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Man Zhang

Man Zhang is a CIRES Research Scientist at the NOAA Global Systems Laboratory and the Developmental Testbed Center (DTC). Her research focuses on advancing atmospheric physics for both research and operational forecasting systems, with particular emphasis on the implementation, integration, and rigorous evaluation of physical parameterizations within NOAA’s Unified Forecast System (UFS).

Since 2016, Dr. Zhang has been dedicated to community-based efforts to bridge research and operations (R2O). She played a key role in the development of the Common Community Physics Package (CCPP), contributing to the separation, modernization, and evaluation of physics schemes in NOAA’s Global Forecast System (GFS v15) and the Hurricane Weather Research and Forecasting model (HWRF).

Currently, Dr. Zhang leads the DTC’s Physics Testing and Evaluation team, overseeing multiscale assessments of physics innovations across UFS applications—from sub-seasonal to seasonal forecasting to convection-allowing models. Her research emphasizes planetary boundary layer processes, moist convection, cloud microphysics, cloud–aerosol–radiation interactions, and land–atmosphere coupling—critical components for improving weather and climate predictions.

Dr. Zhang also contributes substantially to community engagement efforts, including UFS CCPP physics scientific documentation, training, and collaborative development. Through her leadership, she fosters an inclusive, open-science environment that strengthens the operational relevance of community-developed model components.

Research Interests

  • Numerical weather prediction
  • Atmospheric physics
  • Advanced data assimilation technology

Education

  • Ph.D. in Meteorology – Chinese Academy of Sciences
  • M.S. in Meteorology – Chinese Academy of Sciences
  • B.S. in Atmospheric Dynamics – Nanjing Institute of Meteorology

Experience

  • Research Scientist (2013-present), CU/CIRES and NOAA/GSL and DTC
  • Research Scientist (2010-2013), CSU/CIRA
  • Research staff (2004-2007), Department of Atmospheric and Oceanic Sciences, University of Maryland

Selected Publications

  • Bernardet, L., L. Bengtsson, P.A. Reinecke, F. Yang, Zhang M., K. Hall, J. Doyle, M. Martini, G. Girl, and L. Xue, 2024: Common Community Physics Package: Fostering Collaborative Development in Physical Parameterizations and Suites. Bulletin of the American Meteorological Society.   https://doi.org/10.1175/BAMS-D-23-0227.1
  • Heinzeller, D., Bernardet, L. R., Firl, G. J., Zhang, M., Sun, X., and Ek, M. B., 2023: The Common Community Physics Package (CCPP) Framework v6, Geosci. Model Dev., 16, 2235-2259,https://doi.org/10.5194/gmd-16-2235-2023, 2023.
  • Boukabara  S. A. et al., 2016 (co-author): S4: An O2R/R2O Infrastructure for Optimizing Satellite Data Utilization in NOAA Numerical Modeling Systems – A Step Toward Bridging the Gap between Research and Operations. Bulletin of the American Meteorological Society, 97:12, https://doi.org/10.1175/BAMS-D-14-00188.1, 2359-2378
  • Wang H. L., X. Y. Huang, J. Z. Sun, D. M. Xu, Zhang M., S. Y. Fan and J. Q. Zhong, 2014: Inhomogeneous background error modeling for WRF-Var using the NMC method. J. Appl. Meteor. Climatol., 53 (10), 2287-2309
  • Zhang M., M. Zupanski, M.-J. Kim, and J. A. Knaff, 2013: Assimilating AMSU-A radiances in TC core area with NOAA operational HWRF and a hybrid data assimilation system: Danielle (2010). Monthly Weather Review, 141, 3889-3907
  • Zhang M., D.-L. Zhang, 2012: Sub-kilometer simulation of a torrential-rain-producing mesoscale convective system in East China. Part I: Model verification and convective organization. Monthly Weather Review, 140, 184-201
  • Zhang M., D.-L. Zhang, and A.-S. Wang, 2009: Numerical simulation of torrential rainfall and vortical hot towers in a midlatitude mesoscale convective system. Atmospheric and Oceanic Science Letters, 2(4): 189-193

    • Our Mission

    Lead research and directed development through the transition of environmental data, models, products, tools, and services to support commerce, protect life and property, and promote a scientifically literate public.

    Research Areas

    Organizational Excellence, Earth System Prediction, Advanced Technologies, and Decision Support are the foundation to achieving the GSL Grand Challenge: Deliver actionable global storm-scale prediction and environmental information through advanced technologies to serve society.

    Global Systems Laboratory