The DTM-2000 empirical thermosphere model with new data assimilation and constraints at lower boundary: accuracy and properties
Introduction
Atmospheric density models are, besides their use in atmospheric studies, required to compute the atmospheric drag force in satellite orbit determination. They represent temperature and density as a function of altitude, latitude, local solar time, day of year, and parameters related to the state of atmospheric heating.
The accuracy of the model that is most used in precise orbit computations, DTM-94 (Berger et al., 1998), is about 20% (1σ) for its predicted total densities. The MSIS-86 model (Hedin, 1987) has comparable accuracy. DTM-94 is an improvement over the first Drag Temperature Model, DTM-78 (Barlier et al., 1978), which is not representative of low solar activity conditions due to the data set used in this model. This causes uncertain predictions under those conditions.
The inaccurate representation of temperature and density in the 120– altitude range is due to the sparseness of data in this domain. The two basic reasons for the lower atmosphere modelling uncertainties are: first, the modelled density in the lower thermosphere results of adjustment of the model parameters at higher altitudes (220–), which have been propagated downward by means of a modified Bates temperature profile (Bates, 1959); secondly, the modelling of the temperature and its gradient at as constant values in DTM-94 causes uncertainties in the predicted density of the lower thermosphere, since significant temperature variability at that altitude is known from incoherent scatter radar observations (Alcaydé et al., 1974). However, this being acknowledged, the absence of precise data as well as data having enough spatial coverage, has lead to prefer constant boundary conditions.
This is why it is in this region that a special effort has been made to improve the modelling.
In this study, the temperature at (T120) is modelled using incoherent scatter radar observations. The latitudinal coverage is achieved by means of three incoherent scatter radar stations namely St. Santin (45°N), Arecibo (18°N) and the European Incoherent SCATter (EISCAT), which is at approximately 70°N, and is the only available data source at that latitude.
The WIND Imaging Interferometer (WINDII) is an instrument (Shepherd et al., 1993) placed onboard the Upper Atmosphere Research Satellite (UARS), which measured temperature and wind in the lower thermosphere. WINDII data are used to determine the vertical temperature gradients (dT120) as a function of local time, latitude and season. They have also been used to derive relative density variations at altitude (Bruinsma et al., 2002). The variations derived in that study are taken into account in DTM-2000.
Data from the Atmosphere Explorer-C and -E satellites (NSSDC CD-ROM) are assimilated in order to better reproduce in particular the temperature under low solar activity conditions.
The Mg II index (Heath and Schlesinger, 1986) is used as a proxy indicator to represent the solar activity in the EUV and UV whenever possible, which is to say, for data after 1978. Before that date, the solar radio flux at (F10.7) is used. It has recently been demonstrated (Thuillier and Bruinsma, 2001) that the Mg II index is more representative of the solar radiation that governs upper atmosphere heating processes than F10.7 is, while these indices are nearly 100% correlated over periods of at least several years. It is this property that enables both Mg II and F10.7 to be used in the model adjustments.
The next section reviews the data used in this study, and presents the data processing that allowed the modelling of T120 and dT120. The DTM algorithms and the additional temperature and density variations (with respect to variations already represented in DTM-94) are described in Section 3. The results are presented in Section 4 which includes comparisons with DTM-94 and MSIS-86. The various results, the limiting factors in empirical modelling, the effects of data availability, and distribution and accuracy of the estimations, are discussed in Section 5. The future model improvements expected by new and consistent data are presented in Section 6.
Section snippets
Data and external modelling results
The following subsections describe the incoherent scatter radar and WINDII data used in the temperature and temperature gradient modelling at , respectively. They are modelled in spherical harmonics and are introduced in the DTM algorithm described in Section 3. The third subsection gives a short description of relative density variations at , derived from tidal wind measurements. Section 2.4 reviews the accuracy and geographical and temporal coverage of the temperature and density
The DTM-2000 algorithm
The representation of the total density in the altitude range 120– is achieved by summing the contributions of the main thermospheric constituents (N2, O2, O, He, H), under the hypothesis of independent static diffuse equilibrium (which is not always attained (Aikin et al., 1993; Hedin, 1989), but its effect on density is presently not taken into account). The height function fi(z) per constituent i is the result of the integration of the differential equation of diffusive equilibrium:
Results
The adjustment of the model coefficients is done by means of a least-squares procedure; only those coefficients were retained in the solution that had a statistical uncertainty from the fit of less than a tenth of the coefficient value. The properties of the modelling are analysed by mean and RMS of the residuals, which we have defined as ‘observed/calculated’ (O/C). The data sets that are marked with an asterisk in Table 3 are affected by systematic uncertainties and some of them require the
Discussion
One of the reasons for the slow progress in thermosphere modelling is due to the data that we dispose of today. The last thermospheric satellite mission, DE-2, ended in 1983. MSIS-86, DTM-94 and DTM-2000 have therefore been constructed for a large part with the same data. However, these data do not provide adequate geographical coverage (altitude, latitude and solar local time), they are not representative of an entire solar cycle (low to high solar activity), or they are affected by bias
Future improvements
The upper atmosphere empirical modelling effort has presently reached a status quo due to the data problems discussed in the preceding section. The CHAMP nominal mission is scheduled for a 5-year operation, during which time the altitude will decrease to by natural decay or orbit manoeuvres. Total density data will be obtained with a single instrument during solar maximum (2000–2002) to nearly solar minimum (2005). The orbit provides an appropriate local time sampling for geophysical
Conclusion
The latest version of DTM, DTM-2000, has been constructed using AE-C and AE-E data besides the data already assimilated in DTM-94. This allowed significant improvement of the temperature and helium modelling under low solar activity: the temperature bias of 3% has been corrected and the accuracy augmented by approximately 0.5%, while the helium modelling is approximately 3% more precise. The atomic oxygen modelling has been improved by 1–2%. Molecular nitrogen is predicted with the smallest
Acknowledgements
This study was supported by the Centre National d'Etudes Spatiales (CNES). The authors acknowledge the CEDAR database (http://cedarweb.hao.ucar.edu). We thank C. Lathuillère for the EISCAT data. We thank F.A. Marcos and an anonymous referee for reviewing this paper.
References (33)
- et al.
Relative density variations at derived from tidal wind observations made by the UARS/WINDII instrument
Journal of Atmospheric and Solar-Terrestrial Physics
(2002) Satellite drag coefficients
Planetary and Space Science
(1965)- et al.
Global experimental model of the exospheric temperature using optical and incoherent scatter measurements
Journal of Atmospheric and Terrestrial Physics
(1977) - et al.
Thermospheric molecular oxygen measurements using the ultraviolet spectrometer on the Solar Maximum Mission spacecraft
Journal of Geophysical Research
(1993) - et al.
Long-term variations of thermospheric temperature and composition
Journal of Geophysical Research
(1974) - et al.
Aeronomy, Part B
(1973) - et al.
A thermospheric model based on satellite drag data
Annales de Geophysique
(1978) Some problems concerning the terrestrial atmosphere above about the 100-km level
Proceedings of the Royal Society A.
(1959)- et al.
Improvement of the empirical thermospheric model DTMDTM-94- comparative review on various temporal variations and prospects in space geodesy applications
Journal of Geodesy
(1998) - Bruinsma, S.L., Biancale, R., 2001. STAR accelerometer data processing and results: Part II. AAS paper...
The neutral mass spectrometer on Dynamics Explorer
Space Science Instruments
NOAA-11 Solar Backscattered Ultraviolet, model 2 (SBUV/2) instrument solar spectral irradiance measurements in 1989–1994 1. Observations and long-term calibration
Journal of Geophysical Research
Thermospheric tides at equinoxsimulations with coupled composition and auroral forcing, 2, semidiurnal component
Journal of Geophysical Research
Gravity wave and tidal structures between 60 and inferred from Space Shuttle reentry data
Journal of the Atmospheric Science
Evaluation of thermospheric models and the precipitation index for satellite drag
Advances in Space Research
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2022, Advances in Space ResearchCitation Excerpt :Accurate measurements of Earth’s atmospheric density are very important for maneuver planning, precise orbit determination, satellite lifetime prediction and return control of re-entry vehicle (Storz et al., 2005; Prölss, 2011; Doornbos, 2012). With the increasing demand for the Earth’s atmospheric density, various semi-empirical atmosphere models have been developed, such as CIRA model (Kallmann-Bijl et al., 1961), Jacchia model (Jacchia, 1964, 1971, 1977), DTM model (Bruinsma et al., 2003; Bruinsma et al., 2012; Bruinsma, 2015), MSIS model (Hedin, 1987; Picone et al., 2002; Emmert et al., 2021). Due to complex changes in the upper atmosphere, the atmospheric density provided by semi-empirical models tends to have an RMS error of 30% or more at much higher altitudes relative to the lower thermosphere (Pardini and Anselmo, 2001; Doornbos et al., 2008), in general, the RMS error near 120 km should be much smaller (Emmert, 2015).