@COMMENT This file was generated by bib2html_grace.pl <https://sourceforge.net/projects/bib2html/> version 0.94
@COMMENT written by Patrick Riley <https://sourceforge.net/users/patstg/>
@COMMENT This file was prepared using the NASA Astrophysics Data System (ADS)
@COMMENT https://ui.adsabs.harvard.edu/
@ARTICLE{2026ESS...1304914C,
       author = {{Constant}, Charles and {Brownhall}, Indigo and {Aruliah}, Anasuya and {Ziebart}, Marek and {Bhattarai}, Santosh},
        title = "{Empirical Assessment of Storm-Time Thermospheric Density Inversion Methods From LEO POD Data}",
      journal = {Earth and Space Science},
     keywords = {thermospheric density, geomagnetic storms, precise orbit determination, energy dissipation rate, low Earth orbit, data assimilation},
         year = 2026,
        month = aug,
       volume = {13},
       number = {8},
          eid = {e2025EA004914},
        pages = {e2025EA004914},
     abstract = "{Thermospheric mass density is one of the largest sources of operational
        uncertainty for spacecraft in low Earth orbit, particularly
        during geomagnetic storms. The growing population of Global
        Navigation Satellite System--equipped satellites presents a data
        set of opportunity: precise orbit determination (POD) data
        streams can be used to estimate thermospheric density along
        their paths. We present a multi--timescale benchmark of the two
        main cooperative density inversion methods, the Energy
        Dissipation Rate method and POD--accelerometry, against
        accelerometer--derived effective densities from the CHAMP and
        GRACE--FO--A satellites over 49 geomagnetic storms. At orbit--
        effective cadence, both methods achieve ${r}^{2}\ge 0.97$ and a
        log--normal scatter $\text{SD\%}\le 22\%$, and are statistically
        indistinguishable in typical--orbit scatter. However, the Energy
        Dissipation Rate method tracks storm--time variations more
        faithfully and produces fewer large--error orbits (${r}^{2}$
        0.992 vs. 0.985; RMS error 12.0\% vs. 13.5\%), at roughly
        $6\times $ lower computational cost. Increasing the fit--span to
        three orbits reduces $\text{SD\%}$ to 13\%--14\% for both
        methods against matched--interval accelerometer effective
        densities, but ${r}^{2}$ against unaveraged (10 s) accelerometer
        data drops sharply beyond about half an orbit. Optimizing the
        arc length below one full orbit offers an attractive compromise,
        achieving ${r}^{2}=0.93$--0.94 and $\text{SD\%}\approx
        42$--$46\%$ against unaveraged accelerometer data. Retrieval
        accuracy depends strongly on drag--acceleration magnitude, with
        RMS errors below 10\% where drag exceeds $4\times
        1{0}^{-7}\,\mathrm{m}\,{\mathrm{s}}^{-\mathrm{2}}$ but growing
        above 30\% below
        $1{0}^{-8}\,\mathrm{m}\,{\mathrm{s}}^{-\mathrm{2}}$. These
        results provide empirical observation--error statistics for
        storm--time POD--derived density retrievals, directly relevant
        to next--generation assimilative models.}",
          doi = {10.1029/2025EA004914},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2026E&SS...1304914C},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}