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Bayesian Calibration of the Thermosphere-ionosphere Electrodynamics General Circulation Model (Tie-gcm) : Volume 2, Issue 1 (12/05/2009)

By Guillas, S.

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Book Id: WPLBN0003990173
Format Type: PDF Article :
File Size: Pages 22
Reproduction Date: 2015

Title: Bayesian Calibration of the Thermosphere-ionosphere Electrodynamics General Circulation Model (Tie-gcm) : Volume 2, Issue 1 (12/05/2009)  
Author: Guillas, S.
Volume: Vol. 2, Issue 1
Language: English
Subject: Science, Geoscientific, Model
Collections: Periodicals: Journal and Magazine Collection (Contemporary), Copernicus GmbH
Historic
Publication Date:
2009
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications

Citation

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Maute, A., Rougier, J., Guillas, S., Linkletter, C. D., & Richmond, A. D. (2009). Bayesian Calibration of the Thermosphere-ionosphere Electrodynamics General Circulation Model (Tie-gcm) : Volume 2, Issue 1 (12/05/2009). Retrieved from http://www.hawaiilibrary.com/


Description
Description: Department of Statistical Science & Aon Benfield UCL Hazard Research Centre, University College London, UK. In this paper, we demonstrate a procedure for calibrating a complex computer simulation model having uncertain inputs and internal parameters, with application to the NCAR Thermosphere-Ionosphere-Electrodynamics General Circulation Model (TIE-GCM). We compare simulated magnetic perturbations with observations at two ground locations for various combinations of calibration parameters. These calibration parameters are: the amplitude of the semidiurnal tidal perturbation in the height of a constant-pressure surface at the TIE-GCM lower boundary, the local time at which this maximises and the minimum night-time electron density. A fully Bayesian approach, that describes correlations in time and in the calibration input space is implemented. A Markov Chain Monte Carlo (MCMC) approach leads to potential optimal values for the amplitude and phase (within the limitations of the selected data and calibration parameters) but not for the minimum night-time electron density. The procedure can be extended to include additional data types and calibration parameters.

Summary
Bayesian calibration of the Thermosphere-Ionosphere Electrodynamics General Circulation Model (TIE-GCM)

Excerpt
Higdon, D., Gattiker, J., Williams, B., and Rightley, M.: Computer model calibration using high-dimensional output, J. Am. Stat. Assoc., 103, 570–583, 2008.; Bayarri, M. J., Berger, J. O., Cafeo, J., Garcia-Donato, G., Liu, F., Palomo, J., Parthasarathy, R. J., Paulo, R., Sacks, J., and Walsh, D.: Computer model validation with functional output, Ann. Statist., 35, 1874–1906, 2007.; Fang, T., Richmond, A., Liu, J., Maute, A., Lin, C., Chen, C., and Harper, B.: Model simulation of the equatorial electrojet in the Peruvian and Philippine sectors, J. Atmos. Solar-Terr. Phys., 70, 2203–2211, 2008.; Fesen, C. G., Crowley, G., Roble, R. G., Richmond, A. D., and Fejer, B. G.: Simulation of the pre-reversal enhancement in the low latitude vertical ion drifts, Geophys. Res. Let., 27(13), 1851–1854, 2000.; Gneiting, T.: Correlation functions for atmospheric data analysis, Q. J. Roy. Meteorol. Soc., 125, 2449–2464, 1999.; Gilks, W. R., Richardson, S., and Spiegelhalter, D. J. (Eds.): Markov chain {M}onte {C}arlo in practice, Interdisciplinary Statistics, Chapman & Hall, London, 1996.; Hagan, M. and Forbes, J.: Migrating and nonmigrating diurnal tides in the middle and upper atmosphere excited by tropospheric latent heat release, J. Geophys. Res., 107(D24), 4754, doi:10.1029/2001JD001236, 2002.; Hagan, M. and Forbes, J.: Migrating and nonmigrating semidiurnal tides in the middle and upper atmosphere excited by troposheric latent heat release, J. Geophys. Res., 108(A2), 1062, doi:10.1029/2002JA009466, 2003.; Kennedy, M. C. and O'Hagan, A.: Bayesian calibration of computer models, J. R. Stat. Soc. Ser. B Stat. Methodol., 63, 425–464, 2001.; Kleijnen, J. and van Beers, W.: Application-driven sequential designs for simulation experiments: Kriging metamodelling, J. Oper. Res. Soc., 55, 876–883, doi:10.1057/palgrave.jors.2601747, 2004.; Linkletter, C., Bingham, D., Hengartner, N., Higdon, D., and Ye, K. Q.: Variable selection for {G}aussian process models in computer experiments, Technometrics, 48, 478–490, 2006.; Richmond, A., Ridley, E., and Roble, R.: A Thermosphere/Ionosphere general circulation model with coupled electrodynamics, Geophys. Res. Lett., 19, 601–604, 1992.; Rougier, J.: Efficient Emulators for Multivariate Deterministic Functions, J. Comput. Graph. Stat., 17, 827–843, doi:10.1198/106186008X384032, 2008.; Rougier, J., Guillas, S., Maute, A., and Richmond, A.: Expert Knowledge and Multivariate Emulation: The Thermosphere-Ionosphere Electrodynamics General Circulation Model ({TIE-GCM}), Technometrics, revised, 2009.; Scherliess, L., Schunk, R. W., Sojka, J. J., Thompson, D. C., and Zhu, L.: 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, doi:10.1029/2006JA011712, 2006.; Welch, W. J., Buck, R. J., Sacks, J., Wynn, H. P., Mitchell, T. J., and Morris, M. D.: Screening, predicting, and computer experiments, Technometrics, 34, 15–25, 1992.; Williams, B. J., Santner, T. J., and Notz, W.: Sequential design of computer experiments to minimize integrated response functions, Statist. Sinica, 10, 1133–1152, 2000.

 

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