Kara Sulia
PhD, Meteorology, Penn State University, 2013
BS, Meteorology, Penn State University, 2009
Dr. Sulia's research has focused on understanding the growth mechanisms of frozen hydrometeors and their influence on cloud systems. In particular this includes the development of a habit-dependent ice microphysical parameterization. This work extends to include aggregation, and more recently, the impact of habit on electrification and lightning production. More recently, and as Director of the xCITE Laboratory at ASRC, Dr. Sulia's research focus has shifted to advanced computational methods including advanced data analytics, machine learning/AI, and software development. Much of this work melds observed or NWP data with non-meteorological data, such as camera images or utility outages, with aims to improve our understanding of how weather impacts sectors vulnerable to weather-related emergencies. In addition to the development of new, unique models, is the necessity to improve explainability of these methods and effectively communicating/translating their outputs.
As research associate/faculty at ASRC, Dr. Sulia is responsible for leading a number of research projects and advising graduate students through their degrees. This includes traditional academic responsibilities, such as publications and proposal submissions. As xCITE Lab Director, these responsibilities expand to management of the lab and lab staff, including extensive high-end GPU-based hardware and scientific visualization, and management of the many projects that require these resources as well as xCITE expertise (e.g., GPU computing, training, web app development, data management, etc.).
Wirz, C.D., Sutter, C., Demuth, J. L., Mayer, K. J., Chapman, W. E., Cains, M. G., Radford, J., Przybylo, V., Evans, A., Martin, T., Gaudet, L. C., Sulia, K., Bostrom, A., Gagne, D. J., Bassill, N., Schumacher, A., and Thorncroft, C, 2024. Increasing the reproducibility, replicability, and evaluability of supervised AI/ML in the earth systems science by leveraging social science methods. Earth and Space Science, 11 (7), 10.1029/2023EA003364.
Gaudet, L, K. J. Sulia, Ryan D. Torn, and Nick P. Bassill, 2024: Verification of the Global Forecast System, North American Mesoscale Forecast System, and High-Resolution Rapid Refresh Model Near-Surface Forecasts by use of the New York State Mesonet. Weather and Forecasting, 39 (2), 10.1175/WAF-D-23-0094.1.
Przybylo, V, K. J. Sulia, Z. Lebo, and C G. Schmitt, 2022: The Ice Particle and Aggregate Simulator (IPAS). Part III: Verification and Analysis of Ice-Aggregate and Aggregate-Aggregate Collection for Microphysical Parameterization. J. Atmos. Sci., 79 (6), 1651-1667, 10.1175/JAS-D-21-0180.1.
Przybylo, V, K. J. Sulia, Z. Lebo, and C G. Schmitt, 2022: The Ice Particle and Aggregate Simulator (IPAS). Part II: Analysis of a Database of Theoretical Aggregates for Microphysical Parameterization. J. Atmos. Sci, 79 (6), 1633-1649, 10.1175/JAS-D-21-0179.1.
Przybylo, V, K. J. Sulia, C G. Schmitt, and Z. Lebo, 2022: Classification of Cloud Particle Imagery from Aircraft Platforms Using Convolutional Neural Networks. J. Atmos. Oceanic Tech., 39, 405-424, 10.1175/JTECH-D-21-0094.1.
Gaudet, L, K. J. Sulia, T.-C. Tsai, J.-P. Chen, J. P. Blair, 2021: Assessment of a Microphysical Ensemble Used to Investigate the OWLeS IOP4 Lake-Effect Storm. J. Atmos. Sci., 78 (5), 1607-1628, 10.1175/JAS-D-20-0045.1.
Sulia, K. J., Z. J. Lebo, V. Przybylo, and C. G. Schmitt, 2021: A new method for ice-ice aggregation in the Adaptive Habit Model. J. Atmos. Sci., 78, 133-154, 10.1175/JAS-D-20-0020.1.
Schmitt, C. G., K. J. Sulia, Z. J. Lebo, A. J. Heymsfield, *V. Przybylo, P. Connolly, 2019: The variability of the terminal velocity of similarly sized ice particles. J. Appli. Met. Climatol., 58, 1751-1761, 10.1175/JAMC-D-18-0291.1.
Gaudet, L, K. J. Sulia, F. Yu, and G. Luo, 2019: Sensitivity of Lake-Effect Cloud Microphysical Processes to Ice Crystal Habit and Nucleation during OWLeS IOP4. J. Atmos. Sci., 76, 3411-3434, 10.1175/JAS-D-19-0004.1
Przybylo, V, K. J. Sulia, C G. Schmitt, and Z. Lebo, 2019: The Ice Particle and Aggregate Simulator (IPAS). Part I: Extracting dimensional properties of ice-ice aggregates for microphysical parameterization. J. Atmos. Sci., 76, 1661-1676, 10.1175/JAS-D-18-0187.1
Sulia, K. J. and M. R. Kumjian, 2017: Simulated Polarimetric Fields of Ice Vapor Growth Using the Adaptive Habit Model. Part I: Large-Eddy Simulations. Mon. Wea. Rev., 145, 2281-2302, 10.1175/MWR-D-16-0061.1.
Sulia, K. J. and M. R. Kumjian, 2017: Simulated Polarimetric Fields of Ice Vapor Growth Using the Adaptive Habit Model. Part II: A Case Study from the FROST Experiment. Mon. Wea. Rev., 145, 2303-2323, 10.1175/MWR-D-16-0062.1.
Sulia, K., J.Y. Harrington, and H. Morrison, 2014: Dynamical and microphysical evolution during mixed-phase cloud glaciation simulated using the bulk adaptive habit prediction model. Journal of the Atmospheric Sciences, early online release, 10.1175/JAS-D-14-0070.1.
Sulia, K., J. Y. Harrington, and H. Morrison, 2013: A method for adaptive habit prediction in bulk microphysical models: Part III: Applications and studies within a two-dimensional kinematic model. Journal of the Atmospheric Sciences, 70 (10), 3302-3320, 10.1175/JAS-D-12-0316.1.
Harrington, J. Y., K. Sulia, and H. Morrison, 2013: A method for adaptive habit prediction in bulk microphysical models: Part I: Theoretical Development. Journal of the Atmospheric Sciences, 70 (2), 349-364, 10.1175/JAS-D-12-0040.1.
Sulia, K. and J. Y. Harrington, 2011: Ice Aspect Ratio Influences on Mixed-Phase Clouds. Impacts of Phase Partitioning in Parcel Models. Journal of Geophysical Research, 116, D21309, 10.1029/2011JD016298.