The Development of a Consensus Machine Learning Model for Hurricane Rapid Intensification Forecasts with Hurricane Weather Research and Forecasting (HWRF) Data

Ko, Mu-Chieh; Chen, Xiaomin; Kubat, Miroslav; Gopalakrishnan, Sundararaman

doi:10.1175/waf-d-22-0217.1

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The Development of a Consensus Machine Learning Model for Hurricane Rapid Intensification Forecasts with Hurricane Weather Research and Forecasting (HWRF) Data

2023
By Ko, Mu-Chieh ; Chen, Xiaomin ; Kubat, Miroslav ; ...
Source: Weather and Forecasting (2023)

[PDF-2.18 MB]

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Journal Title:

Weather and Forecasting
Personal Author:

Ko, Mu-Chieh ; Chen, Xiaomin ; Kubat, Miroslav ; Gopalakrishnan, Sundararaman

Ko, Mu-Chieh ; Chen, Xiaomin ; Kubat, Miroslav ; Gopalakrishnan, Sundararaman Less -
NOAA Program & Office:

OAR (Oceanic and Atmospheric Research) ; AOML (Atlantic Oceanographic and Meteorological Laboratory) ; CIMAS (Cooperative Institute for Marine and Atmospheric Studies)

OAR (Oceanic and Atmospheric Research) ; AOML (Atlantic Oceanographic and Meteorological Laboratory) ; CIMAS (Cooperative Institute for Marine and Atmospheric Studies) Less -
Description:

This study focused on developing a consensus machine learning (CML) model for tropical cyclone (TC) intensity-change forecasting, especially for rapid intensification (RI). This CMLmodelwas built upon selected classical machine learning models with the input data extracted from a high-resolution hurricane model, the HurricaneWeather Research and Forecasting (HWRF) system. The input data contained 21 or 34 RI-related predictors extracted from the 2018 version of HWRF (H218). This study found that TC inner-core predictors can be critical for improving RI predictions, especially the inner-core relative humidity. Moreover, this study emphasized that the importance of performing resampling on an imbalanced input dataset. Edited Nearest Neighbor and Synthetic Minority Oversampling Technique improved the Probability of Detection (POD) by ∼10% for the RI class. This paper also showed that the CML model has satisfactory performance on RI predictions compared to the operational models. CML reached 56% POD and 46% False Alarm Ratio (FAR), while the operational models had only 10 to 30% POD but 50 to 60% FAR. The CML performance on the non-RI classes was comparable to the operational models. The results indicated that, with proper and sufficient training data and RI-related predictors, CML has the potential to provide reliable probabilistic RI forecasts during a hurricane season.
Keywords:

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Atmospheric Science
Source:

Weather and Forecasting (2023)
DOI:

https://doi.org/10.1175/waf-d-22-0217.1
ISSN:

0882-8156;1520-0434;
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PDF
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American Meteorological Society
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Journal Article
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urn:sha256:651ad10978192a321bdbf658029057927a084040b9cea41ae71f5458d0dcdcb9
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https://repository.library.noaa.gov/view/noaa/53692/noaa_53692_DS1.pdf
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The Development of a Consensus Machine Learning Model for Hurricane Rapid Intensification Forecasts with Hurricane Weather Research and Forecasting (HWRF) Data

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