Junzi Sun, Ph.D.

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Weather

Research in aviation weather is crucial for flight safety and efficiency. Advanced models and algorithms aim to improve real-time weather forecasting, mitigating risks and reducing operational costs. Continuous research in this area is vital for the aviation industry's growth and safety.

Highlights

Meteo-Particle model Python library
A Python library is introduced for wind field estimation based on the Meteo-Particle model. By employing ADS-B and Mode S data from aircraft, indirect wind and temperature measurements can be derived initially. These measurements are often unevenly distributed in the airspace. The Meteo-Particle model allows the assimilation of wind and temperature fields using these measurements, thereby overcoming the challenge posed by the distribution of the data.
Detection of turbulence from Mode S data in realtime
This is a join research with Xavier Oliver from ONERA, we present a novel method to detect turbulence experienced by aircraft based on Mode S data, emitted by transponders in reply to BDS 6,0 requests (heading and speed reports) sent by Secondary Surveillance Radars. By analyzing the variations in the vertical speed reported from different systems onboard, we are able to detect turbulence on ground in real time.

Related publications

  1. Polynomial Chaos Expansion-Based Enhanced Gaussian Process Regression for Wind Velocity Field Estimation from Aircraft-Derived Data
    Journal 2023
    DOI: 10.3390/math11041018
    Marinescu, Marius ; Olivares, Alberto ; Staffetti, Ernesto ; and Sun, Junzi
    Mathematics
    Copy Marinescu, M., Olivares, A., Staffetti, E., & Sun, J. (2023). Polynomial Chaos Expansion-Based Enhanced Gaussian Process Regression for Wind Velocity Field Estimation from Aircraft-Derived Data. Mathematics, 11(4), 1018. https://doi.org/10.3390/math11041018
    Copy 
    @article{marinescu2023polynomial,
  title = {Polynomial Chaos Expansion-Based Enhanced Gaussian Process Regression for Wind Velocity Field Estimation from Aircraft-Derived Data},
  author = {Marinescu, Marius and Olivares, Alberto and Staffetti, Ernesto and Sun, Junzi},
  journal = {Mathematics},
  volume = {11},
  number = {4},
  pages = {1018},
  year = {2023},
  publisher = {MDPI},
  link = {https://doi.org/10.3390/math11041018},
  doi = {10.3390/math11041018},
  tag = {meteo}
}
  2. Wind velocity field estimation from aircraft derived data using Gaussian process regression
    Journal 2022
    DOI: 10.1371/journal.pone.0276185
    Marinescu, Marius ; Olivares, Alberto ; Staffetti, Ernesto ; and Sun, Junzi
    Plos one
    Copy Marinescu, M., Olivares, A., Staffetti, E., & Sun, J. (2022). Wind velocity field estimation from aircraft derived data using Gaussian process regression. Plos One, 17(10), e0276185. https://doi.org/10.1371/journal.pone.0276185
    Copy 
    @article{marinescu2022wind,
  title = {Wind velocity field estimation from aircraft derived data using Gaussian process regression},
  author = {Marinescu, Marius and Olivares, Alberto and Staffetti, Ernesto and Sun, Junzi},
  journal = {Plos one},
  volume = {17},
  number = {10},
  pages = {e0276185},
  year = {2022},
  publisher = {Public Library of Science San Francisco, CA USA},
  link = {https://doi.org/10.1371/journal.pone.0276185},
  doi = {10.1371/journal.pone.0276185},
  tag = {meteo}
}
  3. METSIS: Hyperlocal Wind Nowcasting for U-space
    Conference 2021
    Sunil, Emmanuel ; Koerse, Ralph ; Selling, Stijn ; Doorn, Jan-Willem ; Brinkman, Thomas ; and Sun, Junzi
    In Proceedings of the 11th SESAR Innovation Days, Online
    Copy Sunil, E., Koerse, R., van Selling, S., van Doorn, J.-W., Brinkman, T., & Sun, J. (2021, December). METSIS: Hyperlocal Wind Nowcasting for U-space. Proceedings of the 11th SESAR Innovation Days, Online.
    Copy 
    @inproceedings{sunil2021metsis,
  title = {METSIS: Hyperlocal Wind Nowcasting for U-space},
  author = {Sunil, Emmanuel and Koerse, Ralph and van Selling, Stijn and van Doorn, Jan-Willem and Brinkman, Thomas and Sun, Junzi},
  booktitle = {Proceedings of the 11th SESAR Innovation Days, Online},
  year = {2021},
  month = dec,
  link = {https://www.sesarju.eu/sites/default/files/documents/sid/2021/papers/SIDs_2021_paper_88.pdf},
  tag = {meteo}
}
  4. Wind profile estimation from aircraft derived data using Kalman Filters and Gaussian Process Regression
    Conference 2021
    Marinescu, Marius ; Olivares, Alberto ; Staffetti, Ernesto ; and Sun, Junzi
    In 14th USA/Europe Air Traffic Management Research and Development Seminar
    Copy Marinescu, M., Olivares, A., Staffetti, E., & Sun, J. (2021, September). Wind profile estimation from aircraft derived data using Kalman Filters and Gaussian Process Regression. 14th USA/Europe Air Traffic Management Research and Development Seminar.
    Copy 
    @inproceedings{marinescu2021wind,
  title = {Wind profile estimation from aircraft derived data using Kalman Filters and Gaussian Process Regression},
  author = {Marinescu, Marius and Olivares, Alberto and Staffetti, Ernesto and Sun, Junzi},
  booktitle = {14th USA/Europe Air Traffic Management Research and Development Seminar},
  organization = {FAA/EUROCONTROL},
  year = {2021},
  month = sep,
  link = {https://research.tudelft.nl/files/105023305/ATM_Seminar_2021_paper_21.pdf},
  tag = {meteo}
}
  5. Detecting and Measuring Turbulence from Mode S Surveillance Downlink Data
    Conference 2020
    Olive, Xavier ; and Sun, Junzi
    In Proceedings of the 9th International Conference on Research in Air Transportation, Tampa, FL, USA
    Copy Olive, X., & Sun, J. (2020). Detecting and Measuring Turbulence from Mode S Surveillance Downlink Data. Proceedings of the 9th International Conference on Research in Air Transportation, Tampa, FL, USA, 23–26.
    Copy 
    @inproceedings{olive2020detectinh,
  title = {Detecting and Measuring Turbulence from Mode S Surveillance Downlink Data},
  author = {Olive, Xavier and Sun, Junzi},
  booktitle = {Proceedings of the 9th International Conference on Research in Air Transportation, Tampa, FL, USA},
  pages = {23--26},
  year = {2020},
  month = sep,
  link = {https://research.tudelft.nl/files/85733396/ICRAT2020_paper_3.pdf},
  tag = {meteo}
}
  6. Weather field reconstruction using aircraft surveillance data and a novel meteo-particle model
    Journal 2018
    DOI: 110.1371/journal.pone.0205029
    Sun, Junzi ; Vû, Huy ; Ellerbroek, Joost ; and Hoekstra, Jacco
    PloS one
    Copy Sun, J., Vû, H., Ellerbroek, J., & Hoekstra, J. (2018). Weather field reconstruction using aircraft surveillance data and a novel meteo-particle model. PloS One, 13(10), e0205029. https://doi.org/110.1371/journal.pone.0205029
    Copy 
    @article{sun2018mp,
  title = {Weather field reconstruction using aircraft surveillance data and a novel meteo-particle model},
  author = {Sun, Junzi and V{\^u}, Huy and Ellerbroek, Joost and Hoekstra, Jacco},
  journal = {PloS one},
  volume = {13},
  number = {10},
  pages = {e0205029},
  year = {2018},
  month = oct,
  publisher = {Public Library of Science},
  doi = {110.1371/journal.pone.0205029},
  link = {https://doi.org/10.1371/journal.pone.0205029},
  tag = {meteo}
}
  7. Ground-based Wind Field Construction from Mode-S and ADS-B Data with a Novel Gas Particle Model
    Conference 2017
    Sun, Junzi ; Vû, Huy ; Ellerbroek, Joost ; and Hoekstra, Jacco
    In Seventh SESAR Innovation Days
    Copy Sun, J., Vû, H., Ellerbroek, J., & Hoekstra, J. (2017). Ground-based Wind Field Construction from Mode-S and ADS-B Data with a Novel Gas Particle Model. Seventh SESAR Innovation Days, 28, 30th.
    Copy 
    @inproceedings{sun2017pwm,
  title = {Ground-based Wind Field Construction from Mode-S and ADS-B Data with a Novel Gas Particle Model},
  author = {Sun, Junzi and V{\^{u}}, Huy and Ellerbroek, Joost and Hoekstra, Jacco},
  booktitle = {Seventh SESAR Innovation Days},
  pages = {30th},
  volume = {28},
  year = {2017},
  month = dec,
  link = {https://www.sesarju.eu/sites/default/files/documents/sid/2017/SIDs_2017_paper_16.pdf},
  tag = {meteo}
}

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