Evaluation of the DTM-2009 thermosphere model for benchmarking purposes

Bruinsma, Sean; Sánchez-ortiz, Noelia; Olmedo, Estrella; Guijarro, Nuria

doi:10.1051/swsc/2012005

Cited by 47 publications

(47 citation statements)

References 24 publications

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“…Essentially the same neutral density data that were used in the benchmark model DTM2009 and described in Bruinsma et al (2012) are used in the construction and validation of DTM2013, with some notable exceptions. Only these new datasets will be described in more detail, as well as the only independent dataset.…”

Section: The Total Density Datamentioning

confidence: 99%

“…The algorithm has not been modified with respect to the description given in Bruinsma et al (2012). However, the 30 cm solar radio flux, F30 (described in detail Dudok de Wit et al 2014), is presently used as solar proxy, whereas DTM2009 uses F10.7.…”

Section: The Dtm2013 Algorithmmentioning

confidence: 99%

“…The main objective of the ATMOP research project was to update the DTM2009 thermosphere model (Bruinsma et al 2012) and to develop an operational version. A full description of the project is given in the Science Document, which is available on the ATMOP website.…”

Section: Introductionmentioning

confidence: 99%

“…A full description of the project is given in the Science Document, which is available on the ATMOP website. DTM2013 is evaluated by comparisons to total density data and the pre-ATMOP benchmark DTM2009 (Bruinsma et al 2012) in order to demonstrate and quantify the improvements made. Although not the main topic of this paper, results for the COSPAR reference model for atmospheric drag JB2008 (Bowman et al 2008) are also given in most cases.…”

Section: Introductionmentioning

confidence: 99%

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The DTM-2013 thermosphere model

Bruinsma¹

2015

J. Space Weather Space Clim.

157

102

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Aims -The Drag Temperature Model (DTM) is a semi-empirical model describing the temperature, density, and composition of the Earth's thermosphere. DTM2013 was developed in the framework of the Advanced Thermosphere Modelling and Orbit Prediction project (ATMOP). It is evaluated and compared with DTM2009, the pre-ATMOP benchmark, and the Committe on Space Research (COSPAR) reference model for atmospheric drag JB2008. Methods -The total density data used in this study, including the high-resolution CHAMP, GRACE and GOCE data, cover the 200-900 km altitude range and all solar activities. DTM2013 was constructed with the data assimilated in DTM2009, but with more GRACE data, and low-altitude GOCE data in particular. The solar activity proxy, F10.7 in DTM2009, has been replaced with F30. The bias and precision of the models is evaluated by comparing to the observations according to a metric, which consists of computing mean, RMS, and correlation. Secondly, the residuals are binned, which procedure aids in revealing specific model errors.Results -This evaluation shows that DTM2013 is the least biased and most precise model for the data that were assimilated.Comparison to independent density data shows that it is also the most accurate model overall. It is a significant improvement over DTM2009 under all conditions and at all altitudes, but the largest improvements are obtained at low altitude thanks to GOCE data. The precision of JB2008 decreases with altitude, which is due to its modeling of variations in local solar time and seasons in particular of the exospheric temperature rather than modeling these variations for the individual constituents.

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Section: The Total Density Datamentioning

confidence: 99%

Section: The Dtm2013 Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The DTM-2013 thermosphere model

Bruinsma¹

2015

J. Space Weather Space Clim.

157

102

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“…This limitation should not be misunderstood as a devaluation of nonEuropean models. European operational models in use for post-event analysis include, to name a few important examples: Salammbô (Bourdarie et al 1996), a physics-based radiation belt flux model; SOLPENCO (Aran et al 2006), physics-based simulations of gradual SEP event interplanetary fluxes and fluences; Magnetocosmics (http://cosray.unibe.ch/~laurent/ magnetocosmics/), simulation of cosmic ray particle propagation through the Earth's magnetosphere; Planetocosmics (http://cosray.unibe.ch/~laurent/planetocosmics/), simulation of the hadronic and electromagnetic interactions of cosmic rays with various planetary environments; DTM (Bruinsma et al 2012), an empirical thermosphere density model. They can be used in combination with other models developed by specialized providers, as for instance: DICTAT (Martin 1991) available for internal charging analysis; GEMAT (http://reat.space.…”

Section: Operational Post-event Modeling According To Space Weather Smentioning

confidence: 99%

Progress in space weather modeling in an operational environment

Tsagouri

Belehaki

Bergeot

et al. 2013

J. Space Weather Space Clim.

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This paper aims at providing an overview of latest advances in space weather modeling in an operational environment in Europe, including both the introduction of new models and improvements to existing codes and algorithms that address the broad range of space weather's prediction requirements from the Sun to the Earth. For each case, we consider the model's input data, the output parameters, products or services, its operational status, and whether it is supported by validation results, in order to build a solid basis for future developments. This work is the output of the Sub Group 1.3 ''Improvement of operational models'' of the European Cooperation in Science and Technology (COST) Action ES0803 ''Developing Space Weather Products and services in Europe'' and therefore this review focuses on the progress achieved by European research teams involved in the action.

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Neural networks to predict exosphere temperature corrections

Choury

Bruinsma²,

Schaeffer

2013

Space Weather

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[1] Precise orbit prediction requires a forecast of the atmospheric drag force with a high degree of accuracy. Artificial neural networks are universal approximators derived from artificial intelligence and are widely used for prediction. This paper presents a method of artificial neural networking for prediction of the thermosphere density by forecasting exospheric temperature, which will be used by the semiempirical thermosphere Drag Temperature Model (DTM) currently developed. Artificial neural network has shown to be an effective and robust forecasting model for temperature prediction. The proposed model can be used for any mission from which temperature can be deduced accurately, i.e., it does not require specific training. Although the primary goal of the study was to create a model for 1 day ahead forecast, the proposed architecture has been generalized to 2 and 3 days prediction as well. The impact of artificial neural network predictions has been quantified for the low-orbiting satellite Gravity Field and Steady-State Ocean Circulation Explorer in 2011, and an order of magnitude smaller orbit errors were found when compared with orbits propagated using the thermosphere model DTM2009.Citation: Choury, A., S. Bruinsma, and P. Schaeffer (2013), Neural networks to predict exosphere temperature corrections, Space Weather, 11, 592-602,

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Evaluation of the DTM-2009 thermosphere model for benchmarking purposes

Cited by 47 publications

References 24 publications

The DTM-2013 thermosphere model

The DTM-2013 thermosphere model

Progress in space weather modeling in an operational environment

Neural networks to predict exosphere temperature corrections

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