International Reference Ionosphere IRI and IRI-Plas models due to recent revision of sunspot number time series

2016 ◽  
Vol 2 (3) ◽  
pp. 59-68 ◽  
Author(s):  
Тамара Гуляева ◽  
Tamara Gulyaeva
Keyword(s):  
Time Series ◽  
Solar Activity ◽  
Sunspot Number ◽  
International Reference Ionosphere ◽  
Electron Content ◽  
Prediction Errors ◽  
Solar Cycles ◽  
Total Electron ◽  
International Reference ◽  
Ionospheric Models

The International Reference Ionosphere (IRI) imports global effective ionospheric IG12 index based on ionosonde measurements of the critical frequency foF2 as a proxy of solar activity. Similarly, the global electron content (GEC), smoothed by the sliding 12-months window (GEC12), is used as a solar proxy in the ionospheric and plasmaspheric model IRI-Plas. GEC has been calculated from global ionospheric maps of total electron content (TEC) since 1998 whereas its productions for the preceding years and predictions for the future are made with the empirical model of the linear dependence of GEC on solar activity. At present there is a need to re-evaluate solar and ionospheric indices in the ionospheric models due to the recent revision of sunspot number (SSN2) time series, which has been conducted since 1st July, 2015 [Clette et al., 2014]. Implementation of SSN2 instead of the former SSN1 series with the ionospheric model could increase model prediction errors. A formula is proposed to transform the smoothed SSN212 series to the proxy of the former basic SSN112=R12 index, which is used by IRI and IRI-Plas models for long-term ionospheric predictions. Regression relationships are established between GEC12, the sunspot number R12, and the proxy solar index of 10.7 cm microwave radio flux, F10.712. Comparison of calculations by the IRI-Plas and IRI models with observations and predictions for Moscow during solar cycles 23 and 24 has shown the advantage of implementation of GEC12 index with the IRI-Plas model.

scholarly journals Towards a Real-Time Description of the Ionosphere: A Comparison between International Reference Ionosphere (IRI) and IRI Real-Time Assimilative Mapping (IRTAM) Models

Atmosphere ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 1003
Author(s):  
Alessio Pignalberi ◽  
Marco Pietrella ◽  
Michael Pezzopane
Keyword(s):  
Real Time ◽  
Detailed Comparison ◽  
Magnetic Activity ◽  
International Reference Ionosphere ◽  
Global Network ◽  
International Reference ◽  
Accuracy And Precision ◽  
Noticeable Improvement ◽  
Time Description ◽  
Layer Peak

This paper focuses on a detailed comparison, based on the F2-layer peak characteristics foF2 and hmF2, between the International Reference Ionosphere (IRI), which is a climatological empirical model of the terrestrial ionosphere, and the IRI Real-Time Assimilative Mapping (IRTAM) procedure, which is a real-time version of IRI based on data assimilation from a global network of ionosondes. To perform such a comparison, two different kinds of datasets have been considered: (1) foF2 and hmF2 as recorded by 40 ground-based ionosondes spread all over the world from 2000 to 2019; (2) foF2 and hmF2 from space-based COSMIC/FORMOSAT-3 radio occultation measurements recorded from 2006 to 2018. The aim of the paper is to understand whether and how much IRTAM improves IRI foF2 and hmF2 outputs for different locations and under different diurnal, seasonal, solar and magnetic activity conditions. The main outcomes of the study are: (1) when ionosonde observations are considered for validation, IRTAM significantly improves the IRI foF2 modeling both in accuracy and precision, while a slight improvement in the IRI hmF2 modeling is observed for specific locations and conditions; (2) when COSMIC observations are considered for validation, no noticeable improvement is observed from the IRTAM side for both foF2 and hmF2. Indeed, IRTAM can improve the IRI foF2 description only nearby the assimilated ionosonde locations, while the IRI hmF2 description is always more accurate and precise than IRTAM one.

Predictability of Ionosphere using Assimilative Empirical Model IRTAM

2021 ◽  
Author(s):  
Ivan Galkin ◽  
Artem Vesnin ◽  
Bodo Reinisch ◽  
Dieter Bilitza
Keyword(s):  
Real Time ◽  
Space Weather ◽  
Ionospheric Plasma ◽  
International Reference Ionosphere ◽  
Time Behavior ◽  
Quiet Time ◽  
Science Data ◽  
Radio Observatory ◽  
International Reference ◽  
Ionospheric Plasma Density

<p>Real-time assimilative <em>empirical </em>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].</p><p>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 <em>Eigen</em> 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.</p><p>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.</p><p><strong>References</strong></p><p>[1] Bilitza, D. (ed.) (1990), International Reference Ionosphere 1990, 155 pages, National Space Science Data Center, NSSDC/WDC-A-R&S 90-22, Greenbelt, Maryland, November 1990.</p><p>[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.</p><p>[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.</p><p>[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</p><p>[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.</p><p>[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.</p>

scholarly journals Modification of the solar activity indices in the International Reference Ionosphere IRI and IRI-Plas models due to recent revision of sunspot number time series

2016 ◽  
Vol 2 (3) ◽  
pp. 87-98 ◽  
Author(s):  
Тамара Гуляева ◽  
Tamara Gulyaeva
Keyword(s):  
Time Series ◽  
Solar Activity ◽  
Sunspot Number ◽  
International Reference Ionosphere ◽  
Electron Content ◽  
Prediction Errors ◽  
Solar Cycles ◽  
Total Electron ◽  
International Reference ◽  
Ionospheric Models

The International Reference Ionosphere (IRI) imports global effective ionospheric IG12 index based on ionosonde measurements of the critical frequency foF2 as a proxy of solar activity. Similarly, the global electron content (GEC), smoothed by the sliding 12-months window (GEC12), is used as a solar proxy in the ionospheric and plasmaspheric model IRI-Plas. GEC has been calculated from global ionospheric maps of total electron content (TEC) since 1998 whereas its productions for the preceding years and predictions for the future are made with the empirical model of the linear dependence of GEC on solar activity. At present there is a need to re-evaluate solar and ionospheric indices in the ionospheric models due to the recent revision of sunspot number (SSN2) time series, which has been conducted since July 1, 2015 [Clette et al., 2014]. Implementation of SSN2 instead of the former SSN1 series with the ionospheric model could increase model prediction errors. A formula is proposed to transform the smoothed SSN212 series to the proxy of the former basic SSN112=R12 index, which is used by the IRI and IRI-Plas models for long-term ionospheric predictions. Regression relationships are established between GEC12, the sunspot number R12, and the proxy solar index of 10.7 cm microwave radio flux, F10.712. Comparison of calculations by the IRI-Plas and IRI models with observations and predictions for Moscow during solar cycles 23 and 24 has shown the advantage of implementation of GEC12 index with the IRI-Plas model.

scholarly journals Accuracy Analysis of International Reference Ionosphere 2016 and NeQuick2 in the Antarctic

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1551
Author(s):  
Zihuai Guo ◽  
Yibin Yao ◽  
Jian Kong ◽  
Gang Chen ◽  
Chen Zhou ◽  
...  
Keyword(s):  
Solar Radiation ◽  
International Reference Ionosphere ◽  
Electron Content ◽  
Observation Data ◽  
Dual Frequency ◽  
Total Electron ◽  
International Reference ◽  
Antarctic Continent ◽  
The Antarctic ◽  
Frequency Observation

Global navigation satellite system (GNSS) can provide dual-frequency observation data, which can be used to effectively calculate total electron content (TEC). Numerical studies have utilized GNSS-derived TEC to evaluate the accuracy of ionospheric empirical models, such as the International Reference Ionosphere model (IRI) and the NeQuick model. However, most studies have evaluated vertical TEC rather than slant TEC (STEC), which resulted in the introduction of projection error. Furthermore, since there are few GNSS observation stations available in the Antarctic region and most are concentrated in the Antarctic continent edge, it is difficult to evaluate modeling accuracy within the entire Antarctic range. Considering these problems, in this study, GNSS STEC was calculated using dual-frequency observation data from stations that almost covered the Antarctic continent. By comparison with GNSS STEC, the accuracy of IRI-2016 and NeQuick2 at different latitudes and different solar radiation was evaluated during 2016–2017. The numerical results showed the following. (1) Both IRI-2016 and NeQuick2 underestimated the STEC. Since IRI-2016 utilizes new models to represent the F2-peak height (hmF2) directly, the IRI-2016 STEC is closer to GNSS STEC than NeQuick2. This conclusion was also confirmed by the Constellation Observing System for Meteorology Ionosphere and Climate (COSMIC) occultation data. (2) The differences in STEC of the two models are both normally distributed, and the NeQuick2 STEC is systematically biased as solar radiation increases. (3) The root mean square error (RMSE) of the IRI-2016 STEC is smaller than that of the NeQuick2 model, and the RMSE of the two modeling STEC increases with solar radiation intensity. Since IRI-2016 relies on new hmF2 models, it is more stable than NeQuick2.

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