Abstract
The Ionosphere-Thermosphere environment undergoes constant and sometimes dramatic changes due to solar and geomagnetic activity. Furthermore, given that this environment has a significant effect on space infrastructure, such as satellites, it is important to understand the potential changes caused by space weather events.
This work presents the implementation of the ensemble Kalman filter assimilation technique to improve the nowcast and forecast of the thermosphere environment. Specifically, the assimilation tries to adjust F10.7, a solar radio flux parameter at 10.7 cm wavelength that acts as a proxy for solar activity.
The results show that during high solar activity, the measured F10.7 index is able to account for the variability in the ionosphere-thermosphere, hence the correction provided by the assimilation is small. On the other hand, during low solar activity, F10.7 is unable to account for variability in the ionosphere-thermosphere, and the correction provided by the assimilation drastically improves the nowcast/forecast.
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References
Daley, R.: Atmospheric Data Analysis. Cambridge University Press, Cambridge (1991)
Dickinson, R., Ridley, E., Roble, R.: A three-dimensional general circulation model of the thermosphere. J. Geophys. Res. 86, 1499–1512 (1981)
Evensen, G.: Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. J. Geophys. Res. 99(C5), 10143–10162 (1994)
Evensen, G.: The ensemble Kalman filter: Theoretical formulation and practical implementation. Ocean Dyn. 53, 343–367 (2003)
Hamill, T., Whitaker, J., Snyder, C.: Distance-dependent filtering of background error covariance estimates in an ensemble Kalman filter. Mon. Wea. Rev. 129, 2776–2790 (2001)
Hedin, A.: A revised thermospheric model based on mass spectrometer and incoherent scatter data: Msis-83. J. Geophys. Res. 88, 10170–10188 (1983)
Hedin, A., Fleming, E., Manson, A., Schmidlin, F., Avery, S., Clark, R., Franke, S., Fraser, G., Tsuda, T., Vial, F., Vincent, R.: Empirical wind model for the upper, middle and lower atmosphere. J. Atmos. Terr. Phys. 58, 1421–1447 (1996)
Houtekamer, P., Mitchell, H.: Data assimilation using an ensemble Kalman filter technique. Mon. Wea. Rev. 126, 796–811 (1998)
Hunt, B., Kostelich, E., Szunyogh, I.: Efficient Data Assimilation for Spatiotemporal Chaos: a Local Ensemble Transform Kalman Filter. Arxiv preprint physics/0511236 (2005)
Kalman, R.: A new approach to linear filtering and prediction problems. Trans. ASME Ser. D J. Basic Eng. 82, 35–45 (1960)
Kalnay, E.: Atmospheric Modeling, Data Assimilation, and Predictability. Cambridge University Press, Cambridge (2003)
Lee, I., Matsuo, T., Richmond, A., Liu, J., Wang, W., Lin, C., Anderson, J., Chen, M.: Assimilation of formosat-3/cosmic electron density profiles into a coupled thermosphere/ionosphere model using ensemble kalman filtering. J. Geophys. Res. 117, A10318 (2012)
Matsuo, T., Lee, I.T., Anderson, J.: Thermospheric mass density specification using an ensemble kalman filter. J. Geophys. Res. Space Phys. 118, 1339–1350 (2013)
Morozov, A., Ridley, A., Bernstein, D., Collins, N., Hoar, T., Anderson, J.: Data assimilation and driver estimation for the global ionosphere-thermosphere model using the ensemble adjustment kalman filter. J. Atmos. Sol.-Terr. Phy. 104, 126–136 (2013)
Picone, J., Hedin, A., Drob, D., Aikin, A.: Nrlmsise-00 empirical model of the atmosphere: Statistical comparisons and scientific issues. J. Geophys. Res. 107, 1468 (2002)
Rawer, K., Bilitza, D., Ramakrishnan, S.: Goals and status of the international reference ionosphere. Rev. Geophys. 16, 177 (1978)
Reigber, C., Luhr, H., Schwintzer, P.: Champ mission status. Adv. Space Res. 30, 129–134 (2002)
Richmond, A., Ridley, E., Roble, R.: A thermosphere/ionosphere general circulation model with coupled electrodynamics. Geophys. Res. Lett. 19(6), 601–604 (1992)
Ridley, A.J., Deng, Y., Toth, G.: The global ionosphere-thermosphere model. J. Atmos. Sol.-Terr. Phys. 68, 839–864 (2006)
Sutton, E.: Normalized force coefficients for satellites with elongated shapes. J. Spacecraft Rockets 46, 112–116 (2009)
Sutton, E., Nerem, R., Forbes, J.: Density and winds in the thermosphere deduced from accelerometer data. J. Spacecraft Rockets 44, 1210–1219 (2007)
Tapley, B., Bettadpur, S., Watkins, M., Reigber, C.: The gravity recovery and climate experiment: mission overview and early results. Geophys. Res. Lett. 31, L09607 (2004)
Acknowledgments
This research was conducted as part of the Integrated Modeling of Perturbations in Atmospheres for Conjunction Tracking (IMPACT) project, funded by the Laboratory Directed Research and Development program within Los Alamos National Laboratory. More information on www.impact.lanl.gov.
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Godinez, H.C., Lawrence, E., Higdon, D., Ridley, A., Koller, J., Klimenko, A. (2015). Specification of the Ionosphere-Thermosphere Using the Ensemble Kalman Filter. In: Ravela, S., Sandu, A. (eds) Dynamic Data-Driven Environmental Systems Science. DyDESS 2014. Lecture Notes in Computer Science(), vol 8964. Springer, Cham. https://doi.org/10.1007/978-3-319-25138-7_25
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DOI: https://doi.org/10.1007/978-3-319-25138-7_25
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