Ligia Bernardet

As Chief of the Earth Prediction Advancement Division at GSL, my work bridges research and operations in weather and fire prediction. I plan and support the development of forecast models, focusing on convective-allowing and higher-resolution models that leverage physical principles and artificial intelligence. Our objective is to enhance the representation and prediction of clouds, precipitation, turbulence, fire weather, and atmospheric composition. To accelerate innovation, I foster synergistic interactions between the research and operational communities. The products we create enable research and generate numerical guidance for National Weather Service forecasters, ultimately contributing to a weather-ready nation.

Research Interests

• Weather prediction with physical and AI-based methods
• Atmospheric physics
• Convective systems
• Forecast verification and assessment
• Model infrastructure
• Community support and outreach

Education

• B. S. in Meteorology from the University of Sao Paulo, Brazil
• M. S. in Atmospheric Sciences from the University of Sao Paulo, Brazil
• Ph. D. in Atmospheric Sciences from Colorado State University

Experience

• GSL Earth Prediction Advancement Division chief (2023 – present)
• Meteorologist at the NOAA Global Systems Laboratory, Boulder, CO (2019 – present)
• Scientist at the Cooperative Institute for Research in the Atmosphere (CIRES) at the University of Colorado, Boulder, CO (2010 – 2019)
• Scientist at the Systems Research Group, Boulder, CO (2003-2010)
• Scientist at the Interamerican Institute for Cooperation in Agriculture, Brazil (1999-2001)
• National Research Council postdoctoral fellow, Boulder, CO (1997-1999)

Professional Activities

• Member, American Meteorological Society
• Member, Unified Forecast System (UFS) Steering Committee
• Member, UFS Physics Working Group
• Technical Lead, NOAA MPAS-in-UFS Project
• Co-lead, Community Earth System Model (CESM) Software Engineering Working Group 
• Co-lead, UFS R2O Project Infrastructure Cross Cutting Team (CCT)
• Member, UFS Software Architecture and Infrastructure CCT
• Member, UFS Release Coordination CCT
• Stakeholder, Earth Prediction Innovation Center (EPIC) 
• GSL representative, Model for Prediction Across Scales (MPAS) Cross-Agency Management

Related links

• Unified Forecast System
• Earth Prediction Innovation Center (EPIC)
• Developmental Testbed Center
• Common Community Physics Package

Peer-Reviewed Publications

Li, W., D. D’Amico, L. Bernardet et al., 2025. Demonstrating Hierarchical System Development With the Common Community Physics Package Single-Column Model: A Case Study Over the Southern Great Plains. RMetS Meteorological Applications, 32, 4. http://dx.doi.org/10.1002/met.70073.

Bernardet, L., and Coauthors, 2024: Common Community Physics Package: Fostering Collaborative Development in Physical Parameterizations and Suites. Bull. Amer. Meteor. Soc., 105, E1490–E1505, https://doi.org/10.1175/BAMS-D-23-0227.1.

Firl, G., L. Bernardet, L. Xue, D. Swales, L. Fowler, C. Peverly, M. Xue, and F. Yang, 2024: Envisioning the Future of Community Physics. Bull. Amer. Meteor. Soc., 105, E639–E644, https://doi.org/10.1175/BAMS-D-24-0001.1.

Heinzeller, D., Bernardet, L., Firl, G., Zhang, M., Sun, X., and Ek, M., 2023: The Common Community Physics Package (CCPP) Framework v6, Geosci. Model Dev., 16, 2235–2259, https://doi.org/10.5194/gmd-16-2235-2023.

Newman, K. M., B. Brown, J. H. Gotway, L. Bernardet, M. Biswas, T. Jensen, and L. Nance, 2023: Advancing Tropical Cyclone Precipitation Forecast Verification Methods and Tools. Wea. Forecasting, 38, 1589–1603, https://doi.org/10.1175/WAF-D-23-0001.1.

Sun, X., D. Heinzeller, L. Bernardet, L. Pan, W. Li, D. Turner, and J. Brown, 2023: A Case Study Investigating the Low Summertime CAPE Behavior in the Global Forecast System. Wea. Forecasting, 39, 3–17, https://doi.org/10.1175/WAF-D-22-0208.1.

Liu Q, Zhang X, Tong M, Zhang Z, Liu B, Wang W, Zhu L, Zhang B, Xu X, Trahan S, Bernardet L, Mehra A, Tallapragada V, 2020. Vortex Initialization in the NCEP Operational Hurricane Models. Atmosphere; 11(9):968. https://doi.org/10.3390/atmos1109096.8.

Bao, S., L. Bernardet, G. Thompson, E. Kalina, K. Newman, and M. Biswas, 2020: Impact of the Hydrometeor Vertical Advection Method on HWRF’s Simulated Hurricane Structure. Wea. Forecasting, 35, 723–737, https://doi.org/10.1175/WAF-D-19-0006.1.

Biswas, M. K., (…), L. Bernardet, and Coauthors, 2020: Evaluation of the Grell–Freitas Convective Scheme in the Hurricane Weather Research and Forecasting (HWRF) Model. Wea. Forecasting, 35, 1017–1033, https://doi.org/10.1175/WAF-D-19-0124.1.

Bernardet, L., L. Carson, and V. Tallapragada, 2017. The Design of a Modern Information Technology Infrastructure to Facilitate Research-to-Operations Transition for NCEP’s Modeling Suites. Bull. Amer. Meteor. Soc., 98, 899–904, https://doi.org/10.1175/BAMS-D-15-00139.1.

Bernardet L., V. Tallapragada, S. Bao, S. Trahan, Y. Kwon, Q. Liu, M. Tong, M. Biswas, T. Brown, D. Stark, L. Carson, R. Yablonsky, E. Uhlhorn, S. Gopalakrishnan, X. Zhang, T. Marchok, Y.-H. Kuo, and R. Gall, 2015. Community support and transition of research to operations for the Hurricane Weather Research and Forecast (HWRF) model. Bull. of the Amer. Meteor. Soc, 96, 6. doi: http://dx.doi.org/10.1175/BAMS-D-13-00093.1.

Tallapragada, V., L. Bernardet, M. K. Biswas, I. Ginis, Y. Kwon, Q. Liu, T. Marchok, D. Sheinin, B. Thomas, M. Tong, S. Trahan, W. Wang, R. Yablonsky, X. Zhang, 2015: Hurricane Weather Research and Forecasting (HWRF) Model: 2015 Scientific Documentation, NCAR/TN-522+STR. 122pp. Available at https://opensky.ucar.edu/islandora/object/technotes%3A535.

Yablonsky, R. M., I. Ginis, B. Thomas, V. Tallapragada, D. Sheinin, and L. Bernardet, 2015. Description and analysis of the ocean component of NOAA’s operational Hurricane Weather Research and Forecasting (HWRF) model. J. Atmos. and Oceanic Tech., 32, 144-163. http://dx.doi.org/10.1175/JTECH-D-14-00063.1. 

Biswas, M. K., L. Bernardet, and J. Dudhia, 2014. Sensitivity of hurricane forecasts to cumulus parameterizations in the HWRF model. Geophys. Res. Lett, 41, doi:10.1002/2014GL062071. https://agupubs.onlinelibrary.wiley.com/doi/10.1002/2014GL062071. 

Doyle, J. D., S. Gaberšek, Q. Jiang, L. Bernardet, J. M. Brown, A. Dörnbrack, E. Filaus, V. Grubišić, D. Kirshbaum, O. Knoth, S. Koch, I. M. Stiperski, S. Vosper, S. Zhong, 2011. An intercomparison of T- REX mountain wave simulations and implications for mesoscale predictability. Mon. Wea. Rev., 139, 2811-2831. Available at https://doi.org/10.1175/MWR-D-10-05042.1.

Bernardet, L., L. Nance, M. Demirtas, S. Koch. E. Szoke, T. Fowler, A. Loughe, J. L. Mahoney, H.-Y. Chuang, M. Pyle, and R. Gall, 2008. The Developmental Testbed Center and its Winter Forecasting Experiment. Bull. Amer. Meteor. Soc., 89, 611-627. https://doi.org/10.1175/BAMS-89-5-611. 

Bernardet, L. R., 2003. Verification of high-resolution precipitation forecasts for the Atlanta Olympic Games. Natl. Wea. Assoc. Digest, 26:3-4, 19-36. 

Bernardet, L. R., L. D. Grasso, J. E. Nachamkin, C. A. Finley and W. R. Cotton, 2000. Simulating convective events using a high-resolution mesoscale model. J. Geophys. Res., 105 D11, 14,963-14,982. 

Bernardet, L. R. and W. R. Cotton, 1998. Multiscale evolution of a derecho-producing mesoscale convective system. Mon. Wea. Rev., 126, 2991-3015. https://journals.ametsoc.org/doi/abs/10.1175/1520-0493%281998%29126%3C2991%3AMEOADP%3E2.0.CO%3B2. 

Pielke R. A., L. R. Bernardet, P. J. Fitzpatrick, R. F. Hertenstein, A. S. Jones, X. Lin, J. E. Nachamkin, U. S. Nair, G. S. Poulos, M. H. Savoie and P. L. Vidale, 1995. Standardized test to evaluate numerical weather prediction algorithms. Bull. Amer . Meteor . Soc., 76, 46-48. https://journals.ametsoc.org/doi/abs/10.1175/1520-0477-76.1.46.