Abstract
Based on the thermosphere-Ionosphere-Electrodynamics General Circulation Model (TIEGCM), a thermospheric-ionospheric data assimilation and forecast system is developed. Using this system, we estimated the oxygen ions, neutral temperature, wind, and composition by assimilating the simulated data from Formosa Satellite 3/Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) occultation electron density profiles to evaluate their effects on the ionospheric forecast. An ensemble Kalman filter data assimilation scheme and combined state and parameter estimation methods are used to estimate the unobserved parameters in the model. The statistical results show that the neutral and ion compositions are more effective than the neutral temperature and wind for improving the forecast of the ionospheric electron density, whose root mean square errors in the assimilation period decreased by approximately 40%, 30%, and 10% due to the estimations of the neutral composition, oxygen ions, and neutral temperature, respectively. Due to the different physical and chemical processes that these parameters primarily affect, their e-folding times differ greatly from longer than 12 h for neutral composition to approximately 6 h for oxygen ions and 3 h for neutral temperature. The effect of estimating the neutral composition on improving the ionospheric forecast is greater than that of estimating the oxygen ions, which can be also be seen in an actual data assimilation experiment. This indicates that the neutral composition is the most important thermospheric parameter in ionospheric data assimilations and forecasts.
Similar content being viewed by others
References
Aa E, Huang W, Yu S, Liu S, Shi L, Gong J, Chen Y, Shen H. (2015). A regional ionospheric TEC mapping technique over China and adjacent areas on the basis of data assimilation. J Geophys Res-Space Phys, 120: 5049–5061
Aa E, Liu S, Huang W, Shi L, Gong J, Chen Y, Shen H, Li J. (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
Angling M J, Cannon P S. (2004). Assimilation of radio occultation measurements into background ionospheric models. Radio Sci, 39: RS1S08
Bust G S, Crowley G, Garner T W, Gaussiran Ii T L, Meggs R W, Mitchell C N, Spencer P S J, Yin P, Zapfe B. (2007). Four-dimensional GPS imaging of space weather storms. Space Weather, 5: 02003
Bust G S, Garner T W, Gaussiran T L. (2004). Ionospheric data assimilation three-dimensional (IDA3D): A global, multisensor, electron density specification algorithm. J Geophys Res, 109: A11312
Chartier A T, Jackson D R, Mitchell C N. (2013). A comparison of the effects of initializing different thermosphere-ionosphere model fields on storm time plasma density forecasts. J Geophys Res-Space Phys, 118: 7329–7337
Evensen G. (1994). Sequential data assimilation with a nonlinear quasigeostrophic model using Monte Carlo methods to forecast error statistics. J Geophys Res, 99: 10143–10162
Evensen G. (2003). The Ensemble Kalman filter: Theoretical formulation and practical implementation. Ocean Dyn, 53: 343–367
Gaspari G, Cohn S E. (1999). Construction of correlation functions in two and three dimensions. Q J R Meteorol Soc, 125: 723–757
Houtekamer P L, Mitchell H L. (1998). Data assimilation using an ensemble Kalman filter technique. Mon Weather Rev, 126: 796–811
Houtekamer P L, Mitchell H L. (2001). A sequential ensemble Kalman filter for atmospheric data assimilation. Mon Weather Rev, 129: 123–137
Howe B M, Runciman K, Secan J A. (1998). Tomography of the ionosphere: Four-dimensional simulations. Radio Sci, 33: 109–128
Hsu C T, Matsuo T, Wang W, Liu J Y. (2014). Effects of inferring unobserved thermospheric and ionospheric state variables by using an Ensemble Kalman filter on global ionospheric specification and forecasting. J Geophys Res-Space Phys, 119: 9256–9267
Lee I T, Matsuo T, Richmond A D, Liu J Y, Wang W, Lin C H, Anderson J L, Chen M Q. (2012). Assimilation of FORMOSAT-3/COSMIC electron density profiles into a coupled thermosphere/ionosphere model using ensemble Kalman filtering. J Geophys Res, 117: A10318
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). J Geophys Res, 110: A12306
Matsuo T, Araujo-Pradere E A. (2011). Role of thermosphere-ionosphere coupling in a global ionospheric specification. Radio Sci, 46: RS0D23
Matsuo T, Lee I T, Anderson J L. (2013). Thermospheric mass density specification using an ensemble Kalman filter. J Geophys Res-Space Phys, 118: 1339–1350
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. J Geophys Res, 108: 1075
Richmond A D, Ridley E C, Roble R G. (1992). A thermosphere/ionosphere general circulation model with coupled electrodynamics. Geophys Res Lett, 19: 601–604
Rishbeth H, Müller-Wodarg I C F. (1999). Vertical circulation and thermospheric composition: A modelling study. Ann Geophys, 17: 794–805
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 Sci, 39: RS1S04
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. J Geophys Res, 111: A11315
Schunk R W, Scherliess L, Sojka J J, Thompson D C, Anderson D N, Codrescu M, Minter C, Fuller-Rowell T J, Heelis R A, Hairston M, Howe B M. (2004). Global assimilation of ionospheric measurements (GAIM). Radio Sci, 39: RS1S02
Van Leeuwen P J, Evensen G. (1996). Data assimilation and inverse methods in terms of a probabilistic formulation. Mon Weather Rev, 124: 2898–2913
Wang C, Hajj G, Pi X, Rosen I G, Wilson B. (2004). Development of the global assimilative ionospheric model. Radio Sci, 39: RS1S06
Yue X A. (2008). Modeling and data assimilation of mid- and low-latitude ionosphere (in Chinese). Doctoral Dissertation. Beijng: Institude of Geology and Geophysics, Chinese Academy of Sciences
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. J Geophys Res, 116: A03317
Yue X, Schreiner W S, Lei J, Sokolovskiy S V, Rocken C, Hunt D C, Kuo Y H. (2010). Error analysis of Abel retrieved electron density profiles from radio occultation measurements. Ann Geophys, 28: 217–222
Yue X, Wan W, Lin L, Zheng F, Lei J, Zhao B, Xu G, Zahng S R, Zhu J. (2007). Data assimilation of incoherent scatter radar observation into a one-dimensional midlatitude ionospheric model by applying ensemble Kalman filter. Radio Sci, 42: RS6006
Zhang S R, Oliver W L, Fukao S, Kawamura S. (2001). Extraction of solar and thermospheric information from the ionospheric electron density profile. J Geophys Res, 106: 12821–12836
Zhang S R, Oliver W L, Holt J M, Fukao S. (2002). Solar EUV flux, exospheric temperature and thermospheric wind inferred from incoherent scatter measurements of the electron density profile at Millstone Hill and Shigaraki. Geophys Res Lett, 29: 72–1–72–4
Zhang Y N, Wu X C and Hu X. (2017). TIEGCM Ensemble Kalman Filter Assimilation Model Design and Preliminary Results (in Chinese). Chin J Space Sci, 37: 168–176
Acknowledgements
The TIEGCM were obtained from High Altitude Observatory, NCAR (URL: https://doi.org/www.hao.ucar.edu/modeling/tgcm/tie.php). The FORMOSAT-3/COSMIC data were obtained from COSMIC Data Analysis and Archival Center (CDAAC) (URL: https://doi.org/cdaac-www.cosmic.ucar.edu/cdaac/tar/rest.html). This work was supported by National Important Basic Research Project of China (Grant No. 2016YFB0501503) and National Natural Science Foundation of China (Grant No. 41204137).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Zhang, Y., Wu, X. & Hu, X. Effects of estimating the ionospheric and thermospheric parameters on electron density forecasts. Sci. China Earth Sci. 61, 1875–1887 (2018). https://doi.org/10.1007/s11430-017-9251-4
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11430-017-9251-4