DOE PAGES title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: A new method for predicting hurricane rapid intensification based on co-occurring environmental parameters

Journal Article · · Natural Hazards

Abstract Tropical cyclones (TCs) that undergo Rapid Intensification (RI) can pose serious socioeconomic threats and can potentially result in major damaging impacts along coastal areas. Considering the complexity of various physical mechanisms that play a role in RI and its relatively low probability of occurrence, predicting RI remains a major operational challenge. In this study, we propose a simple deterministic binary classification model based on the co-occurrence of environmental parameters (MCE) to predict an RI event. More specifically, the model determines the possibility of RI based on a simple count of the number of environmental predictors deemed favorable and unfavorable. We compare our model results to logistic regression (LR) and decision tree (DT) models, well-trained using the same set of environmental predictors. Results reveal that at an RI threshold of 30 kt, the MCE exhibits a critical success index score of 0.233 which is 14% higher than DT and LR model performances. When tested at multiple RI thresholds, the MCE displays relatively higher skill scores across multiple metrics. By simultaneously evaluating the favorability of predictors, the MCE is able to comparatively reduce the number of false alarms predicted when certain predictors are unfavorable toward RI. Interpreting these model results to gain a physical understanding of how co-occurring environmental parameters can affect RI, we highlight future directions for using models based on the MCE approach to understand and predict TC RI as well as other meteorological extremes.

Sponsoring Organization:
USDOE Office of Science (SC), Biological and Environmental Research (BER)
OSTI ID:
2202512
Journal Information:
Natural Hazards, Journal Name: Natural Hazards Journal Issue: 1 Vol. 120; ISSN 0921-030X
Publisher:
Springer Science + Business MediaCopyright Statement
Country of Publication:
Netherlands
Language:
English

References (30)

The application of decision tree to intensity change classification of tropical cyclones in western North Pacific: DECISION TREE FOR TC INTENSITY CHANGE journal May 2013
Development of objective forecast guidance on tropical cyclone rapid intensity change journal March 2021
Climate-driven Atlantic hurricanes create complex challenges for cancer care journal December 2022
Atlantic Tropical Cyclone Rapid Intensification Probabilistic Forecasts from an Ensemble of Machine Learning Methods journal January 2017
Improved associated conditions in rapid intensifications of tropical cyclones journal October 2007
A “sufficient” condition combination for rapid intensifications of tropical cyclones journal October 2008
Applying Satellite Observations of Tropical Cyclone Internal Structures to Rapid Intensification Forecast With Machine Learning journal August 2020
A potential explanation for the global increase in tropical cyclone rapid intensification journal November 2022
On Summary Measures of Skill in Rare Event Forecasting Based on Contingency Tables journal December 1990
A Statistical Hurricane Intensity Prediction Scheme (SHIPS) for the Atlantic Basin journal June 1994
An Updated Statistical Hurricane Intensity Prediction Scheme (SHIPS) for the Atlantic and Eastern North Pacific Basins journal June 1999
Large-Scale Characteristics of Rapidly Intensifying Tropical Cyclones in the North Atlantic Basin journal December 2003
An Objective Model for Identifying Secondary Eyewall Formation in Hurricanes journal March 2009
Advances and Challenges at the National Hurricane Center journal April 2009
Visualizing Multiple Measures of Forecast Quality journal April 2009
A Revised Tropical Cyclone Rapid Intensification Index for the Atlantic and Eastern North Pacific Basins journal February 2010
Hurricane Ida (2021): Rapid Intensification Followed by Slow Inland Decay journal October 2022
Interbasin Differences in the Relationship between SST and Tropical Cyclone Intensification journal March 2018
“Dendrology” in Numerical Weather Prediction: What Random Forests and Logistic Regression Tell Us about Forecasting Extreme Precipitation journal June 2018
Association Rule Data Mining Applications for Atlantic Tropical Cyclone Intensity Changes journal June 2011
New Probabilistic Forecast Models for the Prediction of Tropical Cyclone Rapid Intensification journal October 2011
Improvements in the Probabilistic Prediction of Tropical Cyclone Rapid Intensification with Passive Microwave Observations journal August 2015
A Systematic Classification Investigation of Rapid Intensification of Atlantic Tropical Cyclones with the SHIPS Database journal March 2016
Evaluating Environmental Impacts on Tropical Cyclone Rapid Intensification Predictability Utilizing Statistical Models journal October 2015
A Long Short-Term Memory Model for Global Rapid Intensification Prediction journal August 2020
Further Improvements to the Statistical Hurricane Intensity Prediction Scheme (SHIPS) journal August 2005
Statistics for Research journal August 1992
An Advanced Artificial Intelligence System for Investigating Tropical Cyclone Rapid Intensification with the SHIPS Database journal April 2021
Operational Forecasting of Tropical Cyclone Rapid Intensification at the National Hurricane Center journal May 2021
Decision-Tree-Based Classification of Lifetime Maximum Intensity of Tropical Cyclones in the Tropical Western North Pacific journal June 2021