- Program Year: 4
- Academic Institution: Colorado State University
- Field of Study: Atmospheric Science
- Academic Advisor: Elizabeth Barnes
Lawrence Berkeley National Laboratory (2018)
Oak Ridge National Laboratory (2019)
B.S. Civil Engineering, and B.S. Meteorology, University of Oklahoma, 2016
Summary of Research
How can we accelerate scientific discovery using machine learning and artificial intelligence? Machine learning has commonly been treated as a black box, but my collaborators and I are trying to break down this stigma. I use machine learning, mostly neural networks, to learn more about how we can predict climate and weather patterns relevant to decision making.
My current research focuses on decadal predictability. Can we use information about the ocean to predict climate over the continents years to a decade in the future? It seems like we can, but we also haven't identified all of the climate regimes that are most favorable for predictability on these time scales. Machine learning can help accelerate this process by efficiently identifying such climate regimes, which enables atmospheric scientists such as myself to focus on the physical mechanisms that produce these regimes.
Barnes, E. A., Toms, B., Hurrell, J. W., Ebert-Uphoff, I., Anderson, C., & Anderson, D. (2020). Indicator patterns of forced change learned by an artificial neural network. Journal of Advances in Modeling Earth Systems, 12, e2020MS002195. https://doi.org/10.1029/2020MS002195
Prabhat, Kashinath, K., Mudigonda, M., Kim, S., Kapp-Schwoerer, L., Graubner, A., Karaismailoglu, E., von Kleist, L., Kurth, T., Greiner, A., Yang, K., Lewis, C., Chen, J., Lou, A., Chandran, S., Toms, B., Chapman, W., Dagon, K., Shields, C. A., O'Brien, T., Wehner, M., and Collins, W.: ClimateNet: an expert-labelled open dataset and Deep Learning architecture for enabling high-precision analyses of extreme weather, Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2020-72, 2020.
van den Heever, S. C. and coauthors: Diving into cold pools and flying into updrafts of deep convective storms. Submitted to the Bulletin of the American Meteorological Society.
Toms, B. A., Barnes, E. A., & Ebert-Uphoff, I. (2020). Physically Interpretable Neural Networks for the Geosciences: Applications to Earth System Variability. Journal of Advances in Modeling Earth Systems, 12, e2019MS002002. https://doi.org/10.1029/2019MS002002
Toms, B. A., Kashinath, K., Prabhat, and Yang, D.: Testing the Reliability of Interpretable Neural Networks in Geoscience Using the Madden-Julian Oscillation, Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2020-152, 2020.
Toms, B., A, Barnes, E. A., Maloney, E. D., & van den Heever, S. C. ( 2020). The global teleconnection signature of the Madden-Julian oscillation and its modulation by the quasi-biennial oscillation. Journal of Geophysical Research: Atmospheres, 125, e2020JD032653. https://doi.org/10.1029/2020JD032653
Riley Dellaripa, E. M., E. D. Maloney, B. A. Toms, S. M. Saleeby, and S. C. van den Heever, 2019: Topographic effects on the Luzon diurnal cycle during the BSISO, J. Atmos. Sci., https://doi.org/10.1175/JAS-D-19-0046.1
Toms. B. A., S. C. van den Heever, E. M. Riley Dellaripa S. M. Saleeby, and E. D. Maloney, 2019: The relationship between the Boreal Summertime Madden-Julian Oscillation and tropical moist convective morphology, J. Atmos. Sci., https://doi.org/10.1175/JAS-D-19-0029.1
Toms, B. A., J. M. Tomaszewski, D. D. Turner, and S. S. Koch, 2017: Analysis of a lower-tropospheric gravity wave train using direct and remote sensing measurement systems. Mon. Wea. Rev., 145, 2791-2812, https://doi.org/10.1175/MWR-D-16-0216.1
Toms, B. A., J. B. Basara, and Y. Hong, 2017: Usage of existing meteorological data networks for parameterized road ice formation modeling. J. Appl. Meteor. Climatol., 56, 1959-1976, https://doi.org/10.1175/JAMC-D-16-0199.1
(2017) DOE Computational Science Graduate Fellowship
(2016) American Meteorological Society Graduate Fellowship - Lockheed Martin Sponsorship
(2015) Barry M. Goldwater Scholar
(2015) Astronaut Scholarship Foundation Scholar
(2015) American Meteorological Society Named Scholar - Orville Family Endowment
(2014) National Institute of Standards and Technology (NIST) Summer Undergraduate Fellow
(2020) Riehl Award - Best Early PhD Publication in CSU Department of Atmospheric Science
(2017) Mark and Kandi McCasland Award for Outstanding Undergraduate Research Paper
(2016) Letzeiser Honorable Mention - OU's 13 Most Outstanding Men
(2016) Outstanding Senior in Civil Engineering - OU CEES
(2020) Outstanding Oral Presentation Award; 33rd Conference on Climate Variability and Change
(2020) Outstanding Oral Presentation Award; 26th Conference on Probability and Statistics
(2019) Outstanding Oral Presentation; 18th Conference on Artificial Intelligence and Computational Intelligence and its Application to the Environmental Sciences
(2019) Outstanding Oral Presentation; 32nd Conference on Climate Variability and Change
(2018) Outstanding Student Presentation; 33rd Conference on Hurricanes and Tropical Meteorology
(2017) 1st Place Oral Presentation; 33rd Conference on Environmental Information Processing Technologies
(2016) Outstanding Poster Presentation Honorable Mention; 3rd Conference on Atmospheric Biogeosciences
(2015) Best Student Presentation Award; 2015 AMS Meeting: 10th Symposium on Societal Applications
(2014) Best Undergraduate Oral Presentation; NWA 39th Annual Meeting