Yanjun Gan

Image of Yanjun Gan

Position

Research Scientist

Division

Modeling and Data Assimilation

Affiliation

CIRES

Contact

(720) 593-1649

yanjun.gan@noaa.gov

About

Dr. Yanjun Gan is a Research Scientist II at the Cooperative Institute for Research in Environmental Sciences (CIRES) at the University of Colorado Boulder (CU Boulder), collaborating with NOAA's Physical Sciences Laboratory (PSL). Prior to joining CU Boulder, Dr. Gan worked at the University of Texas at Arlington from 2019 to 2024, first as a Research Engineering Scientist and later as an Assistant Professor of Research. From 2015 to 2019, he was with the Chinese Academy of Meteorological Sciences (CAMS), where he began as an Assistant Research Scientist and was later promoted to Associate Research Scientist.

Dr. Gan is currently focusing on improving snow data assimilation within the Unified Forecast System (UFS) to enhance predictions of precipitation extremes. His research aims to refine the land data assimilation system by introducing a scheme that assimilates screen-level temperature observations to update model snow temperatures and expanding the assimilation of snow cover observations to optimize key model parameters. These improvements are expected to lead to more accurate simulations of snowpack, surface radiation balance, boundary layer processes, and ultimately, precipitation.

Research Interests

  • Land Data Assimilation
  • Uncertainty Quantification
  • Hydrometeorology
  • Water Resources

Education

  • Ph.D., Global Environmental Change, Beijing Normal University, 2015
  • M.S., Hydrology and Water Resources, Wuhan University, 2010
  • B.S., Hydrology and Water Resources, Wuhan University, 2008

Selected Publications

  • Gan, Y., Zhang, Y., Kongoli, C., & Pan, M. (2024). The role of forcing and parameterization in improving snow simulation in the Upper Colorado River Basin using the National Water Model. Water Resources Research, 60, e2023WR035303. https://doi.org/10.1029/ 2023WR035303
  • Gan, Y., Zhang, Y., Liu, Y., Kongoli, C., & Grassotti, C. (2022). Assimilation of blended in situ-satellite snow water equivalent into the National Water Model for improving hydrologic simulation in two US river basins. Science of the Total Environment, 838, 156567. https://doi.org/10.1016/j.scitotenv.2022.156567
  • Gan, Y., Zhang, Y., Kongoli, C., Grassotti, C., Liu, Y., Lee, Y.-K., & Seo, D.-J. (2021). Evaluation and blending of ATMS and AMSR2 snow water equivalent retrievals over the conterminous United States. Remote Sensing of Environment, 254, 112280. https://doi.org/10.1016/j.rse.2020.112280
  • Gan, Y., Liang, X.-Z., Duan, Q., Chen, F., Li, J., & Zhang, Y. (2019). Assessment and reduction of the physical parameterization uncertainty for Noah-MP land surface model. Water Resources Research, 55 (7), 5518–5538. https://doi.org/10.1029/2019WR024814
  • Gan, Y., Duan, Q., Gong, W., Tong, C., Sun, Y., Chu, W., Ye, A., Miao, C., & Di, Z. (2014). A comprehensive evaluation of various sensitivity analysis methods: A case study with a hydrological model. Environmental Modelling & Software, 51, 269–285. https://doi.org/10.1016/j.envsoft.2013.09.031

Presentations

Professional Memberships

  • American Geophysical Union
  • European Geosciences Union
  • American Meteorological Society

Related Links