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)