Author Bibliographies
Publications by Marcus van Lier-Walqui
This citation list includes papers published while the author has been on staff at the NASA Goddard Institute for Space Studies. It may include some publications based on research conducted prior to their having joined the institute.
Submitted / In Review
Using machine learning to generate a GISS ModelE calibrated physics ensemble (CPE). J. Adv. Model. Earth Syst., submitted.
, , Q. Yang, , , , , , , A. Behrangi, S.J. Camargo, , , , and J.D.O. Strong, 2024:Yang, Q., A simple emulator that enables interpretation of parameter-output relationships, applied to two climate model PPEs. J. Adv. Model. Earth Syst., submitted.
, , T. Eidhammer, and L. Hawkins, 2024:2024
Bruning, E.C., K.N. Brunner, Lightning and radar measures of mixed phase updraft variability in tracked storms during the TRACER field campaign in Houston, Texas. Mon. Weather Rev., 152, no. 12, 2753-2769, doi:10.1175/MWR-D-24-0060.1.
, T. Logan, and T. Matsui, 2024:Eidhammer, T., A. Gettelman, K. Thayer-Calder, D. Watson-Parris, An extensible perturbed parameter ensemble (PPE) for the Community Atmosphere Model Version 6. Geosci. Model Dev., 17, no. 21, 7835-7853, doi:10.5194/gmd-17-7835-2024.
, H. Morrison, , C. Song, and D. McCoy, 2024:Estimating the impact of a 2017 smoke plume on surface climate over northern Canada with a climate model, satellite retrievals, and weather forecasts. J. Geophys. Res. Atmos., 129, no. 15, e2023JD039396, doi:10.1029/2023JD039396.
, M. Luo, , , , , , , and , 2024:Lamb, K., Reduced order modeling for linearized representations of microphysical process rates. J. Adv. Model. Earth Syst., 16, no. 7, e2023MS003918, doi:10.1029/2023MS003918.
, S. Santos, and H. Morrison, 2024:2023
TRACER Lightning Mapping Array Field Campaign Report. DOE/SC-ARM-23-026. U.S. Department of Energy, Office of Science, doi:10.2172/1975120.
, E. Bruning, K. Brunner, T. Matsui, and T. Iguchi, 2023:2022
Igel, A.L., H. Morrison, S.P. Santos, and Limitations of separate cloud and rain categories in parameterizing collision-coalescence for bulk microphysics schemes. J. Adv. Model. Earth Syst., 14, no. 6, e2022MS003039, doi:10.1029/2022MS003039.
, 2022:Kumjian, M.R., O.P. Prat, K.J. Reimel, Dual-polarization radar fingerprints of precipitation physics: A review. MDPI Remote Sens., 14, no. 15, 3706, doi:10.3390/rs14153706.
, and H.C. Morrison, 2022:2021
Schrom, R.S., Radar-based Bayesian estimation of ice crystal growth parameters within a microphysical model. J. Atmos. Sci., 78, no. 2, 549-569, doi:10.1175/JAS-D-20-0134.1.
, M.R. Kumjian, J.Y. Harrington, A.A. Jensen, and Y.-S. Chen, 2021:2020
Morrison, H.C., A Bayesian approach for statistical-physical bulk parameterization of rain microphysics, Part I: Scheme description. J. Atmos. Sci., 77, no. 3, 1019-1041, doi:10.1175/JAS-D-19-0070.1.
, M.R. Kumjian, and O.P. Prat, 2020:Morrison, H., Confronting the challenge of modeling cloud and precipitation microphysics. J. Adv. Model. Earth Syst., 12, no. 8, e2019MS001689, doi:10.1029/2019MS001689.
, , W.W. Grabowski, J.Y. Harrington, C. Hoose, A. Korolev, M.R. Kumjian, J.A. Milbrandt, H. Pawlowska, D.J. Posselt, O.P. Prat, K.J. Reimel, S.-I. Shima, , and L. Xue, 2020:A Bayesian approach for statistical-physical bulk parameterization of rain microphysics, Part II: Idealized Markov chain Monte Carlo experiments. J. Atmos. Sci., 77, no. 3, 1043-1064, doi:10.1175/JAS-D-19-0071.1.
, H.C. Morrison, M.R. Kumjian, K.J. Karly J. Reimel, O.P. Prat, S. Lunderman, and M. Morzfeld, 2020:2019
Use of polarimetric radar measurements to constrain simulated convective cell evolution: A pilot study with Lagrangian tracking. Atmos. Meas. Tech., 12, 2979-3000, doi:10.5194/amt-12-2979-2019.
, , S. Collis, S.E. Giangrande, R.C. Jackson, X. Li, T. Matsui, R. Orville, M.H. Picel, D. Rosenfeld, A. Ryzhkov, R. Weitz, and P. Zhang, 2019:Kumjian, M.R., O.P. Prat, S. Collis, A moment-based polarimetric radar forward operator for rain microphysics. J. Appl. Meteorol. Climatol., 58, no. 1, 113-130, doi:10.1175/JAMC-D-18-0121.1.
, and H.C. Morrison, 2019:Morrison, H.C., M.R. Kumjian, C.P. Martinkus, O.P. Prat, and A general N-moment normalization method for deriving rain drop size distribution scaling relationships. J. Appl. Meteorol. Climatol., 58, no. 2, 247-267, doi:10.1175/JAMC-D-18-0060.1.
, 2019:2017
An improved convective ice parameterization for the NASA GISS Global Climate Model and impacts on cloud ice simulation. J. Climate, 30, no. 1, 317-336, doi:10.1175/JCLI-D-16-0346.1.
, , J. Jiang, and , 2017:Derivation of aerosol profiles for MC3E convection studies and use in simulations of the 20 May squall line case. Atmos. Chem. Phys., 17, 5947-5972, doi:10.5194/acp-17-5947-2017.
, X. Li, D. Wu, , , W.-K. Tao, G.M. McFarquhar, W. Wu, X. Dong, J. Wang, A. Ryzhkov, P. Zhang, M.R. Poellot, A. Neumann, and J.M. Tomlinson, 2017:2016
Polarimetric radar signatures of deep convection: Columns of specific differential phase observed during MC3E. Mon. Weather Rev., 144, no. 2, 737-758, doi:10.1175/MWR-D-15-0100.1.
, , , S. Collis, J.J. Helmus, D.R. MacGorman, K. North, P. Kollias, and D.J. Posselt, 2016:2014
Linearization of microphysical parameterization uncertainty using multiplicative process perturbation parameters. Mon. Weather Rev., 142, no. 1, 401-413, doi:10.1175/MWR-D-13-00076.1.
, T. Vukicevic, and D.J. Posselt, 2014:2012
Van Lier-Walqui, M., T. Vukicevic, and D.J. Posselt, 2012: Quantification of cloud microphysical parameterization uncertainty using radar reflectivity. Mon. Weather Rev., 140, 3442-3466, doi:10.1175/MWR-D-11-00216.1.