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

S Guillas,C D Linkletter,A Maute,A D Richmond,J Rougier

doi:10.5194/gmd-2-137-2009

Abstract

Abstract. 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.

Highlights

The calibration of complex computer models, or simulators, of physical systems is a difficult endeavor, see Kennedy and O’Hagan (2001) and discussion therein
For this study we explore the response of the magnetic-eastward (D) magnetic perturbation at the ground, MAGGRD-D in [nT], at two locations to variations in just three inputs: two that help describe atmospheric tides at the Thermosphere-Ionosphere-Electrodynamics General Circulation Model (TIE-GCM) lower boundary, and one that constrains the minimum night-time electron density
For the calibration of TIE-GCM, we follow here a Bayesian approach (Kennedy and O’Hagan, 2001). It consists of putting distributional assumptions on the calibration parameters θ1, θ2 and θ3 before comparing with observations and letting the information contained in the data update this a priori assumption to get as a result a posterior distribution of the calibration parameters

Summary

Introduction

The calibration of complex computer models, or simulators, of physical systems is a difficult endeavor, see Kennedy and O’Hagan (2001) and discussion therein It consists of searching for the best combination of parameters in the simulator inputs which will produce outputs that match best the observations. The reasons for which we want to calibrate such a simulator are: to replace tuning and fudge factors, to obtain more reliable simulations as we use more observations (ground- or space-based) under various conditions at different locations, seasons, local times, and to account for uncertainty when the model is used for system predictions.

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Conclusion