Is this project an undergraduate, graduate, or faculty project?
Undergraduate
Project Type
group
Campus
Daytona Beach
Authors' Class Standing
Bruno Platero, Senior Lexi A. Comstoc, Senior
Lead Presenter's Name
Lexi Comstoc
Faculty Mentor Name
Dr. Daniel Halperin
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Abstract
Even though operational track forecasts of Tropical Cyclones (TC) have improved a lot in recent years and are highly accurate, intensity forecasts are still relatively poor, especially for rapidly intensifying storms. An increase in intensity forecast accuracy would help give more credibility to TC forecasts as well as tremendously help authorities in their risk management and decision making to prevent loss of life and property. Therefore, the purpose of this project is to develop a statistical linear regression model and determine if it can better predict TC intensification over water. For this project, different predictors variables will be tested, and 2011-2017 Atlantic basin storms will be analyzed. The initial set of predictors selected for the model are Reynolds sea surface temperatures, 700-500 mb relative humidity, 200-800 km disk average 850-200 mb wind shear magnitude, 200-800 km disk average 850-500 mb wind shear magnitude, and 200 mb divergence. This project also intends to identify which of the predictors is the most deterministic in predicting TC intensification. Once the initial model is developed it will be optimized and tested to determine its forecast accuracy. Preliminary results will be discussed.
Did this research project receive funding support (Spark, SURF, Research Abroad, Student Internal Grants, Collaborative, Climbing, or Ignite Grants) from the Office of Undergraduate Research?
No
Atlantic Tropical Cyclone Intensification Regression Model
Even though operational track forecasts of Tropical Cyclones (TC) have improved a lot in recent years and are highly accurate, intensity forecasts are still relatively poor, especially for rapidly intensifying storms. An increase in intensity forecast accuracy would help give more credibility to TC forecasts as well as tremendously help authorities in their risk management and decision making to prevent loss of life and property. Therefore, the purpose of this project is to develop a statistical linear regression model and determine if it can better predict TC intensification over water. For this project, different predictors variables will be tested, and 2011-2017 Atlantic basin storms will be analyzed. The initial set of predictors selected for the model are Reynolds sea surface temperatures, 700-500 mb relative humidity, 200-800 km disk average 850-200 mb wind shear magnitude, 200-800 km disk average 850-500 mb wind shear magnitude, and 200 mb divergence. This project also intends to identify which of the predictors is the most deterministic in predicting TC intensification. Once the initial model is developed it will be optimized and tested to determine its forecast accuracy. Preliminary results will be discussed.