Publications related to the GRACE Missions (no abstracts)

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Robust Short–Term Forecasting of Thermospheric Density and Orbit Prediction Using an Interpretable iTransformer Model

Zhang, Mingyi, Meng, Xiangguang, Wang, Xin, Sun, Yueqiang, Du, Qifei, Bai, Weihua, Hu, Peng, and Zhou, Zhiqi, 2026. Robust Short–Term Forecasting of Thermospheric Density and Orbit Prediction Using an Interpretable iTransformer Model. Space Weather, 24(9):e2026SW005105, doi:10.1029/2026SW005105.

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@ARTICLE{2026SpWea..2405105Z,
       author = {{Zhang}, Mingyi and {Meng}, Xiangguang and {Wang}, Xin and {Sun}, Yueqiang and {Du}, Qifei and {Bai}, Weihua and {Hu}, Peng and {Zhou}, Zhiqi},
        title = "{Robust Short-Term Forecasting of Thermospheric Density and Orbit Prediction Using an Interpretable iTransformer Model}",
      journal = {Space Weather},
     keywords = {thermospheric density forecast, iTransformer, orbit prediction},
         year = 2026,
        month = sep,
       volume = {24},
       number = {9},
          eid = {e2026SW005105},
        pages = {e2026SW005105},
     abstract = "{Accurate short--term forecasting of thermospheric mass density is
        crucial for satellite operations. However, achieving high--
        precision predictions remains a significant challenge. In this
        study, we develop an iTransformer framework that forecasts
        thermospheric density over the next 24 hr using observations
        from the preceding 24 hr. Using density data from multiple low
        Earth orbit satellites (CHAMP, GRACE--A, GRACE--C, GOCE and
        Tianmu--1/TM02), we investigate two strategies: directly
        predicting density values and learning the residuals relative to
        the MSISE--00 empirical baseline. Performance assessments
        against independent observations reveal that the residual--based
        strategy (iTrans--Res) generally provides higher accuracy than
        the direct forecasting approach (iTrans--Dir), the original and
        bias--corrected MSISE--00 (MSISE--Res) and a residual--based Bi
        --LSTM (Bi--LSTM--Res) baseline. For CHAMP in 2008, iTrans--Res
        reaches R$^{2}$ = 0.9052 and reduces MAPE from 77.66\% to
        11.56\%; for GRACE--C in 2024, it reaches R$^{2}$ = 0.9432 and
        reduces MAPE from 38.77\% to 9.58\%. Storm--event analyses show
        that iTrans--Res more closely reproduces the enhanced density
        peaks and substantially reduces the recovery--phase bias
        inherited from MSISE--00. Variate--attention analysis reveals
        model--internal associations among the spatiotemporal and space
        --weather inputs, with geomagnetic ap information broadly
        integrated into the variable representations. Orbit propagation
        tests demonstrate that iTrans--Res reduces the 24--hr maximum
        position error by 24.6\% for CHAMP and 16.3\% for TM02.
        Robustness tests with noise--injected inputs confirm that iTrans
        --Res maintains reliability even under moderate space weather
        forecasting uncertainties.}",
          doi = {10.1029/2026SW005105},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2026SpWea..2405105Z},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

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