Publications related to the GRACE Missions (no abstracts)

• Sorted by Date • Sorted by Last Name of First Author •

A Machine Learning-Based Thermospheric Density Model with Uncertainty Quantification

Li, Junzhi, Ning, Xin, and Wang, Yong, 2025. A Machine Learning-Based Thermospheric Density Model with Uncertainty Quantification. Atmosphere, 16(10):1120, doi:10.3390/atmos16101120.

Downloads

• from the NASA Astrophysics Data System  • by the DOI System  •

@ARTICLE{2025Atmos..16.1120L,
       author = {{Li}, Junzhi and {Ning}, Xin and {Wang}, Yong},
        title = "{A Machine Learning-Based Thermospheric Density Model with Uncertainty Quantification}",
      journal = {Atmosphere},
     keywords = {exospheric temperature, neural network, thermospheric density},
         year = 2025,
        month = sep,
       volume = {16},
       number = {10},
          eid = {1120},
        pages = {1120},
     abstract = "{Conventional thermospheric density models are limited in their ability
        to capture solar-geomagnetic coupling dynamics and lack
        probabilistic uncertainty estimates. We present MSIS-UN
        (NRLMSISE-00 with Uncertainty Quantification), an innovative
        framework integrating sparse principal component analysis (sPCA)
        with heteroscedastic neural networks. Our methodology leverages
        multi-satellite density measurements from the CHAMP, GRACE, and
        SWARM missions, coupled with MSIS-00-derived exospheric
        temperature (tinf) data. The technical approach features three
        key innovations: (1) spherical harmonic decomposition of
        T{\ensuremath{\infty}} using spatiotemporally orthogonal basis
        functions, (2) sPCA-based extraction of dominant modes from
        sparse orbital sampling data, and (3) neural network prediction
        of temporal coefficients with built-in uncertainty
        quantification. This integrated framework significantly enhances
        the temperature calculation module in MSIS-00 while providing
        probabilistic density estimates. Validation against SWARM-C
        measurements demonstrates superior performance, reducing mean
        absolute error (MAE) during quiet periods from MSIS-00's 44.1\%
        to 23.7\%, with uncertainty bounds (1{\ensuremath{\sigma}})
        achieving an MAE of 8.4\%. The model's dynamic confidence
        intervals enable rigorous probabilistic risk assessment for LEO
        satellite collision avoidance systems, representing a paradigm
        shift from deterministic to probabilistic modeling of
        thermospheric density.}",
          doi = {10.3390/atmos16101120},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2025Atmos..16.1120L},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

Generated by bib2html_grace.pl (written by Patrick Riley modified for this page by Volker Klemann) on Mon Dec 15, 2025 18:11:59

---

Mon Dec 15, F. Flechtner