Machine learning-based dynamical seasonal prediction of summer rainfall in China
- 1Faculty of Geographical Science, Beijing Normal University, Beijing, China (yangjing@bnu.edu.cn)
- 2National Climate Center, China Meteorological Administration, Beijing, China (renhl@cma.gov.cn)
- 3State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China (baoqing@mail.iap.ac.cn))
- 4School of Geographic Sciences, Nanjing University of Information Science and Technology, Nanjing, China (gaomn@nuist.edu.cn)
The seasonal prediction of summer rainfall is crucial for regional disaster reduction but currently has a low prediction skill. This study developed a machine learning (ML)-based dynamical (MLD) seasonal prediction method for summer rainfall in China based on suitable circulation fields from an operational dynamical prediction model CAS FGOALS-f2. Through choosing optimum hyperparameters for three ML methods to reach the best fitting and the least overfitting, gradient boosting regression trees eventually exhibit the highest prediction skill, obtaining averaged values of 0.33 in the reference training period (1981-2010) and 0.19 in eight individual years (2011-2018) of independent prediction, which significantly improves the previous dynamical prediction skill by more than 300%. Further study suggests that both reducing overfitting and using the best dynamical prediction are imperative in MLD application prospects, which warrants further investigation.
How to cite: Wang, J., Yang, J., Ren, H., Li, J., Bao, Q., and Gao, M.: Machine learning-based dynamical seasonal prediction of summer rainfall in China, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12271, https://doi.org/10.5194/egusphere-egu2020-12271, 2020
