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John Cintineo

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Integrated Sensing Group (ISG)

Job Title:Research Meteorologist

Affiliation:Federal

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John is a research meteorologist at the National Severe Storms Laboratory (NSSL), but stationed at the University of Wisconsin in Madison. His primary area of research combines remote sensing data and machine-learning methods to predict convective storm hazards for operational purposes. John was a key developer of the ProbSevere v3 and LightningCast models, especially during his time at the Cooperative Institute for Meteorological Satellite Studies. He has participated in numerous Hazardous Weather Testbed experiments, learning about forecaster needs, challenges, and how they use satellite and radar data in the warning process. John has also worked on volcanic ash detection and characterization research, as supports the Next-Generation Fire System (NGFS) research need for thunderstorm nowcasting guidance.

Education
Degree (Ph.D, M.S, B.A, etc.) Major Subject University or College Name Year (YYYY) (optional)
M.S. Meteorology University of Oklahoma 2011
B.S. Atmospheric Science Cornell University 2009
Research Interests
  • Convective storms
  • Satellite meteorology
  • Radar meteorology
  • Artificial Intelligence / Machine Learning
  • Lightning
  • Short-term forecasting
  • Image processing
Professional Activities
  • American Meteorological Society
Outreach/Volunteer
  • AMS Satellite Meteorology, Oceanography, and Climatology committee
  • Guest lectures at local high schools in Madison, WI
Honors & Awards
Award Name Year
World Meteorological Organization – Artificial Intelligence Nowcasting Pilot Project 2022-present
NOAA Technology Transfer Award 2013
AMS Loren Crow Memorial Scholarship 2009
NOAA Hollings Scholar 2008-2009
Selected Publications
  • Cintineo, J. L., M. J. Pavolonis, J. M. Sieglaff, 2022: ProbSevere LightningCast: A deep-learning model for satellite-based lightning nowcasting. Wea. Forecasting, 37, 1239-1257.

  • Cintineo, J. L., M. J. Pavolonis, J. M. Sieglaff, A. Wimmers, and J. Brunner, 2020: A deep-learning model for automated detection of intense mid-latitude convection using geostationary satellite images. Wea. Forecasting, 35, 2567-2588.

  • Cintineo, J. L., M. J. Pavolonis, J. M. Sieglaff, L. Cronce, and J. Brunner, 2020: NOAA ProbSevere v2.0 – ProbHail, ProbWind, and ProbTor. Wea. Forecasting, 35, 1523–1543.

  • Pavolonis, J. M. Sieglaff, and J. L. Cintineo, 2018: Automated Detection of Explosive Volcanic Eruptions Using Satellite-derived Cloud Vertical Growth Rates. Earth and Space Science, 5, 12, 903-928.

  • Cintineo, J. L., M. J. Pavolonis, J. M. Sieglaff, D. T. Lindsey, L. Cronce, J. Gerth, B. Rodenkirch, J. Brunner, and C. Gravelle, 2018: The NOAA/CIMSS ProbSevere Model - incorporation of total lightning and validation. Wea. Forecasting, 33, 331–345.

  • Karstens, C. D., J. Correia Jr., D. S. LaDue, J. Wolf, T. C. Meyer, D. R. Harrison, J. L. Cintineo, K. M. Calhoun, T. M. Smith, A. E. Gerard, L. P. Rothfusz, 2018: Development of a human-machine mix for forecasting severe convective events. Wea. Forecasting, 33, 715-737.

  • Pavolonis, M. J., J. M. Sieglaff, and J. L. Cintineo, 2015: Spectrally Enhanced Cloud Objects (SECO): A Generalized Framework for Automated Detection of Volcanic Ash and Dust Clouds using Passive Satellite Measurements: 2. Cloud Object Analysis and Global Application, J. Geophys. Res. – Atmospheres, 120, 15, (7842-7870).

  • Pavolonis, M. J., J. M. Sieglaff, and J. L. Cintineo, 2015: Spectrally Enhanced Cloud Objects (SECO): A Generalized Framework for Automated Detection of Volcanic Ash and Dust Clouds using Passive Satellite Measurements: 1. Multispectral Analysis, J. Geophys. Res. – Atmospheres, 120, 15, (7813-7841).

  • Cintineo, J. L., M. J. Pavolonis, J. M. Sieglaff, and D. T. Lindsey, 2014: An empirical model for assessing the severe weather potential of developing convection. Wea. Forecasting, 29, 639-653.

  • Schmit, T. J., S. J. Goodman, D. T. Lindsey, R. M. Rabin, K. M. Bedka, M. M. Gunshor, J. L. Cintineo, C. S. Velden, A. S. Bachmeier, S. S. Lindstrom, and C. C. Schmidt, 2013: GOES-14 super rapid scan operations to prepare for GOES-R. J. Appl. Remote. Sens., 7, 073462.

  • Cintineo, J. L., M. J. Pavolonis, J. M. Sieglaff, and A. K. Heidinger, 2013: Evolution of severe and non-severe convection inferred from GOES-derived cloud properties. J. Appl. Meteorol. Climatol., 52, 2009-2023.

  • Cintineo, J. L., T. M. Smith, V. Lakshmanan, H. E. Brooks, and K. L. Ortega, 2012: An objective high-resolution hail climatology of the contiguous United States. Wea. Forecasting, 27, 1235-1248.