Effective Solar Indices for Ionospheric Modeling: A Review and a Proposal for a Real-Time Regional IRI
The first part of this paper reviews methods using effective solar indices to update a background ionospheric model focusing on those employing the Kriging method to perform the spatial interpolation. Then, it proposes a method to update the International Reference Ionosphere (IRI) model through the...
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description | The first part of this paper reviews methods using effective solar indices to update a background ionospheric model focusing on those employing the Kriging method to perform the spatial interpolation. Then, it proposes a method to update the International Reference Ionosphere (IRI) model through the assimilation of data collected by a European ionosonde network. The method, called International Reference Ionosphere UPdate (IRI UP), that can potentially operate in real time, is mathematically described and validated for the period 9–25 March 2015 (a time window including the well-known St. Patrick storm occurred on 17 March), using IRI and IRI Real Time Assimilative Model (IRTAM) models as the reference. It relies on
fo
F2 and
M
(3000)F2 ionospheric characteristics, recorded routinely by a network of 12 European ionosonde stations, which are used to calculate for each station effective values of IRI indices
I
G
12
and
R
12
(identified as
I
G
12
eff
and
R
12
eff
); then, starting from this discrete dataset of values, two-dimensional (2D) maps of
I
G
12
eff
and
R
12
eff
are generated through the universal Kriging method. Five variogram models are proposed and tested statistically to select the best performer for each effective index. Then, computed maps of
I
G
12
eff
and
R
12
eff
are used in the IRI model to synthesize updated values of
fo
F2 and
hm
F2. To evaluate the ability of the proposed method to reproduce rapid local changes that are common under disturbed conditions, quality metrics are calculated for two test stations whose measurements were not assimilated in IRI UP, Fairford (51.7°N, 1.5°W) and San Vito (40.6°N, 17.8°E), for IRI, IRI UP, and IRTAM models. The proposed method turns out to be very effective under highly disturbed conditions, with significant improvements of the
fo
F2 representation and noticeable improvements of the
hm
F2 one. Important improvements have been verified also for quiet and moderately disturbed conditions. A visual analysis of
fo
F2 and
hm
F2 maps highlights the ability of the IRI UP method to catch small-scale changes occurring under disturbed conditions which are not seen by IRI. |
doi_str_mv | 10.1007/s10712-017-9438-y |
format | Article |
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fo
F2 and
M
(3000)F2 ionospheric characteristics, recorded routinely by a network of 12 European ionosonde stations, which are used to calculate for each station effective values of IRI indices
I
G
12
and
R
12
(identified as
I
G
12
eff
and
R
12
eff
); then, starting from this discrete dataset of values, two-dimensional (2D) maps of
I
G
12
eff
and
R
12
eff
are generated through the universal Kriging method. Five variogram models are proposed and tested statistically to select the best performer for each effective index. Then, computed maps of
I
G
12
eff
and
R
12
eff
are used in the IRI model to synthesize updated values of
fo
F2 and
hm
F2. To evaluate the ability of the proposed method to reproduce rapid local changes that are common under disturbed conditions, quality metrics are calculated for two test stations whose measurements were not assimilated in IRI UP, Fairford (51.7°N, 1.5°W) and San Vito (40.6°N, 17.8°E), for IRI, IRI UP, and IRTAM models. The proposed method turns out to be very effective under highly disturbed conditions, with significant improvements of the
fo
F2 representation and noticeable improvements of the
hm
F2 one. Important improvements have been verified also for quiet and moderately disturbed conditions. A visual analysis of
fo
F2 and
hm
F2 maps highlights the ability of the IRI UP method to catch small-scale changes occurring under disturbed conditions which are not seen by IRI.</description><identifier>ISSN: 0169-3298</identifier><identifier>EISSN: 1573-0956</identifier><identifier>DOI: 10.1007/s10712-017-9438-y</identifier><language>eng</language><publisher>Dordrecht: Springer Netherlands</publisher><subject>Astronomy ; Earth and Environmental Science ; Earth Sciences ; F 2 region ; foF2 ; Geophysics/Geodesy ; Interpolation ; Ionosphere ; Ionospheric models ; Kriging interpolation ; Mathematical models ; Observations and Techniques ; Real time ; Solar cycle ; Statistical analysis ; Statistical methods ; Storms</subject><ispartof>Surveys in geophysics, 2018, Vol.39 (1), p.125-167</ispartof><rights>Springer Science+Business Media B.V. 2017</rights><rights>Surveys in Geophysics is a copyright of Springer, (2017). All Rights Reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c316t-551f81a3b252ae40a1493b278be6b325e74f960f5be8c058dbafc295a97d7b603</citedby><cites>FETCH-LOGICAL-c316t-551f81a3b252ae40a1493b278be6b325e74f960f5be8c058dbafc295a97d7b603</cites><orcidid>0000-0001-9459-4919</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s10712-017-9438-y$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s10712-017-9438-y$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27901,27902,41464,42533,51294</link.rule.ids></links><search><creatorcontrib>Pignalberi, A.</creatorcontrib><creatorcontrib>Pezzopane, M.</creatorcontrib><creatorcontrib>Rizzi, R.</creatorcontrib><creatorcontrib>Galkin, I.</creatorcontrib><title>Effective Solar Indices for Ionospheric Modeling: A Review and a Proposal for a Real-Time Regional IRI</title><title>Surveys in geophysics</title><addtitle>Surv Geophys</addtitle><description>The first part of this paper reviews methods using effective solar indices to update a background ionospheric model focusing on those employing the Kriging method to perform the spatial interpolation. Then, it proposes a method to update the International Reference Ionosphere (IRI) model through the assimilation of data collected by a European ionosonde network. The method, called International Reference Ionosphere UPdate (IRI UP), that can potentially operate in real time, is mathematically described and validated for the period 9–25 March 2015 (a time window including the well-known St. Patrick storm occurred on 17 March), using IRI and IRI Real Time Assimilative Model (IRTAM) models as the reference. It relies on
fo
F2 and
M
(3000)F2 ionospheric characteristics, recorded routinely by a network of 12 European ionosonde stations, which are used to calculate for each station effective values of IRI indices
I
G
12
and
R
12
(identified as
I
G
12
eff
and
R
12
eff
); then, starting from this discrete dataset of values, two-dimensional (2D) maps of
I
G
12
eff
and
R
12
eff
are generated through the universal Kriging method. Five variogram models are proposed and tested statistically to select the best performer for each effective index. Then, computed maps of
I
G
12
eff
and
R
12
eff
are used in the IRI model to synthesize updated values of
fo
F2 and
hm
F2. To evaluate the ability of the proposed method to reproduce rapid local changes that are common under disturbed conditions, quality metrics are calculated for two test stations whose measurements were not assimilated in IRI UP, Fairford (51.7°N, 1.5°W) and San Vito (40.6°N, 17.8°E), for IRI, IRI UP, and IRTAM models. The proposed method turns out to be very effective under highly disturbed conditions, with significant improvements of the
fo
F2 representation and noticeable improvements of the
hm
F2 one. Important improvements have been verified also for quiet and moderately disturbed conditions. A visual analysis of
fo
F2 and
hm
F2 maps highlights the ability of the IRI UP method to catch small-scale changes occurring under disturbed conditions which are not seen by IRI.</description><subject>Astronomy</subject><subject>Earth and Environmental Science</subject><subject>Earth Sciences</subject><subject>F 2 region</subject><subject>foF2</subject><subject>Geophysics/Geodesy</subject><subject>Interpolation</subject><subject>Ionosphere</subject><subject>Ionospheric models</subject><subject>Kriging interpolation</subject><subject>Mathematical models</subject><subject>Observations and Techniques</subject><subject>Real time</subject><subject>Solar cycle</subject><subject>Statistical analysis</subject><subject>Statistical methods</subject><subject>Storms</subject><issn>0169-3298</issn><issn>1573-0956</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNp1kEFPAyEQhYnRxFr9Ad5IPKOwLLB4a5qqm9Roaj0TdhcqzXZZoa3pv5e6Hrx4mjd5700mHwDXBN8SjMVdJFiQDGEikMxpgQ4nYESYoAhLxk_BCBMuEc1kcQ4uYlxjjAsu6QjYmbWm3rq9gW--1QGWXeNqE6H1SfvOx_7DBFfDZ9-Y1nWreziBC7N35gvqroEavgbf-6jbn4ZOnm7R0m1MUivnu2SUi_ISnFndRnP1O8fg_WG2nD6h-ctjOZ3MUU0J3yLGiC2IplXGMm1yrEku0yKKyvCKZsyI3EqOLatMUWNWNJW2dSaZlqIRFcd0DG6Gu33wnzsTt2rtdyE9ERWRXAgsZc5TigypOvgYg7GqD26jw0ERrI441YBTJZzqiFMdUicbOjFlu5UJfy7_W_oGBDl3XQ</recordid><startdate>2018</startdate><enddate>2018</enddate><creator>Pignalberi, A.</creator><creator>Pezzopane, M.</creator><creator>Rizzi, R.</creator><creator>Galkin, I.</creator><general>Springer Netherlands</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7TG</scope><scope>7TN</scope><scope>7UA</scope><scope>7XB</scope><scope>88I</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>BKSAR</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>F1W</scope><scope>GNUQQ</scope><scope>H8D</scope><scope>H96</scope><scope>HCIFZ</scope><scope>KL.</scope><scope>L.G</scope><scope>L7M</scope><scope>M2P</scope><scope>P5Z</scope><scope>P62</scope><scope>PATMY</scope><scope>PCBAR</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PYCSY</scope><scope>Q9U</scope><orcidid>https://orcid.org/0000-0001-9459-4919</orcidid></search><sort><creationdate>2018</creationdate><title>Effective Solar Indices for Ionospheric Modeling: A Review and a Proposal for a Real-Time Regional IRI</title><author>Pignalberi, A. ; Pezzopane, M. ; Rizzi, R. ; Galkin, I.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c316t-551f81a3b252ae40a1493b278be6b325e74f960f5be8c058dbafc295a97d7b603</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Astronomy</topic><topic>Earth and Environmental Science</topic><topic>Earth Sciences</topic><topic>F 2 region</topic><topic>foF2</topic><topic>Geophysics/Geodesy</topic><topic>Interpolation</topic><topic>Ionosphere</topic><topic>Ionospheric models</topic><topic>Kriging interpolation</topic><topic>Mathematical models</topic><topic>Observations and Techniques</topic><topic>Real time</topic><topic>Solar cycle</topic><topic>Statistical analysis</topic><topic>Statistical methods</topic><topic>Storms</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Pignalberi, A.</creatorcontrib><creatorcontrib>Pezzopane, M.</creatorcontrib><creatorcontrib>Rizzi, R.</creatorcontrib><creatorcontrib>Galkin, I.</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Oceanic Abstracts</collection><collection>Water Resources Abstracts</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Science Database (Alumni Edition)</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection (ProQuest)</collection><collection>Natural Science Collection</collection><collection>Earth, Atmospheric & Aquatic Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>ProQuest Central Student</collection><collection>Aerospace Database</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>SciTech Premium Collection</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Science Database</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Environmental Science Database</collection><collection>Earth, Atmospheric & Aquatic Science Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Environmental Science Collection</collection><collection>ProQuest Central Basic</collection><jtitle>Surveys in geophysics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Pignalberi, A.</au><au>Pezzopane, M.</au><au>Rizzi, R.</au><au>Galkin, I.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Effective Solar Indices for Ionospheric Modeling: A Review and a Proposal for a Real-Time Regional IRI</atitle><jtitle>Surveys in geophysics</jtitle><stitle>Surv Geophys</stitle><date>2018</date><risdate>2018</risdate><volume>39</volume><issue>1</issue><spage>125</spage><epage>167</epage><pages>125-167</pages><issn>0169-3298</issn><eissn>1573-0956</eissn><abstract>The first part of this paper reviews methods using effective solar indices to update a background ionospheric model focusing on those employing the Kriging method to perform the spatial interpolation. Then, it proposes a method to update the International Reference Ionosphere (IRI) model through the assimilation of data collected by a European ionosonde network. The method, called International Reference Ionosphere UPdate (IRI UP), that can potentially operate in real time, is mathematically described and validated for the period 9–25 March 2015 (a time window including the well-known St. Patrick storm occurred on 17 March), using IRI and IRI Real Time Assimilative Model (IRTAM) models as the reference. It relies on
fo
F2 and
M
(3000)F2 ionospheric characteristics, recorded routinely by a network of 12 European ionosonde stations, which are used to calculate for each station effective values of IRI indices
I
G
12
and
R
12
(identified as
I
G
12
eff
and
R
12
eff
); then, starting from this discrete dataset of values, two-dimensional (2D) maps of
I
G
12
eff
and
R
12
eff
are generated through the universal Kriging method. Five variogram models are proposed and tested statistically to select the best performer for each effective index. Then, computed maps of
I
G
12
eff
and
R
12
eff
are used in the IRI model to synthesize updated values of
fo
F2 and
hm
F2. To evaluate the ability of the proposed method to reproduce rapid local changes that are common under disturbed conditions, quality metrics are calculated for two test stations whose measurements were not assimilated in IRI UP, Fairford (51.7°N, 1.5°W) and San Vito (40.6°N, 17.8°E), for IRI, IRI UP, and IRTAM models. The proposed method turns out to be very effective under highly disturbed conditions, with significant improvements of the
fo
F2 representation and noticeable improvements of the
hm
F2 one. Important improvements have been verified also for quiet and moderately disturbed conditions. A visual analysis of
fo
F2 and
hm
F2 maps highlights the ability of the IRI UP method to catch small-scale changes occurring under disturbed conditions which are not seen by IRI.</abstract><cop>Dordrecht</cop><pub>Springer Netherlands</pub><doi>10.1007/s10712-017-9438-y</doi><tpages>43</tpages><orcidid>https://orcid.org/0000-0001-9459-4919</orcidid></addata></record> |
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subjects | Astronomy Earth and Environmental Science Earth Sciences F 2 region foF2 Geophysics/Geodesy Interpolation Ionosphere Ionospheric models Kriging interpolation Mathematical models Observations and Techniques Real time Solar cycle Statistical analysis Statistical methods Storms |
title | Effective Solar Indices for Ionospheric Modeling: A Review and a Proposal for a Real-Time Regional IRI |
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