Faculty Mentor

Daniel Halperin


Operational track forecasts of Tropical Cyclones (TCs) have been improved substantially in recent years and nowadays are sufficiently accurate. However, intensity forecasts have not shown similar improvements, especially for rapidly intensifying storms. The improvement of intensity forecast accuracy can help authorities in risk management and decision making to prevent loss of life and property. The purpose of our project is to develop a statistical linear regression model that provides better predictions for TC intensification over the ocean. Here, different predictor variables are studied, and 2011-2017 Atlantic basin storms are investigated. The final set of predictor variables selected for the model are Reynolds sea surface temperature, 700-500 hPa relative humidity, 200-800 km disk average 850-200 hPa wind shear magnitude, and 200 hPa divergence. We identify the variables that are the most deterministic in predicting TC intensification. Model performance tests, based on the 2018 Atlantic TC season, reveal a mean absolute error of 10.43 knots in the 24-hour intensity forecast. We conclude that Reynolds sea surface temperature is the most deterministic predictor, having the largest coefficient and test statistic, what is consistent with known TC physical mechanisms.

Included in

Meteorology Commons



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