Lindsay Fitzpatrick

As an Environmental Data Scientist, Lindsay Fitzpatrick is currently engaged in the Bipartisan Infrastructure Law (BIL) Project, where she is developing the next generation of operational net basin supply and water level forecasting tools for the Great Lakes on a subseasonal to annual (SA) scale. As a member of the new Great Lakes AI Lab, Lindsay aims to leverage innovative methodologies to enhance water level prediction capabilities through advanced data-driven modeling, contributing to effective water resource management and coastal resilience. Additionally, she collaborates with governmental agencies, environmental organizations, and community stakeholders to ensure that forecasting tools are aligned with real-world applications and community needs.

Education:

• • M.S., Atmospheric Science, University of Michigan, Ann Arbor, MI (2016)
  • Graduate Work, Meteorology, Florida State University, Tallahassee, FL (2011)
  • B.S., Meteorology and Mathematics, Central Michigan University, Mt. Pleasant, MI (2010)

Interests:

• • Forecasting
  • Severe Weather
  • Atmospheric Science
  • Atmospheric Chemistry
  • Great Lakes

Publications:

Fitzpatrick, L., Titze, D., Anderson, E.J. , Beletsky, D., Kelley, J.G.W. 2023. Simulating flood events at the Twin Ports of Duluth-Superior using a linked hydrologic-hydrodynamic framework. Ocean Dynamics. 73:433-447. (DOI:10.1007/s10236-023-01559-7). [Altmetric Score]

Ford, C.M., Hu, Y., Ghosh, C., Fry, L.M., Malakpour-Estalaki, S., Mason, L., Fitzpatrick, L., Mazrooei, A., and Goering, D.C. 2022. Generalization of Runoff Risk Prediction at Field Scales to a Continental-Scale Region Using Cluster Analysis and Hybrid Modeling. Geophysical Research Letters. 49:e2022GL100667. (DOI:10.1029/2022GL100667). 

Hu, Y., Fitzpatrick, L., Fry, L.M., Mason, L., Read, L.K., and Goering, D.C. 2021. Edge-of-field runoff prediction by a hybrid modeling approach using causal inference, Environmental Research Communications. 3:075003. (DOI:10.1088/2515-7620/ac0d0a).

Fujisaki-Manome, A., G.E. Mann, E.J. Anderson, P.Y. Chu, L.E. Fitzpatrick., S.G. Benjamin, E.P. James, T.G. Smirnova, C.R. Alexander and D.M. Wright. 2020. Improvements to lake-effect snow forecasts using a one-way air-lake model coupling approach. Journal of Hydrometeorology. (DOI:10.1175/JHM-D-20-0079.1). [Altmetric Score]

Charusombat, U., A. Fujisaki-Manome, A.D. Gronewold, B.M. Lofgren, E.J. Anderson, P.D. Blanken, C. Spence, J.D. Lenters, C. Xiao, L.E. Fitzpatrick and G. Cutrell. 2018. Evaluating and improving modeled turbulent heat fluxes across the North American Great Lakes. Hydrology and Earth Systems Sciences. (DOI:10.5194/hess-2017-725). [Altmetric Score]

Fujisaki-Manome, A., L.E. Fitzpatrick, A.D. Gronewold, E.J. Anderson, B.M. Lofgren, C. Spence, J. Chen, C. Shao, D.M. Wright and C. Xiao. 2017. Turbulent Heat Fluxes during an Extreme Lake-Effect Snow Event. Journal of Hydrometeorology. (DOI:10.1175/JHM-D-17-0062.1). [Altmetric Score]

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When doing research for his science fair project, Jason Wang (middle school student from California) found the CIGLR Minute video focused on lake effect snow. In an effort to understand this phenomenon better, Jason contacted CIGLR scientists Dr. Ayumi Fujiski-Manome and Lindsay Fitzpatrick for more information. Jason shared that, “Speaking to the [science fair] judges can be very stressful, but speaking with you all was very fun, helpful, and encouraging to me. Emailing you and getting to meet all of you was the best part of my project. Thank you again.”