An Ionosphere Specification Technique Based on Data Ingestion Algorithm and Empirical Orthogonal Function Analysis Method
dc.contributor.author | Aa, Ercha | |
dc.contributor.author | Ridley, Aaron | |
dc.contributor.author | Huang, Wengeng | |
dc.contributor.author | Zou, Shasha | |
dc.contributor.author | Liu, Siqing | |
dc.contributor.author | Coster, Anthea J. | |
dc.contributor.author | Zhang, Shunrong | |
dc.date.accessioned | 2018-11-20T15:33:31Z | |
dc.date.available | 2019-11-01T15:10:32Z | en |
dc.date.issued | 2018-09 | |
dc.identifier.citation | Aa, Ercha; Ridley, Aaron; Huang, Wengeng; Zou, Shasha; Liu, Siqing; Coster, Anthea J.; Zhang, Shunrong (2018). "An Ionosphere Specification Technique Based on Data Ingestion Algorithm and Empirical Orthogonal Function Analysis Method." Space Weather 16(9): 1410-1423. | |
dc.identifier.issn | 1542-7390 | |
dc.identifier.issn | 1542-7390 | |
dc.identifier.uri | https://hdl.handle.net/2027.42/146373 | |
dc.description.abstract | A data ingestion method in reproducing ionospheric electron density and total electron content (TEC) was developed to incorporate TEC products from the Madrigal Database into the NeQuick 2 model. The method is based on retrieving an appropriate global distribution of effective ionization parameter (Az) to drive the NeQuick 2 model, which can be implemented through minimizing the difference between the measured and modeled TEC at each grid in the local time‐modified dip latitude coordinates. The performance of this Madrigal TEC‐driven‐NeQuick 2 result is validated through the comparison with various International Global Navigation Satellite Systems Services global ionospheric maps and ionosonde data. The validation results show that a general accuracy improvement of 30–50% can be achieved after data ingestion. In addition, the empirical orthogonal function (EOF) analysis technique is used to construct a parameterized time‐varying global Az model. The quick convergence of EOF decomposition makes it possible to use the first six EOF series to represent over 90% of the total variances. The intrinsic diurnal variation and spatial distribution in the original data set can be well reflected by the constructed EOF base functions. The associated EOF coefficients can be expressed as a set of linear functions of F10.7 and Ap indices, combined with a series of trigonometric functions with annual/seasonal variation components. The NeQuick TEC driven by EOF‐modeled Az shows 10–15% improvement in accuracy over the standard ionosphere correction algorithm in the Galileo navigation system. These preliminary results demonstrate the effectiveness of the combined data ingestion and EOF modeling technique in improving the specifications of ionospheric density variations.Key PointsThe Madrigal TEC data are ingested into the NeQuick 2 model through deriving an effective ionization parameter (Az)The Empirical Orthogonal Function (EOF) analysis technique is used to construct a parameterized time‐varying Az model to make a predictionThe TEC data ingestion and EOF modeling are effective in bringing certain systematic improvement of ionosphere now‐cast/forecast | |
dc.publisher | Darmstadt | |
dc.publisher | Wiley Periodicals, Inc. | |
dc.subject.other | NeQuick 2 model | |
dc.subject.other | EOF analysis method | |
dc.subject.other | data ingestion | |
dc.subject.other | Madrigal TEC products | |
dc.title | An Ionosphere Specification Technique Based on Data Ingestion Algorithm and Empirical Orthogonal Function Analysis Method | |
dc.type | Article | en_US |
dc.rights.robots | IndexNoFollow | |
dc.subject.hlbsecondlevel | Electrical Engineering | |
dc.subject.hlbtoplevel | Engineering | |
dc.description.peerreviewed | Peer Reviewed | |
dc.description.bitstreamurl | https://deepblue.lib.umich.edu/bitstream/2027.42/146373/1/swe20760_am.pdf | |
dc.description.bitstreamurl | https://deepblue.lib.umich.edu/bitstream/2027.42/146373/2/swe20760.pdf | |
dc.identifier.doi | 10.1029/2018SW001987 | |
dc.identifier.source | Space Weather | |
dc.identifier.citedreference | Radicella, S. M., & Leitinger, R. ( 2001 ). The evolution of the DGR approach to model electron density profiles. Advances in Space Research, 27, 35 – 40. https://doi.org/10.1016/S0273-1177(00)00138-1 | |
dc.identifier.citedreference | Mandrake, L., Wilson, B., Wang, C., Hajj, G., Mannucci, A., & Pi, X. ( 2005 ). A performance evaluation of the operational Jet Propulsion Laboratory/University of Southern California global assimilation ionospheric model (JPL/USC GAIM). Journal of Geophysical Research, 110, A12306. https://doi.org/10.1029/2005JA011170 | |
dc.identifier.citedreference | Mannucci, A. J., Wilson, B. D., Yuan, D. N., Ho, C. H., Lindqwister, U. J., & Runge, T. F. ( 1998 ). A global mapping technique for GPS‐derived ionospheric total electron content measurements. Radio Science, 33, 565 – 582. https://doi.org/10.1029/97RS02707 | |
dc.identifier.citedreference | Matsuo, T., Fedrizzi, M., Fuller‐Rowell, T. J., & Codrescu, M. V. ( 2012 ). Data assimilation of thermospheric mass density. Space Weather, 10, 05002. https://doi.org/10.1029/2012SW000773 | |
dc.identifier.citedreference | Nava, B., Coïsson, P., Miró Amarante, G., Azpilicueta, F., & Radicella, S. M. ( 2005 ). A model assisted ionospheric electron density reconstruction method based on vertical TEC data ingestion. Annales de Geophysique, 48 ( 2 ). https://doi.org/10.4401/ag-3203 | |
dc.identifier.citedreference | Nava, B., Coïsson, P., & Radicella, S. M. ( 2008 ). A new version of the NeQuick ionosphere electron density model. Journal of Atmospheric and Solar‐Terrestrial Physics, 70, 1856 – 1862. https://doi.org/10.1016/j.jastp.2008.01.015 | |
dc.identifier.citedreference | Nava, B., Radicella, S. M., & Azpilicueta, F. ( 2011 ). Data ingestion into NeQuick 2. Radio Science, 46, RS0D17. https://doi.org/10.1029/2010RS004635 | |
dc.identifier.citedreference | Nava, B., Radicella, S. M., Leitinger, R., & Coïsson, P. ( 2006 ). A near‐real‐time model‐assisted ionosphere electron density retrieval method. Radio Science, 41, RS6S16. https://doi.org/10.1029/2005RS003386 | |
dc.identifier.citedreference | Nigussie, M., Radicella, S. M., Damtie, B., Yizengaw, E., Nava, B., & Roininen, L. ( 2016 ). Validation of NeQuick TEC data ingestion technique against C/NOFS and EISCAT electron density measurements. Radio Science, 51, 905 – 917. https://doi.org/10.1002/2015RS005930 | |
dc.identifier.citedreference | Pi, X., Mannucci, A. J., Iijima, B. A., Wilson, B. D., Komjathy, A., Runge, T. F., et al. ( 2009 ). Assimilative modeling of ionospheric disturbances with FORMOSAT‐3/COSMIC and ground‐based GPS measurements. Terrestrial, Atmospheric and Oceanic Sciences, 20, 273 – 285. | |
dc.identifier.citedreference | Pi, X., Wang, C., Hajj, G. A., Rosen, G., Wilson, B. D., & Bailey, G. J. ( 2003 ). Estimation of E× B drift using a global assimilative ionospheric model: An observation system simulation experiment. Journal of Geophysical Research, 108, 1075. https://doi.org/10.1029/2001JA009235 | |
dc.identifier.citedreference | Rawer, K. ( 1963 ). Propagation of decameter waves (HF band). In Meteorological and Astronomical Influences on Radio Wave Propogation B Landmark (pp. 221 – 250 ). | |
dc.identifier.citedreference | Rideout, W., & Coster, A. ( 2006 ). Automated GPS processing for global total electron content data. GPS Solutions, 10 ( 3 ), 219 – 228. https://doi.org/10.1007/s10291-006-0029-5 | |
dc.identifier.citedreference | Schaer, S. ( 1999 ). Mapping and Predicting the Earth’s Ionosphere Using the Global Positioning System, (Phd dissertation). Astronomical Institute, University of Berne, Berne, Switzerland, 25 March. | |
dc.identifier.citedreference | Scherliess, L., Schunk, R. W., Sojka, J. J., & Thompson, D. C. ( 2004 ). Development of a physics‐based reduced state Kalman filter for the ionosphere. Radio Science, 39, RS1S04. https://doi.org/10.1029/2002RS002797 | |
dc.identifier.citedreference | Scherliess, L., Schunk, R. W., Sojka, J. J., Thompson, D. C., & Zhu, L. ( 2006 ). Utah State University global assimilation of ionospheric measurements Gauss‐Markov Kalman filter model of the ionosphere: Model description and validation. Journal of Geophysical Research, 111, A11315. https://doi.org/10.1029/2006JA011712 | |
dc.identifier.citedreference | Schunk, R. W., Scherliess, L., Eccles, V., Gardner, L. C., Sojka, J. J., Zhu, L., et al. ( 2014 ). Ensemble modeling with data assimilation models: A new strategy for space weather specifications, forecasts, and science. Space Weather, 12, 123 – 126. https://doi.org/10.1002/2014SW001050 | |
dc.identifier.citedreference | Schunk, R. W., Scherliess, L., Sojka, J. J., Thompson, D. C., Anderson, D. N., Codrescu, M., et al. ( 2004 ). Global assimilation of ionospheric measurements (GAIM). Radio Science, 39, RS1S02. https://doi.org/10.1029/2002RS002794 | |
dc.identifier.citedreference | Schunk, R. W., Scherliess, L., Sojka, J. J., Thompson, D., & Zhu, L. ( 2005 ). Ionospheric weather forecasting on the horizon. Space Weather, 3, S08007. https://doi.org/10.1029/2004SW000138 | |
dc.identifier.citedreference | Singer, W., & Dvinskikh, N. I. ( 1991 ). Comparison of empirical models of ionospheric characteristics developed by means of different mapping methods. Advances in Space Research, 11, 3 – 6. https://doi.org/10.1016/0273-1177(91)90311-7 | |
dc.identifier.citedreference | Vierinen, J., Coster, A. J., Rideout, W. C., Erickson, P. J., & Norberg, J. ( 2016 ). Statistical framework for estimating GNSS bias. Atmospheric Measurement Techniques, 9, 1303 – 1312. https://doi.org/10.5194/amt-9-1303-2016 | |
dc.identifier.citedreference | Wang, C., Hajj, G., Pi, X., Rosen, I. G., & Wilson, B. ( 2004 ). Development of the global assimilative ionospheric model. Radio Science, 39, RS1S06. https://doi.org/10.1029/2002RS002854 | |
dc.identifier.citedreference | Yu, X., Zhen, W., Xiong, B., She, C., Ou, M., Xu, J., et al. ( 2015 ). The performance of ionospheric correction based on NeQuick 2 model adaptation to Global Ionospheric Maps. Advances in Space Research, 55, 1741 – 1747. https://doi.org/10.1016/j.asr.2015.01.011 | |
dc.identifier.citedreference | Yue, X., Schreiner, W. S., Kuo, Y.‐H., Hunt, D. C., Wang, W., Solomon, S. C., et al. ( 2012 ). Global 3‐D ionospheric electron density reanalysis based on multisource data assimilation. Journal of Geophysical Research, 117, A09325. https://doi.org/10.1029/2012JA017968 | |
dc.identifier.citedreference | Yue, X., Schreiner, W. S., Lin, Y.‐C., Rocken, C., Kuo, Y.‐H., & Zhao, B. ( 2011 ). Data assimilation retrieval of electron density profiles from radio occultation measurements. Journal of Geophysical Research, 116, A03317. https://doi.org/10.1029/2010JA015980 | |
dc.identifier.citedreference | Zhu, L., Schunk, R., Scherliess, L., & Eccles, V. ( 2012 ). Importance of data assimilation technique in defining the model drivers for the space weather specification of the high‐latitude ionosphere. Radio Science, 47, RS0L24. https://doi.org/10.1029/2011RS004936 | |
dc.identifier.citedreference | Aa, E., Huang, W., Yu, S., Liu, S., Shi, L., Gong, J., et al. ( 2015 ). A regional ionospheric TEC mapping technique over China and adjacent areas on the basis of data assimilation. Journal of Geophysical Research: Space Physics, 120, 5049 – 5061. https://doi.org/10.1002/2015JA021140 | |
dc.identifier.citedreference | Aa, E., Liu, S., Huang, W., Shi, L., Gong, J., Chen, Y., et al. ( 2016 ). Regional 3‐D ionospheric electron density specification on the basis of data assimilation of ground‐based GNSS and radio occultation data. Space Weather, 14, 433 – 448. https://doi.org/10.1002/2016SW001363 | |
dc.identifier.citedreference | Angling, M. J., & Cannon, P. S. ( 2004 ). Assimilation of radio occultation measurements into background ionospheric models. Radio Science, 39, RS1S08. https://doi.org/10.1029/2002RS002819 | |
dc.identifier.citedreference | Angling, M. J., & Khattatov, B. ( 2006 ). Comparative study of two assimilative models of the ionosphere. Radio Science, 41, RS5S20. https://doi.org/10.1029/2005RS003372 | |
dc.identifier.citedreference | Bidaine, B., & Warnant, R. ( 2011 ). Ionosphere modelling for Galileo single frequency users: Illustration of the combination of the NeQuick model and GNSS data ingestion. Advances in Space Research, 47, 312 – 322. https://doi.org/10.1016/j.asr.2010.09.001 | |
dc.identifier.citedreference | Bilitza, D. ( 2001 ). International reference ionosphere 2000. Radio Science, 36 ( 2 ), 261 – 275. https://doi.org/10.1029/2000RS002432 | |
dc.identifier.citedreference | Bilitza, D., & Reinisch, B. W. ( 2008 ). International reference ionosphere 2007: Improvements and new parameters. Advances in Space Research, 42, 599 – 609. https://doi.org/10.1016/j.asr.2007.07.048 | |
dc.identifier.citedreference | Brunini, C., Azpilicueta, F., Gende, M., Camilion, E., Ángel, A. A., Hernandez‐Pajares, M., et al. ( 2011 ). Ground‐ and space‐based GPS data ingestion into the NeQuick model. Journal of Geodesy, 85, 931 – 939. https://doi.org/10.1007/s00190-011-0452-4 | |
dc.identifier.citedreference | Bust, G. S., Crowley, G., Garner, T. W., Gaussiran, T. L., Meggs, R. W., Mitchell, C. N., et al. ( 2007 ). Four‐dimensional GPS imaging of space weather storms. Space Weather, 5, 02003. https://doi.org/10.1029/2006SW000237 | |
dc.identifier.citedreference | Bust, G. S., Garner, T. W., & Gaussiran, T. L. ( 2004 ). Ionospheric Data Assimilation Three‐Dimensional (IDA3D): A global, multisensor, electron density specification algorithm. Journal of Geophysical Research, 109, A11312. https://doi.org/10.1029/2003JA010234 | |
dc.identifier.citedreference | Coïsson, P., Radicella, S. M., Leitinger, R., & Nava, B. ( 2006 ). Topside electron density in IRI and NeQuick: Features and limitations. Advances in Space Research, 37, 937 – 942. https://doi.org/10.1016/j.asr.2005.09.015 | |
dc.identifier.citedreference | Dvinskikh, N. I. ( 1988 ). Expansion of ionospheric characteristics fields in empirical orthogonal functions. Advances in Space Research, 8, 179 – 187. https://doi.org/10.1016/0273-1177(88)90238-4 | |
dc.identifier.citedreference | Feltens, J. ( 2007 ). Development of a new three‐dimensional mathematical ionosphere model at European Space Agency/European Space Operations Centre. Space Weather, 5, S12002. https://doi.org/10.1029/2006SW000294 | |
dc.identifier.citedreference | Feltens, J., & Schaer, S. ( 1998 ). IGS products for the ionosphere, IGS position paper the IGS analysis centers workshop. Germany: Darmstadt. | |
dc.identifier.citedreference | Fuller‐Rowell, T, Araujo‐Pradere, E., Minter, C., Codrescu, M., Spencer, P., Robertson, D., et al. ( 2006 ). US‐TEC: A new data assimilation product from the Space Environment Center characterizing the ionospheric total electron content using real‐time GPS data. Radio Science, 41, RS6003. https://doi.org/10.1029/2005RS003393 | |
dc.identifier.citedreference | Galkin, I. A., Reinisch, B. W., Huang, X., & Bilitza, D. ( 2012 ). Assimilation of GIRO data into a real‐time IRI. Radio Science, 47, RS0L07. https://doi.org/10.1029/2011RS004952 | |
dc.identifier.citedreference | Giovanni, D. G., & Radicella, S. M. ( 1990 ). An analytical model of the electron density profile in the ionosphere. Advances in Space Research, 10, 27 – 30. https://doi.org/10.1016/0273-1177(90)90301-F | |
dc.identifier.citedreference | Hernández‐Pajares, M., Juan, J. M., & Sanz, J. ( 1999 ). New approaches in global ionospheric determination using ground GPS data. Journal of Atmospheric and Solar‐Terrestrial Physics, 61, 1237 – 1247. https://doi.org/10.1016/S1364-6826(99)00054-1 | |
dc.identifier.citedreference | Hernández‐Pajares, M., Juan, J. M., Sanz, J., Orus, R., Garcia‐Rigo, A., Feltens, J., et al. ( 2009 ). The IGS VTEC maps: A reliable source of ionospheric information since 1998. Journal of Geodesy, 83, 263 – 275. https://doi.org/10.1007/s00190-008-0266-1 | |
dc.identifier.citedreference | Jolliffe, I. T. ( 1990 ). Principal component analysis: A beginner’s guide ‐ I. Introduction and application. Weather, 45, 375 – 382. https://doi.org/10.1002/j.1477-8696.1990.tb05558.x | |
dc.identifier.citedreference | Komjathy, A., Sparks, L., Wilson, B. D., & Mannucci, A. J. ( 2005 ). Automated daily processing of more than 1000 ground‐based GPS receivers for studying intense ionospheric storms. Radio Science, 40, RS6006. https://doi.org/10.1029/2005RS003279 | |
dc.identifier.citedreference | Komjathy, A., Wilson, B., Pi, X., Akopian, V., Dumett, M., Iijima, B., et al. ( 2010 ). JPL/USC GAIM: On the impact of using COSMIC and ground‐based GPS measurements to estimate ionospheric parameters. Journal of Geophysical Research, 115, A02307. https://doi.org/10.1029/2009JA014420 | |
dc.identifier.citedreference | Lee, I. T., Matsuo, T., Richmond, A. D., Liu, J. Y., Wang, W., Lin, C. H., et al. ( 2012 ). Assimilation of FORMOSAT‐3/COSMIC electron density profiles into a coupled thermosphere/ionosphere model using ensemble Kalman filtering. Journal of Geophysical Research, 117, A10318. https://doi.org/10.1029/2012JA017700 | |
dc.identifier.citedreference | Leitinger, R., Zhang, M., & Radicella, S. M. ( 2005 ). An improved bottomside for the ionospheric electron density model NeQuick. Annales de Geophysique, 48 ( 3 ). https://doi.org/10.4401/ag-3217 | |
dc.identifier.citedreference | Li, Z., Yuan, Y., Wang, N., Hernandez‐Pajares, M., & Huo, X. ( 2015 ). SHPTS: Towards a new method for generating precise global ionospheric TEC map based on spherical harmonic and generalized trigonometric series functions. Journal of Geodesy, 89, 331 – 345. https://doi.org/10.1007/s00190-014-0778-9 | |
dc.owningcollname | Interdisciplinary and Peer-Reviewed |
Files in this item
Remediation of Harmful Language
The University of Michigan Library aims to describe library materials in a way that respects the people and communities who create, use, and are represented in our collections. Report harmful or offensive language in catalog records, finding aids, or elsewhere in our collections anonymously through our metadata feedback form. More information at Remediation of Harmful Language.
Accessibility
If you are unable to use this file in its current format, please select the Contact Us link and we can modify it to make it more accessible to you.