Randy Chase



My name is Randy Chase, a Research Scientist working at CSU/CIRA. I grew up in Buffalo, New York where lake effect snow likely inspired my love of the weather. For my undergraduate degree I attended SUNY Brockport and had my first research experience at an REU hosted by Penn State where I worked with Dr. Jose Fuentes on ozone transport caused by convection in the Amazon Rainforest. After undergrad, I went to the University of Illinois at Urbana-Champaign where I got my M.S. and Ph.D. in Atmospheric Sciences working with Dr. Stephen Nesbitt and Dr. Greg McFarquhar. My graduate research focused on radar remote sensing of snow, where I investigated why the Global Precipitation Measurement (GPM) mission's snowfall retrievals were deficient, provided an alternative retrieval method and evaluated the new retrieval against CloudSat. After my Ph.D. I did a two year postdoc with Dr. Amy McGovern at the University of Oklahoma as part of the NSF AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography (AI2ES). During my postdoc I was first tasked to write two plain language machine learning tutorials for meteorologists. Beyond the tutorials, I also investigated if machine learning could help us retrieve storm updrafts from ground based radar data alone. My role in the van den Heever group, and more broadly at CSU/CIRA, is to assist in two major projects: INvestigation of Convective UpdraftS (INCUS) and Satellite-based 3D global cloud field analysis (OVERCAST). I will leverage my expertise in radar remote sensing and machine learning to assist the projects in their respective goals.


PhD, Atmospheric Sciences, University of Illinois at Urbana-Champaign, 2021
MS, Atmospheric Sciences, University of Illinois at Urbana-Champaign, 2018
BS, Dual-major in Meteorology and Water Resources, State University of New York, Brockport 2016


NASA Earth and Space Science Graduate Fellowship, 2017-2020
AGU Technical Committee Student Award 2020
The Father James B. Macelwane Annual Award in Meteorology (AMS), 2016
The Chancellor’s Award of Student Excellence (SUNY), 2016
The School of Science and Mathematics Undergraduate Award (SUNY Brockport), 2016
The David S. Johnson Endowed Undergraduate Scholarship (AMS), 2015
Leader of the Year, United States Tennis Association, 2015


11. Finlon, J. A., McMurdie, L. and Chase, R. J. 2022: Investigation of microphysicalproperties within regions of enhanced dual-Frequency ratio during the IMPACTS field campaign. Journal of Atmospheric Science, 79, 2773-2795. https://doi.org/10.1175/JAS-D-21-0311.1
10. Chase, R. J., Nesbitt, S. W., McFarquhar, G. M., Wood, N. B. and Heymsfield, G. M. 2022: Direct comparisons between GPM-DPR and CloudSat snowfall retrievals. JAMC, 61, 1257-1271. https://doi.org/10.1175/JAMC-D-21-0081.11
9. Chase, R. J., Harrison, D. R., Burke, A., Lackmann G. and McGovern A. 2022: A Machine Learning Tutorial for Operational Meteorology, Part I: Traditional Machine Learning. WAF, 37, 1509-1529. https://doi.org/10.1175/WAF-D-22-0070.11
8. Chase, R. J., Nesbitt, S. W. and McFarquhar, G. M. 2021: A dual-frequency radar retrieval of two parameters of the snowfall particle size distribution using a neural network. JAMC, 60, 341 – 359. https://doi.org/10.1175/JAMC-D-20-0177.11
7. Turk, F. J., Ringerud, S. E., Camplani, A., Casella, D., Chase, R. J., Ebtehaj, A. , . . . Wood, N. 2021: Applications of a CloudSat-TRMM and CloudSat-GPM Satellite coincidence dataset. MDPI Remote Sensing, 13, 2264. https://doi.org/10.3390/rs131222641
6. Chase, R. J., Nesbitt, S. W. and McFarquhar, G. M. 2020: Evaluation of the microphysical assumptions within GPM-DPR using ground-based observations of rain and snow. MDPI Atmosphere, 11, 619. https://doi.org/10.3390/atmos110606191
5. Ding, S., McFarquhar, G. M., Nesbitt, S. W., Chase, R. J., Poellot, M. R. and Wang, H. 2020: Dependence of mass-dimensional relationships on median mass diameter. MDPI Atmosphere, 11, 756. https://doi.org/10.3390/atmos110707561
4. Tridon, F., Battaglia, A., Chase, R. J., Turk, J., Leinonen, J., Kneifel, S., Mroz, K., Finlon, J. A., Bansemer, A., Tanelli, S., Heymsfield, A., and Nesbitt, S. W. 2019: The microphysics of stratiform precipitation during OLYMPEX: compatibility between 3-frequency radar and airborne in situ observations. Journal of Geophysical Research Atmospheres, 124, 8764–8792. https://doi.org/10.1029/2018JD0298581
3. Chase, R. J., Finlon, J. A., Borque, P., McFarquhar, G. M., Nesbitt, S. W., Tanelli, S., Sy, O. O., Durden, S. L. and Poellot, M. 2018: Evaluation of triple-frequency radar retrieval of snowfall properties using conincident airborne in-situ observations during OLYMPEX. Geophys. Res. Lett., 45, 5752 – 5760. https://doi.org/10.1029/2018GL0779971
2. Leinonen, J., Lebsock, M. D., Tanelli, S., Sy, O. O., Dolan, B., Chase, R. J., Finlon, J. A., von Lerber A. and Moisseev, D. 2018: Retrieval of snowflake microphysical properties from multi-frequency radar observations. Atmospheric Measurement Techniques, 11, 5471 – 5488. https://doi.org/10.5194/amt-11-5471-20181
1. Gerken, T., Wei, D., Chase, R. J., Fuentes, J. D., Schumacher, C., Machado, L. A., . . . Trowbridge, A. M., 2016: Downward transport of ozone rich air and implications for atmospheric chemistry in the Amazon rainforest. Atmospheric Environment, 124, 64-76. https://doi.org/10.1016/j.atmosenv.2015.11.0141

Submitted or in prep

McGovern, A., Gagne II, D. J., Wirz, C. D., Ebert-Uphoff, I., Bostrom, A., … Chase, R. J., … Peterson, T. 2022: Trustworthy artificial intelligences for enironmental sciences: Summer School Meeting Report. BAMS inbox, in review.
McGovern, A., Chase, R. J., Flora, M., Gagne II, D. J., Lagerquist, R., Potvin, C. K., Snook, N. and Loken, E. 2022: Machine Learning for Convective Weather. AIES, in review.
Chase, R. J., Harrison, D. R., Lackmann G. and McGovern A. 2022: A Machine Learning Tutorial for Operational Meteorology, Part II: Neural Networks and Deep Learning. WAF, in review. https://doi.org/10.48550/arXiv.2211.00147
Chase, R. J., McGovern, A., Homeyer, C., Marinescu, P. and Potvin, C. 2022: Machine Learning Estimation of Storm Updrafts. AIES, in prep.


Associate Editor, Artificial Intelligence for the Earth Systems (AIES), American Meteorology Society, 2021 - present
Scientific and Technological Activities Commission (STAC) Member, Planned and Inadvertent Weather Modification, American Meteorology Society, 2019-2021

Last Updated: 2/23