Volume 32 Issue 2 - February 7, 2020 PDF
The Development and Application of Space Weather Forecasting System
Chia-Hung Chen1,*, Charles Lin1, Tomoko Matsuo2
1 Department of Earth Sciences, National Cheng Kung University, No. 1, University Road, Tainan City 701, Taiwan.
2 Ann and H.J. Smead Department of Aerospace Engineering Sciences, University of Colorado, Boulder, CO 80309-0429, USA.
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【108 MOST Ta-You Wu Memorial Award】Special Issue

    Recently, the self-driving car is getting more and more important. It contains the environment sensing, the safely driving, and the positioning systems. The space-based satellites transmit the radio signals to the ground-based receivers, which can provide the signal propagating and positioning services. When the radio signals pass through the ionosphere (~300 km altitude), however, it will be disturbed by the ionospheric plasma, resulting in the signal decay and the positioning error. If we can monitor and even predict the variations of ionospheric plasma, therefore, it is possible to effectively correct the radio signals and reduce the positioning error, which called as the “Space Weather” forecasting.
    Since the 1960s, the data assimilation methods have been employed in the numerical weather prediction (NWP). The development of satellite technology can further provide lots of temporal and spacial weather observations, which improve the accuracy of weather forecasting and the forecast available time. Our group, recently, focus on the development and the application of data assimilation methods into the higher atmospheric (or ionospheric) physics. Combining the ground-based GNSS networks and the space-based FORMOSAT observations, we have developed a space weather forecasting system to monitor and forecast the variations of space weather in the real-time [Chen et al., 2016a; 2016b; 2016c]. Not only the related application, we also successfully improve the evening ionosphere electric field modeling that has been studied theoretically in decades [Chen et al., 2017], as well as solve the physical question of the early appearance of equator ionization anomaly (EIA) during the solar eclipse period through this space weather forecasting system [Chen et al., 2019].

Chen, C. H., C. H. Lin, T. Matsuo, W. H., Chen, I. T., Lee, J. Y., Liu, J. T. Lin, and C. T., Hsu (2016a), Ionospheric data assimilation with thermosphere-ionosphere-electrodynamics general circulation model and GPS-TEC during geomagnetic storm conditions, Journal of Geophysical Research, 121, 5708-5722, doi:10.1002/2015JA021787.
Chen, C. H., C. H. Lin, J. Y. Liu, T. Matsuo, and W. H. Chen (2016b), Ionospheric electron density forecast during 2015 St. Patrick’s geomagnetic storm event, Journal of Geophysical Research, 121, 11,549-11,559, doi:10.1002/2016JA023346.
Chen, C. H., C. H. Lin, J. Y. Liu, T. Matsuo, and W. H., Chen (2016c), The impact of FORMOSAT-5/AIP on the ionospheric space weather, Terrestrial Atmospheric and Oceanic Sciences, 28, 129-137, doi:10.3319/TAO.2016.09.30.01(EOF5).
Chen, C. H., C. H. Lin, W.-H. Chen, and T. Matsuo (2017), Modeling the ionospheric prereversal enhancement by using coupled thermosphere-ionosphere data assimilation, Geophys. Res. Lett., 44, 1652–1659, doi:10.1002/2016GL071812.
Chen, C. H., C. H. Lin, and T. Matsuo (2019), Ionospheric responses to the 21 August 2017 solar eclipse by using data assimilation approach, Progress in Earth and Planetary Science, 6:13, doi:10.1186/s40645-019-0263-4.
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