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Seasonal Forecast of Nonmonsoonal Winter Precipitation over the Eurasian Continent Using Machine-Learning Models

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  • 1 a Key Laboratory of Geoscience Big Data and Deep Resource of Zhejiang Province, School of Earth Sciences, ZheJiang University, HangZhou, Zhejiang, China
  • | 2 b Recherche en Prévision Numérique Atmosphérique, Environment and Climate Change Canada, Dorval, Quebec, Canada
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Abstract

In this study, four machine-learning (ML) models [gradient boost decision tree (GBDT), light gradient boosting machine (LightGBM), categorical boosting (CatBoost), and extreme gradient boosting (XGBoost)] are used to perform seasonal forecasts for nonmonsoonal winter precipitation over the Eurasian continent (30°–60°N, 30°–105°E) (NWPE). The seasonal forecast results from a traditional linear regression (LR) model and two dynamic models are compared. The ML and LR models are trained using the data for the period of 1979–2010, and then these empirical models are used to perform the seasonal forecast of NWPE for 2011–18. Our results show that the four ML models have reasonable seasonal forecast skills for the NWPE and clearly outperform the LR model. The ML models and the dynamic models have skillful forecasts for the NWPE over different regions. The ensemble means of the forecasts including the ML models and dynamic models show higher forecast skill for the NWEP than the ensemble mean of the dynamic-only models. The forecast skill of the ML models mainly benefits from a skillful forecast of the third empirical orthogonal function (EOF) mode (EOF3) of the NWPE, which has a good and consistent prediction among the ML models. Our results also illustrate that the sea ice over the Arctic in the previous autumn is the most important predictor in the ML models in forecasting the NWPE. This study suggests that ML models may be useful tools to help improve seasonal forecasts of the NWPE.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: XiaoJing Jia, jiaxiaojing@zju.edu.cn

Abstract

In this study, four machine-learning (ML) models [gradient boost decision tree (GBDT), light gradient boosting machine (LightGBM), categorical boosting (CatBoost), and extreme gradient boosting (XGBoost)] are used to perform seasonal forecasts for nonmonsoonal winter precipitation over the Eurasian continent (30°–60°N, 30°–105°E) (NWPE). The seasonal forecast results from a traditional linear regression (LR) model and two dynamic models are compared. The ML and LR models are trained using the data for the period of 1979–2010, and then these empirical models are used to perform the seasonal forecast of NWPE for 2011–18. Our results show that the four ML models have reasonable seasonal forecast skills for the NWPE and clearly outperform the LR model. The ML models and the dynamic models have skillful forecasts for the NWPE over different regions. The ensemble means of the forecasts including the ML models and dynamic models show higher forecast skill for the NWEP than the ensemble mean of the dynamic-only models. The forecast skill of the ML models mainly benefits from a skillful forecast of the third empirical orthogonal function (EOF) mode (EOF3) of the NWPE, which has a good and consistent prediction among the ML models. Our results also illustrate that the sea ice over the Arctic in the previous autumn is the most important predictor in the ML models in forecasting the NWPE. This study suggests that ML models may be useful tools to help improve seasonal forecasts of the NWPE.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: XiaoJing Jia, jiaxiaojing@zju.edu.cn

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