Applications and Data Analysis Tools

  • Space Weather Technology, Research, and Education Center. SWx TREC Space Weather Data Portal. Laboratory for Atmospheric and Space Physics, 2019. https://doi.org/10.25980/NMFX-XX89.

Conference Summaries

Deep Learning Laboratory

  • Acciarini, G., Brown, E., Berger, T., Guhathakurta, M., Parr, J.,  Bridges, C., Güneş Baydin, A. 2024. Improving Thermospheric Density Predictions in Low-Earth Orbit With Machine Learning. AGU Space Weather, 22, 2. https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2023SW003652
  • Breitsch, B., Morton, Y., Xu, D., Yang, R. 2020. Triple-Frequency GNSS Cycle Slip Detection Performance in the Presence of Diffractive Ionosphere Scintillation. Proceedings of IEEE/ION PLANS, 263-269. Virtual.
  • Breitsch, B., Morton, Y., Xu, D., Yang, R. 2020. Ionosphere scintillation-induced phase transitions in triple-frequency GPS measurements. Proceedings of ION ITM/PTTI, San Diego, CA.
  • Breitsch, B., Wang, Y., Morton, Y. 2020. Performance of cycle slip filtering algorithm during ionosphere scintillation. Proceedings of ION GNSS+, 3132-3139. Virtual, Sept.
  • Camporeale, E. 2023. Artificial Intelligence for Space Weather Forecasting. In Artificial Intelligence for Space, AI4SPACE, pp. 190-213. CRC Press, 2023.
  • Chierichini, S., Francisco, G., Mugatwala, R., Foldes, R., Camporeale, E., De Gasperis, G., Luca  Giovannelli, Napoletano, G., Del Moro, D., Erdelyi, R. 2024. A Bayesian approach to the drag-based modelling of ICMEs. J. Space Weather Space Clim., 14, 1, DOI: 10.1051/swsc/2023032
  • Deshmukh, V., Baskar, S., Berger, T.E., Bradley, E., Meiss, J. 2023. Comparing feature sets and machine learning models for prediction of solar flares: Topology, physics, and model complexity. Astronomy & Astrophysics [Preprint]. https://doi.org/10.1051/0004-6361/202245742
  • Deshmukh, V., Berger, T. E., Bradley, E., et. al. 2020. Leveraging the Mathematics for Shape for Solar Magnetic Eruption Prediction, Journal of Space Weather and Space Climate, 10, 13. https://doi.org/10.1051/swsc/2020014
  • Deshmukh, V., Berger, T., Meiss, J., Bradley, E. 2021. Shape-based Feature Engineering for Solar Flare Prediction, Thirty-Third AAAI Conference on Innovative Applications of Artificial Intelligence.
  • Deshmukh, V., Flyer, N., van der Sande, K., Berger, T. 2021. Decreasing False Alarm Rates in ML-based Solar Flare Prediction using SDO/HMI Data, Astrophysical Journal Supplement Series, submitted.
  • Foldes, R., Camporeale, E., and Marino, R. 2023. Low-dimensional representation of intermittent geophysical turbulence with High-Order Statistics-informed Neural Networks. Physics of Fluids. In Press.
  • Jakowski, N., Hoque, M.M, Morton, Y. 2021. Instantaneous GNSS-based indices for estimating spatial structures and dynamics of the ionosphere. Proceedings of COSPAR, Jan.-Feb. Virtual, Jan-Feb. 
  • Knipp, D., and Mannucci, A. 2020. Table of contents in Meeting artifacts from Chapman Conference on scientific challenges pertaining to space weather forecasting including extremes (Version V1.0), [Data set]. Zenodo. http://doi.org/10.5281/zenodo.3693004
  • Krier, W. and Morton, Y. 2020. Conjugate transfer function compensation of ionospheric refractive effects. Proceedings of IEEE/ION PLANS, 259-262. Virtual.
  • Liu, L.,  Morton, Y. J., Liu, Y. 2021. Machine learning prediction of storm-time high latitude ionospheric irregularities from GNSS-derived ROTI maps. Geophy. Res. Lett., DOI: 10.1029/2021GL095561.  
  • Liu, Y., Yang, Z., Morton, Y. J., Li, R. 2021. Spatiotemporal deep learning network for high-latitude ionospheric phase scintillation forecasting. Proc. ION GNSS+.
  • Morton, Y., Liu, Y., Yang, Z., et al. 2020. Expected and unexpected findings in mining massive GNSS data for ionospheric effects. Proceedings of European Navigation Conference. Virtual.
  • Sun, A. K., Chang, H., Pullen, S., Kil, H., Seo, J., Morton, Y. J., & Lee, J. 2021. Markov Chain‐Based Stochastic Modeling of Deep Signal Fading: Availability Assessment of Dual‐Frequency GNSS‐Based Aviation Under Ionospheric Scintillation. Space Weather, 19, 9, e2020SW002655.
  • Van Der Sande, K. et al. 2022. Solar flare catalog based on SDO/AIA EUV images: Composition and correlation with GOES/XRS X-ray flare magnitudes. Frontiers in Astronomy and Space Sciences, 9, 1031211. https://doi.org/10.3389/fspas.2022.1031211.
  • Yang, R., Zhan, X, Huang, J., Morton, Y. 2020. GNSS multi-frequency carrier tracking with cycle slip detection and mitigation under strong ionosphere scintillation. Proceedings of ION GNSS+, 2795-2802. Virtual, Sept. 2020.
  • Yang, Z., Morton, Y. 2020. Time lags in ionospheric scintillation response to geomagnetic storms: Alaska observations,” Proceedings of ION GNSS+, 3494-3501. Virtual, Sept.
  • Yang, Z., Mrak,S., Morton, Y. 2020.  Geomagnetic storm induced mid-latitude ionospheric plasma irregularities and their implications for GPS positioning over North America: a case study. Proceedings of IEEE/ION PLANS, Virtual.
  • Yun J., Park, B., Morton, J. 2020. Detecting ionospheric irregularity based on ROT variation using Android devices cloud system. Proceedings of ION GNSS+, 1850-1872. Virtual, Sept.

Education

Global Electric and Magnetic Perturbations

Global Navigation Satellite Systems (GNSS) Impacts

  • Breitsch, B., Morton, Y., Rino, C., Xu, D.. 2020. GNSS carrier phase transitions due to diffractive ionosphere scintillation: simulation and characterization. IEEE Trans. Aero. Elec. Sys., 56(5).  DOI:10.1109/TAES.2020.2979025.
  • Breitsch, B., Morton, Y., Xu, D., Yang, R. 2020. Ionosphere scintillation-induced phase transitions in triple-frequency GPS measurements. Proceedings of ION ITM/PTTI, San Diego, CA.
  • Breitsch, B., Morton, Y., Xu, D., Yang, R. 2020. Triple-Frequency GNSS Cycle Slip Detection Performance in the Presence of Diffractive Ionosphere Scintillation. Proceedings of IEEE/ION PLANS, 263-269. Virtual.
  • Breitsch, B., Wang, Y., Morton, Y. 2020. Performance of cycle slip filtering algorithm during ionosphere scintillation. Proceedings of ION GNSS+, 3132-3139. Virtual, Sept.
  • Hysell, D. L., Rojas, E., Goldberg, H., Milla, M. A., Kuyeng, K., Valdez, A., ... and Bourne, H. 2021. Mapping irregularities in the postsunset equatorial ionosphere with an expanded network of HF beacons. Journal of Geophysical Research: Space Physics, 126(7), e2021JA029229.
  • Jakowski, N., Hoque, M.M, Morton, Y. 2021. Instantaneous GNSS-based indices for estimating spatial structures and dynamics of the ionosphere. Proceedings of COSPAR, Jan.-Feb. Virtual, Jan-Feb.
  • Krier, W. and Morton, Y. 2020. Conjugate transfer function compensation of ionospheric refractive effects. Proceedings of IEEE/ION PLANS, 259-262. Virtual.
  • Morton, Y., Liu, Y., Yang, Z., et al. 2020. Expected and unexpected findings in mining massive GNSS data for ionospheric effects. Proceedings of European Navigation Conference. Virtual.
  • Morton, Y., Yang. Z, Breitsch. B, et al. 2020. Ionospheric Effects, Monitoring, and Mitigation Techniques, in Position, Navigation, and Timing Technologies, in the 21st Century: Integrated Satellite Navigation, Sensor Systems, and Civil Applications, 1, Wiley-IEEE Press (Eds. Y. J. Morton, F. van Diggelen, J. J., Spilker, B. Parkinson, et al.). Ch. 31. https://doi.org/10.1002/9781119458449.ch31.
  • Rino, C., Breitsch, B., Morton, Y., et al. 2020. GNSS signal phase, TEC, and phase scintillation. Navigation, J. Institute of Navigation, 67(4). http://doi.org/10.1002/navi.396.
  • Sun, K., Lee, J., Seo, J., Morton, Y., Pullen, S. 2020. Performance benefit from dual-frequnecy GNSS-based aviation applications under ionospheric scintillation: a new approach on fading process modeling. Proceedings of ION ITM/PTTI, San Diego, CA.
  • Wang, Y., and Morton, Y. J. 2021. Ionospheric Total Electron Content and Disturbance Observations From Space-Borne Coherent GNSS-R Measurements. IEEE Transactions on Geoscience and Remote Sensing.
  • Xu, D., Morton, Y., Rino, C., et al. 2020. A two-parameter multifrequency GPS signal simulator for strong equatorial ionospheric scintillation: modeling and parameter characterization. Navigation, J. Institute of Navigation, 67(1), 181-195. http://doi.org/10.1002/navi.350.
  • Yang, R., Zhan, X, Huang, J., Morton, Y. 2020. GNSS multi-frequency carrier tracking with cycle slip detection and mitigation under strong ionosphere scintillation. Proceedings of ION GNSS+, 2795-2802. Virtual, Sept. 2020.
  • Yang, Z., Morton, Y, Zakharenkova, I., et al. 2020. Global view of ionospheric disturbances impacts on kinematic GPS positioning solutions during the 2015 St. Patrick’s Day storm. J. Geophy. Res., Space Sci., 125(7). DOI: 10.1029/2019JA027681.
  • Yang, Z., Morton, Y. 2020. Low-latitude ionospheric scintillations of multi-constellation GNSS signals in relation to magnetic field orientation. J. of Geodesy, 94(59), 1-15. https://doi.org/10.1007/s00190-020-01391-7.
  • Yang, Z., Morton, Y. 2020. Time lags in ionospheric scintillation response to geomagnetic storms: Alaska observations,” Proceedings of ION GNSS+, 3494-3501. Virtual, Sept.
  • Yang, Z., Mrak,S., Morton, Y. 2020.  Geomagnetic storm induced mid-latitude ionospheric plasma irregularities and their implications for GPS positioning over North America: a case study. Proceedings of IEEE/ION PLANS, Virtual.
  • Yun J., Park, B., Morton, J. 2020. Detecting ionospheric irregularity based on ROT variation using Android devices cloud system. Proceedings of ION GNSS+, 1850-1872. Virtual, Sept.

Missions and Instruments

Research-to-Operations / Operations-to-Research

  • Cheng, C.-C., Fuller-Rowell, T., Fang, T.-W., Sutton, E.K., Liu J.-Y. 2004. Thermospheric Neutral Density Data Assimilation System Based on the Whole Atmosphere Model during the November 2003 Storm. Space Weather, In Preparation.
  • Cosgrove, R. B., Bahcivan, H., Chen, S., Sanchez, E., & Knipp, D. 2022. Violation of Hemispheric Symmetry in Integrated Poynting Flux via an Empirical Model. Geophysical Research Letters, 49, e2021GL097329. https://doi.org/10.1029/2021GL097329
  • Rowland, W., Codrescu, S., Plummer, T., et. al. 2020. Feasibility of near-real-time GOLD data products. Journal of Geophysical Research. https://doi.org/10.1029/2020JA027819

Solar Physics

  • Consuelo, C, Saiz, E., Flores-Soriano, M., and Knipp. 2023. Interplanetary signatures during the early August 1972 solar storms. ApJ, 958 159. https://iopscience.iop.org/article/10.3847/1538-4357/acf9fd
  • Deshmukh, V., Baskar, S., Berger, T.E., Bradley, E., Meiss, J. 2023. Comparing feature sets and machine learning models for prediction of solar flares: Topology, physics, and model complexity. Astronomy & Astrophysics [Preprint]. https://doi.org/10.1051/0004-6361/202245742
  • Deshmukh, V., Berger, T. E., Bradley, E., et. al. 2020. Leveraging the Mathematics for Shape for Solar Magnetic Eruption Prediction, Journal of Space Weather and Space Climate, 10, 13. https://doi.org/10.1051/swsc/2020014
  • Deshmukh, V., Berger, T., Meiss, J., Bradley, E. 2021. Shape-based Feature Engineering for Solar Flare Prediction, Thirty-Third AAAI Conference on Innovative Applications of Artificial Intelligence.
  • Deshmukh, V., Flyer, N., van der Sande, K., Berger, T. 2021. Decreasing False Alarm Rates in ML-based Solar Flare Prediction using SDO/HMI Data, Astrophysical Journal Supplement Series, submitted.
  • Judge, P., Rempel, M., Ezzeddine, R., Berger, T. et al. 2021. Measuring the Magnetic Origins of Solar Flares, Coronal Mass Ejections, and Space Weather. ApJ, 917 27. https://iopscience.iop.org/article/10.3847/1538-4357/ac081f
  • Plowman, J., and Berger, T. 2020a. Calibrating GONG magnetograms with end-to-end instrument simulation I. Background, the GONG instrument, and end-to-end simulation. Solar Physics, (in review).
  • Plowman, J., and Berger, T. 2020b. Calibrating GONG magnetograms with end-to-end instrument simulation II. Theory of calibration. Solar Physics, (in review).
  • Plowman, J., and Berger, T. 2020c. Calibrating GONG magnetograms with end-to-end instrument simulation III. Comparison, calibration, and results. Solar Physics, (in press).
  • Rast, M. P., Gonzales, N.G.,…Berger T.E., et al. 2021. Critical Science Plan for the Daniel K. Inouye Solar Telescope (DKIST). Sol Phys., 296, 70. https://doi.org/10.1007/s11207-021-01789-2
  • Usoskin, I., Miyake, F., Baroni, M., Dalla, S., Hayakawa, H., Hudson, H., Knipp, D., Koldobskiy, S., Maehara, H., Mekhaldi, F., Notsu, Y., Poluianov, S., Rozanov, E., Spiegl, T., Sukhodolov, T. 2023. Extreme Solar Events: Setting up a paradigm. Space Sci Rev, 219, 73 (2023). https://doi.org/10.1007/s11214-023-01018-1

Space Domain Science

  • Acciarini, G., Brown, E., Berger, T., Guhathakurta, M., Parr, J.,  Bridges, C., Güneş Baydin, A. 2024. Improving Thermospheric Density Predictions in Low-Earth Orbit With Machine Learning. AGU Space Weather, 22, 2. https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2023SW003652
  • Berger, T.E., Dominique, M., Lucas, G., Pilinski, M., Ray, V., Sewell, R. Sutton, E., Thayer, J.P., Thiemann, E. 2023. The Thermosphere Is a Drag: The 2022 Starlink Incident and the Threat of Geomagnetic Storms to Low Earth Orbit Space Operations. Space Weather, 21(3), p. e2022SW003330. https://doi.org/10.1029/2022SW003330.
  • Berger, T. E., Holzinger, M. J., Sutton, E. S., et. al. 2020. Flying through uncertainty. Space Weather, 18(1). http://doi.org/10.1029/2019SW002373
  • Bernstein, V., Pilinski, M. D., Sutton, E. K. 2021. Assessing Thermospheric Densities Derived from Orbital Drag Data. Proceedings of the 31st AAS/AIAA SpaceFlight Mechanics Virtual Meeting, 1-3 February, Paper AAS-21-354.
  • Breitsch, B., Morton, Y., Rino, C., Xu, D. 2020. GNSS carrier phase transitions due to diffractive ionosphere scintillation: simulation and characterization. IEEE Trans. Aero. Elec. Sys., 56(5).  DOI:10.1109/TAES.2020.2979025.
  • Bruinsma, S., Boniface, C., Sutton, E. K., et al. 2021. Thermosphere modeling capabilities assessment: geomagnetic storms. Journal of Space Weather and Space Climate, 11(12). doi:10.1051/swsc/2021002.
  • Bruinsma, S., Sutton, E., Solomon, S.C, et al. 2018. Space weather modeling capabilities assessment: neutral density for orbit determination at low Earth orbit. Space Weather, 16, 1806-1816. doi:10.1029/2018SW002027 .
  • Buynovskiy, A., Thayer, J.P., Sutton, E.K. 2024. An Ascending-Descending Accelerometry Technique to Distinguish Equatorial In-Track Density and Wind Structures, J. Geophys. Res. Space Physics. In Preparation.
  • Cosgrove, R. B., Bahcivan, H., Chen, S., Sanchez, E., & Knipp, D. 2022. Violation of Hemispheric Symmetry in Integrated Poynting Flux via an Empirical Model. Geophysical Research Letters, 49, e2021GL097329. https://doi.org/10.1029/2021GL097329
  • Fang, T-W., Kubaryk, A. Goldstein, D., et al. 2022. Space Weather Environment During the SpaceX Starlink Satellite Loss in February 2022, Space Weather, 20,11. https://doi.org/10.1029/2022SW003193.
  • Jones, M., Sutton, E. K., Emmert, J. T., et al. 2021. On the Effects of Mesospheric and Lower Thermospheric Oxygen Chemistry on the Thermosphere and Ionosphere Semiannual Oscillation. Journal of Geophysical Research, Space Physics, 126, e28647. doi:10.1029/2020JA028647.
  • Kalafatoglu Eyiguler, E. C., Shim, J. S., Kuznetsova, M. M., et al. 2019. Quantifying the storm time thermospheric neutral density variations using model and observations. Space Weather, 17, 269-284. doi:10.1029/2018SW002033.
  • Knipp, D.J., Bernstein, V., Wahl, K. and Hayakawa, H. 2021. Timelines as a tool for learning about space weather storms. J. Space Weather Space Clim, 11, 29. https://doi.org/10.1051/swsc/2021011
  • Knipp, D., Kilcommons, L., Hairston, M., & Coley, W. R. 2021. Hemispheric Asymmetries in Poynting Flux Derived from DMSP Spacecraft. Geophysical Research Letters, 48, e2021GL094781. https://doi.org/10.1029/2021GL094781 
  • Mehta, P. M., Linares, R., & Sutton, E. K. 2019. Data-driven inference of thermosphere composition during solar minimum conditions. Space Weather, 17, 1364-1379. doi:10.1029/2019SW002264.
  • Morton, Y., Yang. Z, Breitsch. B, et al. 2020. Ionospheric Effects, Monitoring, and Mitigation Techniques, in Position, Navigation, and Timing Technologies, in the 21st Century: Integrated Satellite Navigation, Sensor Systems, and Civil Applications, 1, Wiley-IEEE Press (Eds. Y. J. Morton, F. van Diggelen, J. J., Spilker, B. Parkinson, et al.). Ch. 31. https://doi.org/10.1002/9781119458449.ch31.
  • Mutschler, S., Axelrad, P., Matsuo, T., et al. 2019. Physics-Based approach to density estimation and prediction using orbital debris tracking data. Proceedings of the Advanced Maui Optical and Space Surveillance Technologies Conference, Maui, HI, 17-20 September.
  • Mutschler, S., Axelrad, P., Sutton, E. K. 2021. Application of SoleiTool for Density Estimation using CubeSat GPS Data. Proceedings of the Advanced Maui Optical and Space Surveillance Technologies Conference, Maui, HI, 14–17 September.
  • Pilinski, M., Crowley, G., Seaton, M., et al. 2019. Dragster: An assimilative tool for satellite drag specification. Proceedings of the Advanced Maui Optical and Space Surveillance Technologies Conference, Maui, HI, 17-20 September.
  • Ray, V., Thayer, J., Sutton, E.K., Waldron, Z. 2024. Error assessment of thermospheric mass density retrieval with POD products using different strategies, Space Weather. In Review.
  • Rino, C., Breitsch, B., Morton, Y., et al. 2020. GNSS signal phase, TEC, and phase scintillation. Navigation, J. Institute of Navigation, 67(4). http://doi.org/10.1002/navi.396.
  • Sutton, E. K., Pilinski, M. D., Mutschler, S. M., Thayer, J. P., Berger, T. 2020. Improved Physics-Based Simulation of the LEO Space Environment. Proceedings of The Advanced Maui Optical and Space Surveillance Technologies Virtual Conference, 15-18 September.
  • Sutton, E. K., Thayer, J.P, Pilinski, M.D., Mutschler, S.M, Berger, T., Nguyen, V, Masters, D. 2021. Toward Accurate Physics-Based Specifications of Neutral Density using GNSS-Enabled Small. Space Weather. 19, e02736. doi:10.1029/2021SW002736.
  • Thayer J. P., Tobiska, W.K, Pilinski, M., and Sutton, E.K. 2020. Remaining Issues in Upper Atmosphere Satellite Drag. In Space Physics and Aeronomy, Volume 5, Space Weather Effects and Applications, Wiley Publishing (eds. A. J. Coster, P. J. Erickson, L. J. Lanzerotti, Y. Zhang, and L. J. Paxton), doi:10.1002/9781119815600.ch5.
  • Thayer, J.P., Waldron, Z. C., Sutton, E. K. 2023. Solar Flux Dependence of Upper Thermosphere Diurnal Variations: Observed and Modeled. JGR Space Physics, 128, 2. https://doi.org/10.1029/2022JA031146.
  • Waldron, Z., Garcia-Sage, K., Thayer, J., Sutton, E.K., Ray, V., Rowlands, D., Lemoine, F., Luthcke, S., Kuznetsova, M., Ringuette, R., Rastaetter, L., Berland, G. 2024. Assessing Thermospheric Neutral Density Models using GEODYN's Precision Orbit Determination. Space Weather, doi:10.1029/2023SW003603. In Press.
  • Weimer, D. R., Mlynczak, M. G., Emmert, et al. 2018. Correlations between the thermosphere's semiannual density variations and infrared emissions measured with the SABER instrument. Journal of Geophysical Research, Space Physics, 123, 8850-8864. doi:10.1029/2018JA025668. 
  • Xu, D., Morton, Y., Rino, C., et al. 2020. A two-parameter multifrequency GPS signal simulator for strong equatorial ionospheric scintillation: modeling and parameter characterization. Navigation, J. Institute of Navigation, 67(1), 181-195. http://doi.org/10.1002/navi.350.
  • Yang, Z., Morton, Y. 2020. Low-latitude ionospheric scintillations of multi-constellation GNSS signals in relation to magnetic field orientation. J. of Geodesy, 94(59), 1-15. https://doi.org/10.1007/s00190-020-01391-7.
  • Yang, Z., Morton, Y, Zakharenkova, I., et al. 2020. Global view of ionospheric disturbances impacts on kinematic GPS positioning solutions during the 2015 St. Patrick’s Day storm. J. Geophy. Res., Space Sci., 125(7). DOI: 10.1029/2019JA027681.
  • Zhan, W., Doostan, A., Sutton, E.K., Fang, T.-W. 2024. Quantifying uncertainties in the quiet-time ionosphere-thermosphere using WAM-IPE. Space Weather, doi:10.1029/2023SW003665. In Press.