Lindsay Fitzpatrick
Environmental Data Scientist
SEAS CIGLR
4840 South State Road
Ann Arbor, MI 48108-9719
In the News:
- One-Way Air–Lake Model Linkage Improves Lake-Effect Snow Forecasts, Bulletin of the American Meteorological Society, 12/23/2020
- Using the NOAA National Water Model to Guide Fertilizer Application and Improve Water Quality, CIGLR Summer 2019 eNews, 06/2019
- Forecasting the Future of Lake Effect Snow, CIGLR Winter 2018 eNews, 03/2018
- November 2014 storms become case study for better lake-effect forecasting, The Buffalo News, 12/9/2017
- Building a better lake-effect snow forecast, GoErie.com, 12/1/2017
- Better lake-effect forecasts are coming, The Buffalo News, 11/24/2017
- Imagine a Great Lakes weather forecaster who’s always right, Great Lakes Echo, 11/20/2017
Photo Gallery
As an environmental data scientist, Lindsay Fitzpatrick is working to help develop the next generation of Runoff Risk tools using the National Water Model (NWM). Her role is to use WRF-Hydro to conduct skill assessment on the NWM and its ability to model surface runoff at the edge-of-field scale. Before that, she worked with the Coastal Storms Project to reconstruct heat fluxes and evaporation over the Great Lakes to better understand and forecast lake effect snow events. She has had a significant role in the acquisition and maintenance of over water flux stations within the Great Lakes Evaporation Network (GLEN) and processing turbulent heat flux data to be used for model assessment and forecasting.
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:
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]
Video Library
Lake effect snow storms have huge impacts on transportation, public safety and business revenue. Recently, great improvements have been made to forecasting lake effect snow and scientists from CIGLR are a part of it. Dr. Ayumi Fujisaki-Manome, CIGLR Assistant Research Scientist, and Lindsay Fitzpatrick, CIGLR Atmospheric Data Analyst, recently collaborated on an article that discusses instruments and models that can help improve the accuracy of lake effect snow forecasting. m
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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.”