Research

Clustering Analysis of Autumn Weather Regimes in the Northeast U.S.

  • Funded by NASA NESSF Grant NNX15AN91H S03, NOAA NA20OAR4310424, and NSF AGS-1623912
  • Supervisor: Dr. Mathew Barlow
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  • Identified typical autumn circulation patterns across the Northeast U.S. using a k-means clustering algorithm coded in MATLAB.
  • Analyzed seasonality of the circulation patterns, confirming previous observations from other studies that summer is lasting longer in the year and winter starting later.
  • Read the Paper!
  • View the code!

Analyzing the Role of Integrated Vapor Transport and Diabatic Heating on Extreme Precipitation Events in the Northeast U.S.

  • Funded by NOAA NA20OAR4310424, and NSF AGS-1623912
  • Supervisor: Dr. Mathew Barlow
  • Modelled 20 extreme-precipitation events in the Northeast U.S. using the WRF-ARW that were associated with Warm Conveyor Belts and Moisture Transport.
  • Configured WRF-ARW to run with and without diabatic heating terms using a two-way nested model of resolutions 27km and 3km.
  • Coded python scripts to plot and analyze the output, determining that diabatic heating played a significant role in Integrated Vapor Transport, its absence causing weakening of extreme precipitation events, with some events not producing enough precipitation to cross the threshold of extreme precipitation.
  • Lead author on paper in development.

Spring Season Circulation Evolution and Relation to Extreme Events in the Northeast U.S.

  • Funded by NASA NESSF Grant NNX15AN91H S03, NOAA NA20OAR4310424, and NSF AGS-1623912
  • Supervisor: Dr. Mathew Barlow
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  • Identified spring season circulation patterns using K-means clustering and performed analysis on seasonal evolution using them.
  • Enhanced understanding of extreme Precipitation, Heat wave, and Drought events during the spring season in the Northeast U.S. by investigating their relation to the underlying seasonal circulation patterns.
  • Lead author on paper in preparation.

Understanding Winter Season Onset and Withdrawal Timing in the Northeast U.S. Using Circulation Patterns and Deep Learning

  • Supervisor: Dr. Mathew Barlow
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  • Developed a Siamese Neural Network model to perform image matching on small datasets.
  • Matched out of season dates to typical spring and fall circulation patterns to determine typical onset and withdrawal of winter.
  • Lead author on paper in development.

A Climatology of Snow Squalls in Southern New England 1994 - 2018

  • Supervisor: Dr. Frank Colby
  • Coded FORTRAN and Python scripts to download METAR station data and determine dates, times, and locations of snow squall events based on given observations. Created a dataset of NEXRAD files for the dates and times of squalls based on proximity to radar sites KBOX and KENX.
  • Reconfigured existing Python script (TINT) for cell tracking to work with the NEXRAD dataset of snow squall events and output the average direction of cell movement as both a .csv file and plotted on a radial plot of cardinal directions.
  • Snow squalls were grouped based on average direction of movement, resulting in 4 main types of squalls that varied in orientation and direction of movement based on associated fronts and available convection (either or both CAPE and slantwise).
  • Co-author on paper in review at Monthly Weather Review.