Chengsi Liu

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Chengsi Liu

Research
Scientist III

cliu@ou.edu
chengsi.Liu@noaa.gov
National Weather Center 4351

Education

PhD, Geophysical Fluid Dynamics, Institute of Atmospheric Physics Chinese Academy of Sciences
MS, Atmosphere Science, Chinese Academy of Meteorological Sciences
BS, Atmosphere Science, Nanjing University of Informational Science and Technology

Affiliated CIWRO Team

Data Assimilation and Modeling

About

Dr. Chengsi Liu is a Research Scientist III at the Cooperative Institute for Severe and High-Impact Weather Research and Operations (CIWRO), working within the Data Assimilation and Modeling Team in collaboration with the NOAA National Severe Storms Laboratory (NSSL). Before joining CIWRO in 2025, Dr. Liu served as a Senior Research Scientist and data assimilation team lead at the Center for Analysis and Prediction of Storms (CAPS) at the University of Oklahoma. While at CAPS, he was the principal developer of the APRS hybrid ensemble variational system and a primary contributor to radar data assimilation capabilities within the GSI and JEDI frameworks. He also serves as a Co-PI for the NOAA CADRE (Consortium for Advanced Data Assimilation Research and Education) program, supporting the research-to-operations transition of advanced data assimilation methods.

Currently, Dr. Liu’s research focuses on advancing NOAA’s Warn-on-Forecast System (WoFS) program by developing and optimizing experimental systems using the Model for Prediction Across Scales (MPAS) and the JEDI framework. His current research priorities include refining configurations for MPAS-JEDI-based WoFS and transitioning these systems to cloud-based environments. Furthermore, he is pioneering the integration of machine learning to build hybrid AI-DA systems for enhanced severe weather forecasting.

Research Focus

Next-Generation WoFS: Development and optimization of MPAS-JEDI-based Warn-on-Forecast systems
AI for Weather: Integration of machine learning and deep learning into data assimilation and NWP frameworks
Convective-Scale DA: Advanced ensemble and variational techniques for high-resolution severe weather modeling
Radar DA: Implementation of novel radar reflectivity operators and direct assimilation strategies

CIWRO Research Themes

Mesoscale and Stormscale Modeling R&D

Honors and awards

Annual Award for Excellence in Research Grants, University of Oklahoma Research & Creative Activity Awards (2025)

Publications

Liu, C., and M. Xue, 2026: Direct Assimilation of Radar Reflectivity using Hybrid Ensemble-Variational Method: Design, Treatments, and Evaluation. Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications, S. K. Park and L. Xu, Eds., Springer, In press.
Tong, C.-C., C. Liu, and M. Xue, 2026: Radar data assimilation with JEDI LETKF for ensemble forecasting of hurricane Ida (2021) using a HAFS-like configuration. Mon. Wea. Rev., Accepted.
Kong, R., M. Xue, C. Liu, J. Park, A. Back, and E. R. Mansell, 2025: Assimilation of GOES-R Geostationary Lightning Mapper Flash Extent Density in JEDI LETKF, LGETKF, and En3DVar: Development of Assimilation Capabilities and Test with a Convective Storm Case over the US. Mon. Wea. Rev., 153, 2867–2887, https://doi.org/10.1175/MWR-D-25-0003.1.
Kong, R., M. Xue, E. R. Mansell, C. Liu, and A. O. Fierro, 2024: Assimilation of GOES-R Geostationary Lightning Mapper Flash Extent Density Data in GSI 3DVar, EnKF, and Hybrid En3DVar for the Analysis and Short-Term Forecast of a Supercell Storm Case. Adv. Atmos. Sci., 41, 263–277.
Tong, C.-C., M. Xue, C. Liu, J. Luo, and Y. Jung, 2024: Development of Multi-Scale EnKF within GSI and Its Applications to Multiple Convective Storm Cases with Radar Reflectivity Data Assimilation Using the FV3 Limited Area Model. Mon. Wea. Rev., 152, 1839–1857.
Park, J., M. Xue, and C. Liu, 2023: Implementation and Testing of Radar Data Assimilation Capabilities within the Joint Effort for Data assimilation Integration (JEDI) Framework with Ensemble Transformation Kalman Filter coupled with FV3-LAM Model. Geophys. Res. Lett., 50, e2022GL102709, https://doi.org/10.1029/2022GL102709.
Hu, J., J. Gao, C. Liu, G. Zhang, P. Heinselman, and J. T. Carlin, 2023: Test of Power Transformation Function to Hydrometeor and Water Vapor Mixing Ratios for Direct Variational Assimilation of Radar Reflectivity Data. Weather Forecasting, 38, https://doi.org/10.1175/WAF-D-22-0158.1.
Liu, C., H. Li, M. Xue, Y. Jung, J. Park, L. Chen, R. Kong, and C.-C. Tong, 2022: Use of a Reflectivity Operator Based on Two-Moment Thompson Microphysics for Direct Assimilation of Radar Reflectivity in GSI-based Hybrid En3DVar. Mon. Wea. Rev., 150, 907–926, https://doi.org/10.1175/MWR-D-21-0040.1.
Kong, R., M. Xue, C. Liu, A. O. Fierro, and E. R. Mansell, 2022: Development of new observation operators for assimilating GOES-R geostationary lightning mapper flash extent density data using GSI EnKF: Tests with two convective events over the US. Mon. Wea. Rev., https://doi.org/10.1175/MWR-D-21-0326.1.
Li, H., C. Liu, M. Xue, J. Park, L. Chen, Y. Jung, R. Kong, and C.-C. Tong, 2022: Use of power transform total number concentration as control variable for direct assimilation of radar reflectivity in GSI En3DVar and tests with six convective storms cases. Mon. Wea. Rev., 150, 821–842, https://doi.org/10.1175/MWR-D-21-0041.1.
Chen, L., C. Liu, Y. Jung, P. Skinner, M. Xue, and R. Kong, 2022: Object-based Verification of GSI-based EnKF and Hybrid En3DVar Radar Data Assimilation and Convection-Allowing Forecasts within a Warn-on-Forecast Framework. Weather Forecasting, 37, 639–658, https://doi.org/10.1175/WAF-D-20-0180.1.
Chen, L., C. Liu, M. Xue, G. Zhao, R. Kong, and Y. Jung, 2021: Use of Power Transform Mixing Ratios as Hydrometeor Control Variables for Direct Assimilation of Radar Reflectivity in GSI-based En3DVar and Tests with Five Convective Storms Cases. Mon. Wea. Rev., 149, 645–659, https://doi.org/10.1175/MWR-D-20-0149.1.
Labriola, J., Y. Jung, C. Liu, and M. Xue, 2021: Evaluating forecast performance and sensitivity to the GSI EnKF data assimilation configuration for the 28–29 May 2017 mesoscale convective system case. Weather Forecasting, 36, 127–146, https://doi.org/10.1175/WAF-D-20-0071.1.
Kong, R., M. Xue, C. Liu, and Y. Jung, 2021: Comparisons of Hybrid En3DVar with 3DVar, and EnKF for Radar Data Assimilation: Tests with the 10 May 2010 Oklahoma Tornado outbreak. Mon. Wea. Rev., https://doi.org/10.1175/MWR-D-20-0053.1.
Liu, C., M. Xue, and R. Kong, 2020: Direct variational assimilation of radar reflectivity and radial velocity data: Issues with nonlinear reflectivity operator and solutions. Mon. Wea. Rev., 148, 4083–4102, DOI: 10.1175/MWR-D-19-0149.1.
Kong, R., M. Xue, A. O. Fierro, Y. Jung, C. Liu, E. R. Mansell, and D. R. MacGorman, 2020: Assimilation of GOES-16 Geostationary Lightning Mapper Flash Extent Density Data in GSI EnKF for the Analysis and Short Term Forecast of a Mesoscale Convective System. Mon. Wea. Rev., 148, 2111–2133, DOI: 10.1175/MWR-D-19-0192.1.
Tong, C.-C., Y. Jung, M. Xue, and C. Liu, 2020: Direct assimilation of radar data within the National Weather Service operational GSI EnKF and hybrid En3DVar systems for the stand-alone regional FV3 model at a convection-allowing resolution. Geophys. Res. Lett., 47, e2020GL090179.
Liu, C., M. Xue, and R. Kong, 2019: Direct assimilation of radar reflectivity data using 3DVAR: Treatment of hydrometeor background errors and OSSE tests. Mon. Wea. Rev., 147, 17–29.
Kong, R., M. Xue, and C. Liu, 2018: Development of a hybrid en3DVar data assimilation system and comparisons with 3DVar and EnKF for radar data assimilation with observing system simulation experiments. Mon. Wea. Rev., 146, 175–198.
Liu, C., and M. Xue, 2016: Relationships among four-dimensional hybrid ensemble-variational data assimilation algorithms with full and approximate ensemble covariance localization. Mon. Wea. Rev., 144, 591–606.
Liu, C., and Q. Xiao, 2013: An ensemble-based four-dimensional variational data assimilation scheme. Part III: Antarctic applications with advanced research WRF using real data. Mon. Wea. Rev., 141, 2721–2739.
Chu, K., Q. Xiao, and C. Liu, 2013: Experiments of the WRF three-/four-dimensional variational ($3/4$DVAR) data assimilation in the forecasting of Antarctic cyclones. Meteor. Atmos. Phys., 120, 145–156.
Wang, B., J. Liu, S. Wang, W. Cheng, J. Liu, C. Liu, Q. Xiao, and Y.-H. Kuo, 2010: An Economical Approach to Four-dimensional Variational Data Assimilation. Adv. Atmos. Sci., 27, 715–727.
Liu, C., Q. Xiao, and B. Wang, 2009: An Ensemble-Based Four-Dimensional Variational Data Assimilation Scheme. Part II: Observing System Simulation Experiments with Advanced Research WRF (ARW). Mon. Wea. Rev., 137, 1687–1704.
Liu, C., Q. Xiao, and B. Wang, 2008: An Ensemble-Based Four-Dimensional Variational Data Assimilation Scheme. Part I: Technical Formulation and Preliminary Test. Mon. Wea. Rev., 136, 3363–3373.
Liu, C., and J. Xue, 2005: The Development of the Theory and Method of the EnKF. J. Trop. Meteor. (China Edition), 11, 625–632.