Improving the North American multi-model ensemble (NMME) precipitation forecasts at local areas using wavelet and machine learning
Seasonal precipitation forecasts at regional or local areas can help guide agricultural practice and urban water resource management. The North American multi-model ensemble (NMME) is a seasonal forecasting system providing precipitation forecasts globally. Bias correction and downscaling of the NMME is a critical step before applied at local scales. Here, the machine learning methods coupling with wavelet are used to correct the precipitation forecasts in NMME for 518 meteorological stations in China for eight models at 0.5–8.5 months leads. Compared with the traditional quantile mapping (QM) approach, the wavelet support vector machine (WSVM) and wavelet random forest (WRF) methods exhibit obvious advantage in downscaling, with an overall average improvement of Pearson’s correlation coefficient increasing by 0.05–0.3 and root mean square error (RMSE) reducing by 18–40 mm (21–33%) for individual models. Both the spatial and seasonal patterns of downscaled results demonstrate the superiority of wavelet machine learning methods over QM. A spatial analysis indicates that the corrected NMME precipitation forecasts show the best skill in South China, with an average RMSE of about 30 mm, while the worst skill in Central and Southwest China with a RMSE of 80 mm. In spite of the correction, the uncertainties of seasonal precipitation forecasts in summer and extreme wet cases are still large. However, the WSVM and WRF methods may serve as an effective tool in the bias correction of NMME precipitation forecasts.
KeywordsNMME Precipitation forecast Bias correction Wavelet Machine learning
This work was supported by Grants from the National Key Research and Development Program of China (2017YFB0503803), Creative Research Groups of Natural Science Foundation of Hubei Province of China (2016CFA003), the Fundamental Research Funds for the Central Universities (2042017GF0057), the National Nature Science Foundation of China program (41771422, 41890822, 41601406, 41801339), the Nature Science Foundation of Hubei Province (2017CFB616), the China Meteorological Administration Drought Research Fund (IAM201704), LIESMARS Special Research Funding (201806), and the China Postdoctoral Science Foundation (No. 2017M620338, 2018T110804).
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