Abstract

Abstract. Thermospheric densities deduced from STAR accelerometer measurements onboard the CHAMP satellite are used to characterize the thermosphere and its response to space weather events. The STAR thermospheric density estimates are analysed using a Singular Value Decomposition (SVD) approach allowing one to decouple large scale spatial and temporal variations from fast and local transients. Because SVD achieves such decomposition by using the reproducibility of orbital variations, it provides more meaningful results than any method based upon data smoothing or filtering. SVD analysis enables us to propose a new thermosphere proxy, based on the projection coefficient of the CHAMP densities on the first singular vector. The large scale spatial variations in the density, mostly related to altitude/latitude variations are captured by the first singular vector; time variations are captured by the associated projection coefficient. The study presented here is focused on time dependent global scale variations in the thermospheric density between 50 N and 50 S geographic latitudes. We show that the time variations in the projection coefficient do in fact represent those in the global density that are associated with magnetic activity as well as with solar EUV radiations. We also show that the NRLMSISE-00 empirical model better accounts for the density forcing by Solar radiations when tuned using Mg II indices. Using the so modified model with an additional geomagnetic parameterization corresponding to quiet geomagnetic situation enables one to define time reference values which are then used to evaluate the impact of geomagnetic activity. The ratio of CHAMP density projection coefficient to the quiet model projection coefficient is a global quantity, independent of altitude and latitude, which quantifies the thermospheric density response to auroral energy deposition. It will serve as a proxy of the response of thermospheric density to geomagnetic activity forcing.

Highlights

  • The CHAMP satellite was launched in July 2000 in a nearcircular orbit at about 450 km altitude with an inclination of 87.3◦ (Reigber et al, 2002)

  • The large scale spatial variations in the density, mostly related to altitude/latitude variations are captured by the first singular vector; time variations are captured by the associated projection coefficient

  • We show that the time variations in the projection coefficient do represent those in the global density that are associated with magnetic activity as well as with solar EUV radiations

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Summary

Introduction

The CHAMP satellite was launched in July 2000 in a nearcircular orbit at about 450 km altitude with an inclination of 87.3◦ (Reigber et al, 2002). The scale height used for such normalization is estimated by means of empirical thermosphere models Such models tend to underestimate the disturbances in the exospheric temperature in presence of magnetic activity (Burns et al, 1995; Killeen et al, 1995), by as much as 70% (Lathuillere and Menvielle, 2004) which in turn leads to errors in scale height that can reach 15%, and errors in the normalized densities (over one scale height) of the same order of magnitude. These errors depend on the geomagnetic activity through the model imperfectness, which introduce unknown biases to the normalized densities Another solution is to get rid of the density variations related to changes in the satellite altitude by defining a reference that makes it possible to compare observations made at different satellite positions, i.e. at different altitudes, latitudes and local times LT. It is shown that SVD analysis of the densities computed for quiet magnetic conditions can be used to derive a reference level for studying thermospheric density perturbations in response to space weather events

SVD analysis of CHAMP densities during May 2003
The 15-orbit running SVD analysis method
The SVD analysis of the May 2003 CHAMP density
The SVD analysis of NRLMSISE-00 densities
The example of May 2003
Model tuning with the Mg II index
A possible proxy
Findings
Conclusion

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