Bin Chen, Professor, Ph.D. supervisor. Received his B.Sc. and Ph.D. degrees from Lanzhou University in June 2007 and June 2012, respectively, and went on a two-year exchange at Scripps Institution of Oceanography, University of California, San Diego, USA, in October 2010. For further information, please contact: chenbin@lzu.edu.cn.
Teaching Courses:
1. Postgraduate course 'Big Data Analysis and Artificial Intelligence Application in Meteorology'
2. Postgraduate course 'Large-scale Dynamics' (2022-present)
3. Postgraduate course 'Climate and Climate System' (2021-present)
4. Postgraduate course 'Climate System and Global Change' (2018-2019)
5. Undergraduate course 'Dynamic Meteorology' (2013-present)
6. Undergraduate course 'Satellite Meteorology' (2014-2021)
7. Undergraduate course 'Fortran Language and Application' (2013)
Main research directions:
1. Satellite remote sensing and radiation climate effects (atmospheric aerosols, atmospheric pollutants).
2. Research on transmission characteristics and weather situation of synergistic pollution of atmospheric aerosols and atmospheric pollutants.
3. Application of machine learning and deep learning to atmospheric aerosols and atmospheric environment.
4. Research on epidemic big data and machine learning prediction and impacts.
Representative papers (* indicates corresponding author):
1. Chen, B.*, Y. Wang, J. Huang, et. al, Estimation of near-surface ozone concentration and analysis of main weather situation in China based on machine learning model and Himawari-8 TOAR data. Science of the Total Environment, 864, 160928, 2023, https://doi.org/10.1016/j.scitotenv.2022.160928. (The second author is a postgraduate student of the class of 2021)
2. Chen, B.*, Z. Song, J. Huang, et. al, Estimation of Atmospheric PM10 concentration in China using an interpretable deep learning model and top-of-the-atmosphere reflectance data from China's new generation geostationary meteorological satellite, FY-4A. Journal of Geophysical Research: Atmospheres,127, e2021JD036393, 2022, doi:10.1029/2021JD036393. (The second author is a postgraduate student of the class of 2020)
3. Chen, B.*, Song Z., et. al, Obtaining vertical distribution of PM2.5 from CALIOP data and machine learning algorithms. Science of the Total Environment, 805, 150338, 2022, https://doi.org/10.1016/j.scitotenv.2021.150338. (The second author is a postgraduate student of the class of 2020)
4. Chen, B.*, Song Z., et. al, An interpretable deep forest model for estimating hourly PM10 concentration in China using Himawari-8 data. Atmospheric Environment, 268, 118827, 2022, https://doi.org/10.1016/j.atmosenv.2021.118827. (The second author is a postgraduate student of the class of 2020)
5. Song, Z., B. Chen*, and J. Huang, Combining Himawari-8 AOD and deep forest model to obtain city-level distribution of PM2.5 in China. Environmental Pollution, 297, 118826, 2022, https://doi.org/10.1016/j.envpol.2022.118826. (The lead author is a postgraduate student of the class of 2020)
6. Song, Z., B. Chen*, et. al, High temporal and spatial resolution PM2.5 dataset acquisition and pollution assessment based on FY-4A TOAR data and deep forest model in China. Atmospheric Research, 274, 106199, 2022, https://doi.org/10.1016/j.atmosres.2022.106199. (The lead author is a postgraduate student of the class of 2020)
7. Song, Z., B. Chen*, et. al, Estimation of PM2.5 concentration in China using linear hybrid machine learning model. Atmospheric Measurement Techniques, 14, 5333–5347, 2021, https://doi.org/10.5194/amt-14-5333-2021. (The lead author is a postgraduate student of the class of 2020)
8. Dong, L., B. Chen*, et. al, Analysis on the Characteristics of Air Pollution in China during the COVID-19 Outbreak. Atmosphere, 12, 205, 2021, https://doi.org/10.3390/atmos12020205. (The lead author is a postgraduate student of the class of 2019)
9. Chen, B., Huang Y., Huang J., et. al, Using Lidar and Historical Similar Meteorological Fields to Evaluate the Impact of Anthropogenic Control on Dust Weather During COVID-19. Frontiers in Environmental Science, 9:806094, 2021, doi:10.3389/fenvs.2021.806094. (The second author is a postgraduate student of the class of 2018)
10. Chen B.*, P. Zhang, et. al, An overview of passive and active dust detection methods using satellite measurements. Journal of Meteorological Research, 28(6), 2014, doi:10.1007/s13351-014-4032-4.
11. Chen, B.*, J. Huang, et. al, Detection of dust aerosol by combining CALIPSO active lidar and passive IIR measurements. Atmospheric Chemistry and Physics, 10, 4241-4251, 2010, doi:10.5194/acp-10-4241-2010.
