NASA Logo Jet Propulsion Laboratory California Institute of Technology View the NASA Portal
NASA Banner
NASA Banner
NASA Banner
NASA Banner
JPL Science
JPL Science Home
Planetary Science Planetary Science
Astrophysics & Space Sciences Astrophysics & Space Sciences
Earth Science Earth Science
Directorate Science Affiliates Directorate Science Affiliates
Atomic and Molecular Physics
Astronomical Instrumentation
Relativistic Astrophysics
Microwave Atmospheric Science
Tropospheric Sounding, Assimilation, and Modeling (TSAM)
AIRS Atmospheric Science
Orbiting Carbon Observatory Science
Aerosol and Cloud Science
Open Postdoc Positions Open Postdoc Positions
Brochures Brochures
Highlights Highlights
 Directorate Science Affiliates: People
Kay  Suselj's Picture
Jet Propulsion Laboratory
M/S 183-501
4800 Oak Grove Drive
Pasadena, CA 91109
Curriculum Vitae:

Kay Suselj

  • Ph.D. in Physics, Carl von Ossietzky University of Oldenburg, Germany (2009)
  • M.Sc. in Meteorology, University of Ljubljana, Slovenia (2006)
  • B.Sc. in Meteorology, University of Ljubljana, Slovenia (2001)

Research Interests
  • Physical parameterization in atmospheric models
  • Stochastic parameterizations and its impact on the evolution of model fields
  • Understanding of turbulence and convection in the geophysical fluids
  • Interaction between physical processes and dynamics in atmospheric models

Atmospheric models are based on solid physical principles. Nevertheless, for computers to solve them efficiently, for example to make a weather forecast or predict the future climate, certain simplifications are inevitable. These simplifications neglect smaller scale motions, which are instead represented by parameterizations. How accurate and complex do these parameterizations have to be? Can we develop better predictive models and theories for unresolved motions? How do unresolved motions interact with other physical processes, such as condensation and evaporation, atmospheric radiation and microphysics? These are some of the overarching questions that my research tries to address. For this, our team utilizes data from turbulence and cloud resolving models and observations. We are developing a parameterization, based on the Eddy-Diffusivity/Mass-Flux approach, which can be used in any climate or weather prediction model. I collaborate with weather prediction and climate centers to test the skill of our parameterization in their models.

Professional Experience
  • Data Scientist at NASA Jet Propulsion Laboratory/California Institute of Technology (2013-Present)
  • Assistant Researcher at University of California, Los Angeles (2012-2013)
  • Postdoctoral Scholar at NASA Jet Propulsion Laboratory/California Institute of Technology (2009-2012)
  • PhD Student at Carl von Ossietzky University of Oldenburg, Germany (2006-2009)
  • Researcher at the Environmental Agency of the Republic of Slovenia (2001-2006)
  • Intern at the German Weather Service (Deutscher Wetterdienst) in Braunschweig (1999)

  • Selected Awards
    • Discovery Award (2020)
    • Voyager Award (2019)
    • NASA Group Achievement Award (2017)
    • NASA Early Career Public Achievement Medal (2016)

    Selected Publications
    1. Wu, El., H. Yang, J. Kleissl, K. Sušelj, M. J Kurowski, and J. Teixeira, 2020. On the Parameterization of Convective Downdrafts for Marine Stratocumulus Clouds. Mon Weather Rev 148 (5), pp. 1931-1950.
    2. Sušelj, K., M. J. Kurowski, and J. Teixeira, 2019. On the Factors Controlling the Development of Shallow Convection in Eddy-Diffusivity/Mass-Flux Models. J Atmos Sci 76 (2), pp. 433-456.
    3. Kurowski, M. J., K. Sušelj, and W. W. Grabowski, 2019. Is Shallow Convection Sensitive to Environmental Heterogeneities? Geophy Res Lett 46, pp. 1-9.
    4. Sušelj, K., M. J. Kurowski, and J. Teixeira, 2019. A Unified Eddy-Diffusivity/Mass-Flux Approach for Modeling Atmospheric Convection. J Atmos Sci 76 (8) , pp. 2505-2537.
    5. Kurowski, M. J. , H. Th. Thrastarson, K. Sušelj, and J. Teixeira, 2019. Towards Unifying the Planetary Boundary Layer and Shallow Convection in CAM5 with the Eddy-Diffusivity/Mass-Flux Approach. Atmosphere 10 (9).
    6. Smalley, M., K. Sušelj, M. Lebsock, and J. Teixeira, 2019. A Novel Framework for Evaluating and Improving Parameterized Subtropical Marine Boundary Layer Cloudiness. Mon Weather Rev 147 (9), pp. 3241-3260.
    7. Olson, J. B., J. S. Kenyon, and W. A. Angevine J. M. Brown, M. Pagowski and K. Sušelj, 2019. A Description of the MYNN-EDMF Scheme and the Coupling to Other Components in WRF-ARW. NOAA Technical Memorandum OAR GSD-61.
    8. Bhattacharya, R., S. Bordoni, K. Sušelj, and J. Teixeira, Parameterization Interactions in Global Aquaplanet Simulations. J Adv Model Earth Syst 10(2).
    9. Pires, L. B. M., K. Sušelj, L. Rossato, and J. Teixeira, 2018. Analyses of Shallow Convection over the Amazon Coastal Region Using Satellite Images, Data Observations and Modeling. Revista Brasileira de Meteorologia 33 (2), pp. 366-379
    10. Angevine, W. M., J. Olson, J. Kenyon, W. I. Gustafson, S. Endo, K. Sušelj, et al., 2018. Shallow Cumulus in WRF Parameterizations Evaluated against LASSO Large-Eddy Simulations. Mon Weather Rev 146 (12), pp. 4303-4322.
    11. Kurowski, M. J., K. Sušelj, W. W. Grabowski, and J. Teixeira, 2018. Shallow-to-Deep Transition of Continental Moist Convection: Cold Pools, Surface Fluxes, and Mesoscale Organization. J Atmos Sci 75 (12), pp. 4071-4090.
    12. Cesana, G., K. Sušelj, and F. Brient, On the Dependence of Cloud Feedbacks on Physical Parameterizations in WRF Aquaplanet Simulations. Geophy Res Lett 44 (20) pp. 10,762-10,771.
    13. Schreier, M., and K. Sušelj, 2016. Analysis of collocated AIRS and MODIS data: a global investigation of correlations between clouds and atmosphere in 2004-2012. International Journ Remote Sensing 37 (11), pp. 2524-2540.
    14. Schreier, M. M., B. H. Kahn, K. Sušelj, J. Karlsson, S. C. Ou, Q. Yue and S. L. Nasiri, 2014. Atmospheric parameters in a subtropical cloud regime transition derived by AIRS and MODIS: observed statistical variability compared to ERA-Interim. Atmos Chem Phy 14 (7), pp. 3573-3587.
    15. Sušelj, K., T. F. Hogan, and J. Teixeira, 2014. Implementation of a Stochastic Eddy-Diffusivity/Mass-Flux Parameterization into the Navy Global Environmental Model. Weather Forecast 29 (6), pp. 1374-1390.
    16. Sušelj, K., J. Teixeira, and D. Chung, 2013. A Unified Model for Moist Convective Boundary Layers Based on a Stochastic Eddy-Diffusivity/Mass-Flux Parameterization. J Atmos Scie 70 (7), pp. 1929-1953.
    17. Yue, Q., B. H. Kahn, H. Xiao, Mathias M. Schreier, Eric J. Fetzer, J. Teixeira and K. Sušelj, 2014. Transitions of cloud-topped marine boundary layers characterized by AIRS, MODIS, and a large eddy simulation model. Journal of Geophy Res: Atmospheres 118 (15), pp. 8598-8611.
    18. Sušelj, K., J. Teixeira, and G.s Matheou, Eddy Diffusivity/Mass Flux and Shallow Cumulus Boundary Layer: An Updraft PDF Multiple Mass Flux Scheme. Journal Atmos Sci 69 (5) pp. 1513-1533.
    19. Semedo, A., K. Sušelj, A. Rutgersson, and A. Sterl,2010. A Global View on the Wind Sea and Swell Climate and Variability from ERA-40. J Climate 24 (5), pp. 1461-1479
    20. Sušelj, K., A. Sood, and D. Heinemann, 2010. North Sea near-surface wind climate and its relation to the large-scale circulation patterns. Theoret Appl Clim 99 (3-4), pp. 403-419.
    21. Sušel, K., and A. Sood, 2010. Improving the Mellor-Yamada-Janji? Parameterization for wind conditions in the marine planetary boundary layer. Boundary-Layer Meteorology 136, no. 2 pp. 301-324.
    22. Sušelj, K., M. N. Tsimplis, and K. Bergant, 2008. Is the Mediterranean Sea surface height variability predictable? Phys Chem Earth, Parts A/B/C 33, no. 3-4, pp. 225-238.
    23. Kobold, M., and K. Sušelj, 2005 Precipitation forecasts and their uncertainty as input into hydrological models. Hydrology and Earth Syst Sciences 9, no. 4 pp. 322-332.

    Section Home Page
    People in the Section Office

    JPL Privacy Statement Sitemap Contact Site Manager
    FIRST GOV NASA Home Page