Prediction of Tropical Cyclone Genesis from Mesoscale Convective Systems using Machine Learning
Tropical cyclone (TC) genesis is a problem of great significance in climate and weather research. Although various environmental conditions necessary for TC genesis have been recognized for a long time, prediction of TC genesis remains a challenge due to complex and stochastic processes involved during TC genesis. Different from traditional statistical and dynamical modeling of TC genesis, in this research, a machine learning framework is developed to determine whether a mesoscale convective system (MCS) would evolve into a tropical cyclone. The machine learning models 1) are built upon a number of essential environmental predictors associated with MCS/TC, 2) predict whether MCSs can become TCs at different lead times, 3) provide information about the relative importance of each predictor, which can be conducive to discovering new aspects of TC genesis. The results indicate that the machine learning classifier, AdaBoost, is able to achieve a 97.2% F1-score accuracy in predicting TC genesis over the entire tropics at a 6-hour lead time using a comprehensive set of environmental predictors. A robust performance can still be attained when the lead time is extended to 12 hours, 24 hours, and 48 hours, and when this machine learning classifier is separately applied to the North Atlantic Ocean and the Western North Pacific Ocean. In contrast, the conventional approach based on genesis potential index can only have no more than 80% F1-score accuracy. Moreover, the machine learning classifier suggests that the low-level vorticity and genesis potential index are the most important predictors to TC genesis, which is consistent with previous discoveries.
- Research Organization:
- Brookhaven National Lab. (BNL), Upton, NY (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC), Basic Energy Sciences (BES)
- Grant/Contract Number:
- SC0012704
- OSTI ID:
- 1526688
- Report Number(s):
- BNL-211773-2019-JAAM
- Journal Information:
- Weather and Forecasting, Vol. 34, Issue 4; ISSN 0882-8156
- Publisher:
- American Meteorological SocietyCopyright Statement
- Country of Publication:
- United States
- Language:
- English
Web of Science
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