Corene Matyas
Professor
- Gainesville FL UNITED STATES
- College of Liberal Arts and Sciences
Corene Matyas researches the spatial analysis of rainfall from tropical cyclones.
Contact More Open optionsBiography
Corene Matyas is a geography professor who teaches courses on hurricanes, atmospheric teleconnections, climatology and data analysis using GIS. Their research areas of specialization include tropical climatology, hurricanes, severe weather and remote sensing of rainfall.
Areas of Expertise
Media Appearances
Latest update from Florida as Hurricane Idalia hits the state
CBC Listen radio
2023-08-30
In this 8:20 a.m. PDT update, Stephen speaks with Corene Matyas, a professor of geography at the University of Florida who specializes in hurricane climatology, about the storm's impact.
Florida braces for 'extremely dangerous' Hurricane Idalia
ABC News Radio (Australia) radio
2023-08-30
Hurricane Idalia has been gathering strength as it moves north across the Gulf of Mexico and is expected to hit the US state of Florida within hours.
How climate change fueled Hurricane Idalia
CNN tv
2023-08-30
University of Florida geography professor Dr. Corene Matyas and CNN meteorologist Chad Myers join Jake Tapper to discuss how climate change played a role in intensifying Hurricane Idalia.
Why Cyclone Idai was so destructive
National Geographic online
2019-03-19
Cyclone Idai may have killed more than 1,000 people and left 400,000 homeless near the port city of Beira in the southeastern African nation of Mozambique.
Cyclone Kenneth Threatens Africa - Will The World Pay Attention This Time?
Forbes online
2019-04-24
Back in mid-March, I wrote a piece for Forbes urging people to pay attention to Tropical Cyclone Idai. It was bearing down on extremely vulnerable populations in parts of African.
Articles
Classification of tropical cyclone rain patterns using convolutional autoencoder
Scientific ReportsDasol Kim & Corene J. Matyas
2024-01-08
Heavy rainfall produced by tropical cyclones (TCs) frequently causes wide-spread damage. TCs have different patterns of rain depending on their development stage, geographical location, and surrounding environmental conditions. However, an objective system for classifying TC rain patterns has not yet been established. This study objectively classifies rain patterns of North Atlantic TCs using a Convolutional Autoencoder (CAE).
Evaluation of Experimental High-Resolution Model Forecasts of Tropical Cyclone Precipitation Using Object-Based Metrics
Weather and ForecastingShakira D. Stackhouse, et. al
2023-10-17
Tropical cyclone (TC) precipitation poses serious hazards including freshwater flooding. High-resolution hurricane models predict the location and intensity of TC rainfall, which can influence local evacuation and preparedness policies. This study evaluates 0–72-h precipitation forecasts from two experimental models, the Hurricane Analysis and Forecast System (HAFS) model and the basin-scale Hurricane Weather Research and Forecasting (HWRF-B) Model, for 2020 North Atlantic landfalling TCs.
Assessing environmental conditions associated with spatially varying rainfall structure of North Atlantic tropical cyclones: An object-based climatological analysis
International Journal of ClimatologyYao Zhou, et. al
2023-06-22
This study utilizes geographic information system-based shape analyses with spatial regressions to examine the spatial variations in the relationships between North Atlantic TC rain field patterns and the environmental conditions. We measure the area, solidity, dispersion and closure of rain fields associated with North Atlantic tropical cyclones (TCs) during 1998–2014 from satellite-based rain rate estimates.
Despite challenges, 2-year college students benefit from faculty-mentored geoscience research at a 4-year university during an extracurricular program
Journal of Geoscience EducationCorene J. Matyas, et. al
2022-02-17
This study details the mentored research component of a program intended to recruit, retain, and transfer students attending a two-year college (2YC) to four-year geosciences programs. Eighteen of 20 students who started the program were from minoritized backgrounds: 12 women, six racial/ethnic minorities, 12 low-income, and 13 first-generation college attendees.
