Steven J. Greybush

Steven J. Greybush

  • Associate Professor of Meteorology
  • Associate Director, Center for Advanced Data Assimilation and Predictability Techniques
  • Co-Hire, Institute for Computational and Data Sciences
618 Walker Building
University Park, PA 16802
Email: sjg213@psu.edu
Phone: (814) 867-4926

Education:

  1. Ph.D., Atmospheric and Oceanic Science, The University of Maryland
  2. M.S., Atmospheric and Oceanic Science, The University of Maryland
  3. B.S., Computer Science, The Pennsylvania State University
  4. B.S., Meteorology, The Pennsylvania State University

Research Specialties:

Numerical Weather Prediction:

Biography:

Research Interests:

  • Data Assimilation
  • Numerical Weather Prediction
  • Weather and Climate of Mars
  • Atmospheric Modeling
  • Ensemble Forecasting
  • Predictability
  • Statistical and Artificial Intelligence Applications

Center for Advanced Data Assimilation and Predictability Techniques (ADAPT)

Selected Publications:

Fan, D., S. J. Greybush, D. J. Gagne, and E. E. Clothiaux, 2024: Physically Explainable Deep Learning for Convection Initiation Nowcasting Using GOES-16 Satellite Observations. Accepted to Artificial Intelligence for the Earth Systems.

Gillespie, H. E., D. J. McCleese, A. Kleinboehl, D. M. Kass, S. J. Greybush, and R. J. Wilson, 2024: Water Transport in the Mars Polar Atmosphere: Observations and Simulations. JGR Planets, 129, 5, doi:10.1029/2023JE008273. (link)

Greybush, S. J., T. D. Sikora, G. S. Young, Q. Mulhern^, R. D. Clark, and M. Jurewicz, 2024: Elevated Mixed Layers during Great Lake Lake-effect Events: An Investigation and Case Study from OWLeS. Monthly Weather Review, 152(1), 79-95, doi:10.1175/MWR-D-22-0344. (link)

Naegele, S. M., J. A. Lee, S. J. Greybush, S. E. Haupt, and G. S. Young, 2024: Identifying Wind Regimes Near Kuwait Using Self-Organizing Maps. Journal of Renewable and Sustainable Energy, 17, 02651, doi:10.1063/5.0152718. (link)

McMurdie, L. A., G. M. Heymsfield, J. E. Yorks, S. A. Braun, G. Skofronick-Jackson, R. Rauber, S. Yuter, B. Colle, G. M. McFarquahr, M. Poellot, D. R. Novak, T. J. Lang, R. Kroodsma, M. McLinden, M. Oue, P. Kollias, M. R. Kumjian, S. J. Greybush, A. J. Heymsfield, J. A. Finlon, V. McDonald, S. Nicholls, 2022: Chasing Snowstorms: The Investigation of Microphysics and Precipitation for Atlantic Coast-threatening Snowstorms (IMPACTS) Campaign.  BAMS, 103(5), E1243-E1269, doi:10.1175/BAMS-D-20-0246.1. (link)

Fan, D., S. J. Greybush, X. Chen, Y. Lu, G. S. Young, and F. Zhang, 2022: Exploring the Role of Deep Moist Convection in the Wavenumber Spectra of Atmospheric Kinetic Energy and Brightness Temperature.  Journal of the Atmospheric Sciences, 79(10), 2721-2737, doi:10.1175/JAS-D-21-0285.1. (link)

Seibert, J. J., S. J. Greybush, J. Li, Z. Zhang, and F. Zhang, 2022: Applications of the Geometry-Sensitive Ensemble Mean for Lake-Effect Snowbands and Other Weather Phenomena.  Mon. Wea. Rev., 150, 2, 409-429, doi:10.1175/MWR-D-21-0212.1. (link)

Nystrom, R. G., S. J. Greybush, X. Chen, and F. Zhang, 2021: Potential for new constraints on tropical cyclone surface exchange coefficients through simultaneous ensemble-based state and parameter estimation.  Mon. Wea. Rev., 149, 2213-2230, doi:10.1175/MWR-D-20-0259.1. (link)

Gillespie, H. E., S. J. Greybush, and R. J. Wilson, 2020: An investigation of the encirclement of Mars by dust in the 2018 global dust storm using the Ensemble Mars Atmosphere Reanalysis System (EMARS).  J. Geophys. Res. Planets, 125, e2019JE006106, doi:10.1029/2019JE006106. (link)

Greybush, S. J., E. Kalnay, R. J. Wilson, R. N. Hoffman, T. Nehrkorn, M. Leidner, J. Eluszkiewicz, H. E. Gillespie, M. Wespetal, Y. Zhao, M. Hoffman, P. Dudas, T. McConnochie, A. Kleinboehl, D. Kass, D. McCleese, and T. Miyoshi, 2019: The Ensemble Mars Atmosphere Reanalysis System (EMARS) Version 1.0. Geoscience Data Journal, 6, 2, 137-150, doi:10.1002/gdj3.77. (link)

Greybush, S. J., H. E. Gillespie, and R. J. Wilson, 2019: Transient Eddies in the TES/MCS Ensemble Mars Atmosphere Reanalysis System (EMARS). Icarus, 317, 158-181, doi:10.1016/j.icarus.2018.07.001. (link)

Saslo, S., and S. J. Greybush, 2017: Prediction of Lake-Effect Snow using Convection-Allowing Ensemble Forecasts and Regional Data Assimilation. Wea. Forecasting, 32, 1727-1744, doi:10.1175/WAF-D-16-0206.1. (link)

Greybush, S. J., S. Saslo, and R. Grumm, 2017: Assessing the Ensemble Predictability of Precipitation Forecasts for the January 2015 and 2016 East Coast Winter storms. Wea. Forecasting, 32, 1057-1078, doi:10.1175/WAF-D-16-0153.1. (link)

Zhao, Y., S. J. Greybush, R. J. Wilson, R. N. Hoffman, and E. Kalnay, 2015: Impact of assimilation window length on diurnal features in a Mars atmospheric analysis. Tellus A, 67, 26042, doi: 10.3402/tellusa.v67.20642. (link)

Greybush, S. J., E. Kalnay, M. J. Hoffman, and R. J. Wilson, 2013: Identifying Martian atmospheric instabilities and their physical origins using bred vectors. Q. J. R. Meteorol. Soc., 139, 639-653, doi: 10.1002/qj.1990. (link)

Greybush, S. J., R. J. Wilson, R. N. Hoffman, M. J. Hoffman, T. Miyoshi, K. Ide, T. McConnochie, and E. Kalnay, 2012: Ensemble Kalman Filter Data Assimilation of Thermal Emission Spectrometer Temperature Retrievals into a Mars GCM. J. Geophys. Res., 117, E11008, doi: 10.1029/2012JE004097. (link)

Greybush, S. J., E. Kalnay, T. Miyoshi, K. Ide, and B. Hunt, 2011: Balance and Ensemble Kalman Filter Localization Techniques. Mon. Wea. Rev., 139, 511-522. (link)

Greybush, S. J., S. E. Haupt, and G. S. Young, 2008: The Regime Dependence of Optimally Weighted Ensemble Model Consensus Forecasts of Surface Temperature. Wea. Forecasting, 23, 1146-1161. (link)