Machine Learning Models for the Seasonal Forecast of Winter Surface Air Temperature in North America

Machine Learning Models for the Seasonal Forecast of Winter Surface Air Temperature in North America

Abstract In this study, two machine learning (ML) models (support vector regression (SVR) and extreme gradient boosting (XGBoost)) are developed to perform seasonal forecasts of the surface air temperature (SAT) in winter (December‐January‐February, DJF) in North America (NA). The seasonal forecast...

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Bibliographic Details
Main Authors: Qi Feng Qian, Xiao Jing Jia, Hai Lin
Format: Article
Language:English
Published: American Geophysical Union (AGU) 2020-08-01
Series:Earth and Space Science
Subjects:
machine learning
support vector regression
XGBoost
seasonal forecast
North America
Online Access:https://doi.org/10.1029/2020EA001140

Internet

https://doi.org/10.1029/2020EA001140

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