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Small Satellite Constellations for Data Driven Atmospheric Remote Sensing Read chapter Read first chapter

2015 | OriginalPaper | Chapter

Specification of the Ionosphere-Thermosphere Using the Ensemble Kalman Filter

Authors : Humberto C. Godinez, Earl Lawrence, David Higdon, Aaron Ridley, Josef Koller, Alexei Klimenko

Published in: Dynamic Data-Driven Environmental Systems Science

Publisher: Springer International Publishing

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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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previous chapter A Hybrid Particle-Ensemble Kalman Filter for High Dimensional Lagrangian Data Assimilation
next chapter Ensemble Adjustment Kalman Filter Data Assimilation for a Global Atmospheric Model
Literature
1.
Daley, R.: Atmospheric Data Analysis. Cambridge University Press, Cambridge (1991)
2.
Dickinson, R., Ridley, E., Roble, R.: A three-dimensional general circulation model of the thermosphere. J. Geophys. Res. 86, 1499–1512 (1981)CrossRef
3.
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)CrossRef
4.
Evensen, G.: The ensemble Kalman filter: Theoretical formulation and practical implementation. Ocean Dyn. 53, 343–367 (2003)CrossRef
5.
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)CrossRef
6.
Hedin, A.: A revised thermospheric model based on mass spectrometer and incoherent scatter data: Msis-83. J. Geophys. Res. 88, 10170–10188 (1983)CrossRef
7.
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)CrossRef
8.
Houtekamer, P., Mitchell, H.: Data assimilation using an ensemble Kalman filter technique. Mon. Wea. Rev. 126, 796–811 (1998)CrossRef
9.
Hunt, B., Kostelich, E., Szunyogh, I.: Efficient Data Assimilation for Spatiotemporal Chaos: a Local Ensemble Transform Kalman Filter. Arxiv preprint physics/​0511236 (2005)
10.
Kalman, R.: A new approach to linear filtering and prediction problems. Trans. ASME Ser. D J. Basic Eng. 82, 35–45 (1960)CrossRef
11.
Kalnay, E.: Atmospheric Modeling, Data Assimilation, and Predictability. Cambridge University Press, Cambridge (2003)
12.
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)CrossRef
13.
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)CrossRef
14.
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)CrossRef
15.
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)CrossRef
16.
Rawer, K., Bilitza, D., Ramakrishnan, S.: Goals and status of the international reference ionosphere. Rev. Geophys. 16, 177 (1978)CrossRef
17.
Reigber, C., Luhr, H., Schwintzer, P.: Champ mission status. Adv. Space Res. 30, 129–134 (2002)CrossRef
18.
Richmond, A., Ridley, E., Roble, R.: A thermosphere/ionosphere general circulation model with coupled electrodynamics. Geophys. Res. Lett. 19(6), 601–604 (1992)CrossRef
19.
Ridley, A.J., Deng, Y., Toth, G.: The global ionosphere-thermosphere model. J. Atmos. Sol.-Terr. Phys. 68, 839–864 (2006)CrossRef
20.
Sutton, E.: Normalized force coefficients for satellites with elongated shapes. J. Spacecraft Rockets 46, 112–116 (2009)CrossRef
21.
Sutton, E., Nerem, R., Forbes, J.: Density and winds in the thermosphere deduced from accelerometer data. J. Spacecraft Rockets 44, 1210–1219 (2007)CrossRef
22.
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)CrossRef
Metadata
Title
Specification of the Ionosphere-Thermosphere Using the Ensemble Kalman Filter
Authors
Humberto C. Godinez
Earl Lawrence
David Higdon
Aaron Ridley
Josef Koller
Alexei Klimenko
Copyright Year
2015
Book
Dynamic Data-Driven Environmental Systems Science

Print ISBN: 978-3-319-25137-0

Electronic ISBN: 978-3-319-25138-7

Copyright Year: 2015

DOI
https://doi.org/10.1007/978-3-319-25138-7_25

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