Steven J. Greybush Publications

2020-2025 | 2014-2019 | 2007-2013
*Grad Student Advised by SJG, ^Student or Postdoc Mentored by SJG

2020-2025

  1. Yorks, J. E., M. A. Miller, T. J. Lang, J. A. Finlon, I. S. Adams, B. A. Colle, S. J. Greybush, A. J. Heymsfield, G. M. Heymsfield, R. Kroodsma, S. LeBlanc, M. W. McLinden, G. M. McFarquhar, R. M. Rauber, S. E. Yuter, T. Zaremba, and L. McMurdie, 2025: Sampling strategies to optimize coincident remote sensing and in situ cloud and precipitation observations from multiple aircraft. BAMS, 106, 12, E2544-E2562, doi:10.1175/BAMS-D-24-0280.1.
  2. Zhang, S.*, M. R. Kumjian, and S. J. Greybush, 2025. Polarimetric Radar-Based Investigation of Microphysics in Dendritic and Needle Aggregation Zones During NASA IMPACTS. Monthly Weather Review, 153, 10, 2007-2030, doi:10.1175/MWR-D-24-0232.1.
  3. Zhang, S.*, T. J. Zaremba, R. M. Rauber, M. M. Varcie, M. Kumjian, S. J. Greybush, G. M. McFarquhar, and L. A. McMurdie, 2025: Mesoscale and Microphysical Characteristics of Elevated Convection and Banded Precipitation over an Arctic Cold Front: results from IMPACTS. J. Atmos. Sci., 82, 1113-1135, doi:10.1175/JAS-D-24-0177.1.
  4. Naegele, S.*, J. M. Wilczak, S. J. Greybush, G. S. Young, M. Gervais, and J. A. Lee, 2025: Analyzing Self-Organizing Maps of Modeled U.S. Coastal Wind Regimes with a Comparison to Observations. Artificial Intelligence for the Earth Systems, 4, 2, doi:10.1175/AIES-D-24-0023.1.
  5. Ssentongo, P., C. Fronterre, J. E. Ericson, M. Wang, L. Al-Shaar, H. Greatrex, P. O. Omadi, J. Muvawala, S. J. Greybush, P. K. Mbabazi, L. Murray-Kolb, A. J. B. Muwanguzi, and S. J. Schiff, 2025: Preconception and Prenatal Environment and Growth Faltering Among Children in Uganda. JAMA Network Open, 8(3), e251122, doi:10.1001/jamanetworkopen.2025.1112.
  6. Berry, T., M. Ferrari, T. Sauer, S. J. Greybush, D. Ebeigbe, A. J. Whalen, and S. J. Schiff, 2025: Existance and Stability of Equilbria in Infectious Disease Dynamics with Behavioral Feedbacks. Physical Review E, 111, 014317, doi:10.1103/PhysRevE.111.014317.
  7. 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. Artificial Intelligence for the Earth Systems, 3, e230098, doi:10.1175/AIES-D-23-0098.1.
  8. 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.
  9. 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-22-0344.1.
  10. 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.
  11. Mykolajtchuk, P. D.^, K. C. Eure^, Y. Zhang, D. J. Stensrud, F. Zhang, S. J. Greybush, and M. R. Kumjian, 2023: Diagnosing a missed convective initiation forecast by assimilating GOES-16 satellite radiances and WSR-88D radar observations. Weather and Forecasting, 38(10), 1935-1951, doi:10.1175/WAF-D-23-0010.1.
  12. Eure, K. C.^, P. D. Mykolajtchuk^, Y. Zhang, D. J. Stensrud, F. Zhang, S. J. Greybush, and M. R. Kumjian, 2023: Simultaneous Assimilation of Radar and Satellite Observations to Improve Ensemble Forecasts of Severe Weather. Monthly Weather Review, 151(3), 795-813, doi:10.1175/MWR-D-22-0188.1.
  13. Wang, Y.^, M. Gueye^, S. J. Greybush, H. Greatrex, A. J. Whalen, P. Ssentongo, F. Zhang, G. Jenkins, and S. J. Schiff, 2023: Verification of Operational Numerical Weather Prediction Model Forecasts of Precipitation Using Satellite Rainfall Estimates over Africa. Meteorological Applications, 30(1), 22p, https://doi.org/10.1002/met.2112.
  14. 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.
  15. 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 Preciptation for Atlantic Coast-threatening Snowstorms (IMPACTS) Campaign. BAMS, 103(5), E1243-E1269, doi:10.1175/BAMS-D-20-0246.1.
  16. Mooring, T. A., G. E. Davis, and S. J. Greybush, 2022: Low-level jets and the convergence of Mars data assimilation algorithms. J. Geophys. Res. Planets, 127, 2, doi:10.1029/2021JE006968.
  17. 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.
  18. Zhang, Y., S. B. Sieron, Y. Lu, X. Chen, R. G. Nystrom*, M. Minamide, M.-Y. Chan, C. M. Hartman^, Z. Yao,J. H. Ruppert, Jr., A. Okazaki^, S. J. Greybush, E. E. Clothiaux, and F. Zhang, 2021: Ensemble-Based Assimilation of Satellite All-Sky Microwave Radiances Improves Intensity and Rainfall Predictions for Hurricane Harvey (2017). Geophys. Res. Lett., 48, 24, doi:10.1029/2021GL096410.
  19. Okazaki, A.^, T. Miyoshi, K. Yoshimura, S. J. Greybush, and F. Zhang, 2021: Revisiting online and offline data assimilation comparison for paleoclimate reconstruction: an idealized OSSE study. J. Geophys. Res. Atmos., 126, 16, doi:10.1029/2020JD034214.
  20. 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.
  21. Ssentongo, P., C. Fronterre, A. Geronimo, S. J. Greybush, P. M. Mbabazi, J. Muvawala, S. Nahalamba, P. O. Omadi, B. T. Opar, S. A. Sinnar, Y. Wang^, A. J. Whalen, L. Held, C. Jewell, A. J. B. Muwanguzi, H. Greatrex, M. N. Norton, P. Diggle, and S. J. Schiff, 2021: Tracking and Predicting the African COVID-19 Pandemic. Proceedings of the National Academies of Science, 118 (28) e2026664118, doi:10.1073/pnas.2026664118.
  22. Naegele, S. M.*, T. C. McCandless, S. J. Greybush, G. S. Young, S. E. Haupt, and M. Al-Rasheedi, 2020: Climatology of Wind Variability for the Shagaya Region in Kuwait. Renewable and Sustainable Energy Reviews, 133, 110089, doi:10.1016/j.rser.2020.110089.
  23. Hermoso, A.^, V. Homar, S. J. Greybush, and D. J. Stensrud, 2020: Tailored ensemble prediction systems: application of seamless scale bred vectors. J. Meteor. Soc. Japan, 98, doi:10.2151/jmsj.2020-053.
  24. 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.

    25. 2014-2019

  25. Eipper, D. T.^, S. J. Greybush, G. S. Young, S. Saslo*, T. D. Sikora, R. D. Clark, 2019: Lake-Effect Snowbands in Baroclinic Environments. Weather and Forecasting, 34, 1657-1674. https://doi.org/10.1175/WAF-D-18-0191.1.
  26. 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, https://doi.org/10.18113/D3W375.
  27. 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, https://doi.org/10.1016/j.icarus.2018.07.001.
  28. Eipper, D. T.^, G. S. Young, S. J. Greybush, S. Saslo*, T. D. Sikora, and R. D. Clark, 2018: Predicting the Inland Penetration of Long-Lake-Axis Parallel Snowbands. Weather and Forecasting, 33, 1435-1451. https://doi.org/10.1175/WAF-D-18-0033.1.
  29. Navarro, T.^, F. Forget, E. Millour, S. J. Greybush, E. Kalnay, and T. Miyoshi, 2017: The Challenge of Atmospheric Data Assimilation on Mars. Earth and Space Science, 4, 690-722. https://doi.org/10.1002/2017EA000274.
  30. Kotsuki, S., S. J. Greybush, and T. Miyoshi, 2017: Can We Optimize the Assimilation Order in the Serial Ensemble Kalman Filter? A Study with the Lorenz-96 Model. Monthly Weather Review, 145, 4977-4995. https://doi.org/10.1175/MWR-D-17-0094.1.
  31. Saslo, S.*, and S. J. Greybush, 2017: Prediction of Lake-Effect Snow Using Convection-Allowing Ensemble Forecasts and Regional Data Assimilation. Weather and Forecasting, 32, 1727-1744. https://doi.org/10.1175/WAF-D-16-0206.1.
  32. 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. Weather and Forecasting, 32, 1057-1078. https://doi.org/10.1175/WAF-D-16-0153.1.
  33. Yang, X.^, R. Siddique, S. Sharma^, S. J. Greybush, and A. Mejia, 2017:Postprocessing of GEFS Precipitation Ensemble Reforecasts over the U.S. Mid-Atlantic Region. Monthly Weather Review, 145, 1641-1658. https://doi.org/10.1175/MWR-D-16-0251.1.
  34. Waugh, D. W., A. Toigo, S. Guzewich, S. J. Greybush, R. J. Wilson, and L. Montabone, 2016: Martian Polar Vortices: Comparison of Reanalyses. JGR-Planets, 121, 9, 1770-1785, doi: 10.1002/2016JE005093.
  35. 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.
  36. Navarro, T.^, F. Forget, E. Millour, and S. J. Greybush, 2014: Detection of detached dust layers in the Martian atmosphere from their thermal signature using assimilation. Geophys. Res. Lett., 41, 19, 6620-6626, doi: 10.1002/2014GL061377.A.

    37. 2007-2013

  37. 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, 639653, doi: 10.1002/qj.1990.
  38. Lee, S.-J., J. Lee, S. J. Greybush, M. Kang, and J. Kim, 2013: Spatial and Temporal Variation in PBL Height over the Korean Peninsula in the KMA Operational Regional Model. Advances in Meteorology, 2013, 16 pp., doi: 10.1155/2013/381630.
  39. Greybush, S. J., E. Kalnay, K. Ide, T. Miyoshi, T. McConnochie, M. J. Hoffman, R. N. Hoffman, and R. J. Wilson, 2012: Ensemble Kalman Filter Data Assimilation of Thermal Emission Spectrometer (TES) Profiles into a Mars Global Circulation Model. J. Geophys. Res. Planets, 117, E11008, doi: 10.1029/2012JE004097.
  40. Greybush, S. J., E. Kalnay, T. Miyoshi, K. Ide, and B. Hunt, 2011: Balance and Ensemble Kalman Filter Localization Techniques. Mon. Wea. Rev., 139, 511522, doi: 10.1175/2010MWR3328.1.
  41. Lee, S.-J., D. F. Parrish, S.-Y. Park, W.-S. Wu, S. J. Greybush, W.-J. Lee, and S. J. Lord, 2011: Effects of 2-m air temperature assimilation and a new near-surface observation operator on the NCEP Gridpoint statistical-interpolation system. Asia-Pacific J. Atmos. Sci., 47, 4, 353376, doi: 10.1007/s13143-011-0022-y.
  42. Hoffman, M. J., S. J. Greybush, R. J. Wilson, G. Gyarmati, R. N. Hoffman, E. Kalnay, K. Ide, E. J. Kostelich, T. Miyoshi, and I. Szunyogh, 2010: An ensemble Kalman filter data assimilation system for the martian atmosphere: Implementation and simulation experiments. Icarus, 209, 470481, doi: 10.1016/j.icarus.2010.03.034.
  43. Greybush, S., S. E. Haupt, and G. S. Young, 2008: The Regime Dependence of Optimally Weighted Ensemble Model Consensus Forecasts, Weather and Forecasting, 23, 11461161, doi: 10.1175/2008WAF2007078.1.
  44. Root, B., P. Knight, G. Young, S. Greybush, R. Grumm, R. Holmes, and J. Ross, 2007: A Fingerprinting Technique for Major Weather Events. J. Appl. Meteor. Climatol., 46, 10531066, doi: 10.1175/JAM2509.1.

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