Research Scientist and Atmospheric Modeler

Atmospheric, Earth, and Energy Division

Turbulence Analysis & Simulation Center (TASC)

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Research Interests

Katie Lundquist is a researcher in the Atmospheric, Earth, and Energy Division and leads the atmospheric modeling team at the National Atmospheric Release Advisory Center (NARAC) at LLNL.

Katie’s research interests are broad within the fields of atmospheric modeling and scientific computing. Her specific interests include mesoscale-to-microscale atmospheric simulation, urban modeling, and transport and dispersion within the atmosphere. In her role at NARAC, she leads a team of atmospheric modelers tasked with developing physics-based models and tools for predicting the consequences of atmospheric releases of hazardous materials.

Katie is a member of the American Physical Society (APS) and of the American Meteorological Society (AMS). She earned her Ph.D. and M.S. in mechanical engineering from the University of California, Berkeley and her B.S. in mechanical engineering at The University of Texas at Austin. 

Ph. D., Mechanical Engineering, University of California, Berkeley, 2010

M.S., Mechanical Engineering, University of California, Berkeley, 2006

B.S., Mechanical Engineering, The University of Texas at Austin, 2000

Lundquist, K. A., R. S. Arthur, S. Neuscamman, J. P. Morris, C. R. Scullard, A. W. Cook, N. G. Wimer, P. Goldstein, G. D. Spriggs, L. G. Glascoe, and J. S. Nasstrom, 2023.  Examining the effects of soil entrainment during nuclear cloud rise on fallout predictions using a multiscale atmospheric modeling framework. Journal of Environmental Radioactivity, 270, 107299.https://doi.org/10.1016/j.jenvrad.2023.107299.

Wiersema, D.J., S. Wharton, R. S. Arthur, T. W. Juliano, K. A. Lundquist, L. G. Glascoe, R. K. Newsom, W. W. Schalk, M. J. Brown, and D. Dexheimer, 2023. V Assessing turbulence and mixing parameterizations in the gray-zone of multiscale simulations over mountainous terrain during the METEX21 field experiment. Frontiers in Earth Science, 11, 1251180. https://www.frontiersin.org/articles/10.3389/feart.2023.1251180

Lee, H.-H., K. A. Lundquist, and Q. Tang, 2023. Pyrocumulonimbus Events over British Columbia in 2017: An Ensemble Model Study of Parameter Sensitivities and Climate Impacts of Wildfire Smoke in the Stratosphere. Journal of Geophysical Research, Atmospheres, 128,e2022JD037648. https://doi.org/10.1029/2022JD037648

Wiersema, D. J., K. A. Lundquist, J. D. Mirocha, and F. K. Chow, 2022. Evaluation of Turbulence and Dispersion in Multiscale Atmospheric Simulations Over Complex Urban Terrain During the Joint Urban 2003 Field Campaign. Monthly Weather Review, 150, 3195-3209. https://doi.org/10.1175/MWR-D-22-0056.1

Arthur, R. S., K. A. Lundquist, J. D. Mirocha, S. Neuscamman, Y. Kanarksa, and J. S. Nasstrom, 2021. Simulating nuclear cloud rise within a realistic atmosphere using the Weather Research and Forecasting model. Atmospheric Environment, 254, 118363. https://doi.org/10.1016/j.atmosenv.2021.118363

Arthur, R. S., K. A. Lundquist, and J. B. Olson, 2021. Improved prediction of cold-air pools in the Weather Research and Forecasting model using a truly horizontal diffusion scheme for potential temperature. Boundary-Layer Meteorology, 149, 155-171. https://doi.org/10.1175/MWR-D-20-0234.1

Wagman, B. M., K. A. Lundquist, Q. Tang, L. G. Glascoe, and D. C. Bader 2020. Examining the Climate Effects of a Regional Nuclear Weapons Exchange Using a Multiscale Atmospheric Modeling Approach. Journal of Geophysical Research: Atmospheres, 125, e2020JD033056. https://doi.org/10.1029/2020JD033056

Kanarska, Y., T. Dunn, L. Glascoe, K. Lundquist, and C. Noble, 2020. Semi-implicit method to solve compressible multiphase fluid flows without acoustic time step restrictions. Computers & Fluids, 210, 104651. https://doi.org/10.1016/j.compfluid.2020.104651

Arthur, R. S., K. A. Lundquist, D. J. Wiersema, J. Bao, and F. K. Chow, 2020. Evaluating implementations of the immersed boundary method in the Weather Research and Forecasting model.  Monthly Weather Review, 148, 2087-2109. https://doi.org/10.1175/MWR-D-19-0219.1

Wiersema, D. J., K. A. Lundquist, and F. K. Chow, 2020. Mesoscale to microscale simulations over complex terrain with the immersed boundary method in the Weather Research and Forecasting model. Monthly Weather Review, 148, 577-595. https://doi.org/10.1175/MWR-D-19-0071.1

Olson, J. B. and Coauthors, 2019. Improving wind energy forecasting through numerical weather prediction model development.  Bulletin of the American Meteorological Society, 100, 2201-2220. https://doi.org/10.1175/BAMS-D-18-0040.1

Jensen, D. D, D. D. Lucas, K. A. Lundquist, and L. G. Glascoe, 2019. Sensitivity of a Bayesian source-term estimation model to spatiotemporal sensor resolution. Atmospheric Environment: X, 3, 100045. https://doi.org/10.1016/j.aeaoa.2019.100045

Chow, F. K., C. S. Schär, N. Ban, K. A. Lundquist, L. Schlemmer, and X. Shi, 2019. Crossing multiple gray zones in the transition from mesoscale to microscale simulation over complex terrain.  Atmosphere, 10(5), 274. https://doi.org/10.3390/atmos10050274

Arthur, R. S., J. D. Mirocha, K. A. Lundquist, and R. L. Street, 2019. Using a canopy model framework to improve large-eddy simulations of the neutral atmospheric boundary layer in the Weather Research and Forecasting model.  Monthly Weather Review, 147, 31-52. https://doi.org/10.1175/MWR-D-18-0204.1

Arthur, R. S., K. A. Lundquist, J. D. Mirocha, and F. K. Chow, 2018. Topographic effects on radiation in the WRF Model with the immersed boundary method: implementation, validation, and application to complex terrain. Monthly Weather Review, 146, 3277-3292. https://doi.org/10.1175/MWR-D-18-0108.1

Bao, J., F. K. Chow, and K. A. Lundquist, 2018. Large-eddy simulation over complex terrain using an improved immersed boundary method in the Weather Research and Forecasting model. Monthly Weather Review, 146, 2781-2797. https://doi.org/10.1175/MWR-D-18-0067.1

Mirocha, J. D., and K. A. Lundquist, 2017. Assessment of vertical mesh refinement in concurrently nested large-eddy simulations using the Weather Research and Forecasting model. Monthly Weather Review, 145, 3025-3048. https://doi.org/10.1175/MWR-D-16-0347.1

Daniels, M. D., K. A. Lundquist, J. D. Mirocha, D. J. Wiersema, and F .K. Chow, 2016. A new vertical grid nesting capability in the Weather Research and Forecasting (WRF) model. Monthly Weather Review, 144(10), 3725- 3747. https://doi.org/10.1175/MWR-D-16-0049.1

Lundquist, K. A. and F. K. Chow, 2013. Flow over complex terrain, numerical modeling of in Encyclopedia of Environmetrics Second Edition, A.-H. El-Shaarawi, W.W. Piegorsch, and M.A. Jenkins (eds).  John Wiley & Sons Ltd, Chichester, UK, pp. 1054-1063. https://doi.org/10.1002/9780470057339.vnn120

Lundquist, K. A., Chow, F. K., and J. K. Lundquist. 2012. An immersed boundary method enabling large-eddy simulations of flow over complex terrain in the WRF model. Monthly Weather Review, 140(12), 3936-3955. https://doi.org/10.1175/MWR-D-11-00311.1

Schmidli, J., Billings, B. J., Chow, F. K., De Wekker, S. F. J., Doyle, J. D., Grubisic, V., Holt, T. R., Jiang, Q., Lundquist, K. A., Sheridan, P., Vosper, S., Whiteman, C. D., Wyszogrodzki, A.A., and G. Zaengl, 2011, Intercomparison of mesoscale model simulations of the daytime valley wind system. Monthly Weather Review, 139(5), 1389-1409. https://doi.org/10.1175/2010MWR3523.1

Lundquist, K. A., Chow, F. K., and J. K. Lundquist, 2010. An immersed boundary method for the Weather Research and Forecasting model. Monthly Weather Review 138(3), 796-817. https://doi.org/10.1175/2009MWR2990.1

Lundquist, K. A. 2010. Immersed Boundary Methods for High-Resolution Simulation of Atmospheric Boundary-Layer Flow Over Complex Terrain, Ph. D. Thesis, University of California, Berkeley, Department of Mechanical Engineering, 179 pages. (also referenced as LLNL Technical Report LLNL-TH-431627) https://www.osti.gov/servlets/purl/1097228

Lundquist, K. A. 2006. Implementation of the Immersed Boundary Method in the Weather Research and Forecasting Model, Master’s Thesis, University of California, Berkeley, Department of Mechanical Engineering, 59 pages. (also referenced as LLNL Technical Report UCRL-TH-226657) https://www.osti.gov/servlets/purl/900883