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A data-driven multi-model ensemble for deterministic and probabilistic precipitation forecasting at seasonal scale

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Abstract

Seasonal precipitation forecasting is valuable for regional water management and agricultural food security. Current numerical models have large uncertainty in model structure, parameterization and initial conditions. Here, a data-driven multi-model ensemble is constructed using a series of statistical and machine learning methods with varying inputs. Deterministic precipitation forecasts are produced by the weighting of ensemble members using Bayesian model averaging (BMA) and probabilistic forecasts are generated by sampling from BMA predictive probability density function (PDF). Three mathematical metrics are used to evaluate the performance of precipitation forecasts, including Pearson’s correlation coefficient (PCC), root mean square error skill score (RMSESS) and continuous ranked probability skill score (CRPSS). The results demonstrate that the accuracy in the statistical ensemble is significantly higher than the North American multi-model ensemble (NMME) for both deterministic and probabilistic precipitation forecasts, especially at 1-month lead. Statistical models are considerably enhanced by incorporating wavelets, which decomposes the raw precipitation series into several different levels, potentially representing underlying precipitation patterns at different time-frequency scales. Selecting some good ensemble members can improve the ensemble performance, instead of including all the ensemble members with some inefficient models. Overall, the statistical ensemble can be considered as an effective complement of numerical models in both deterministic and probabilistic precipitation forecasts.

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Acknowledgements

This work was supported by grants from the National Key Research and Development Projects (2018YFB2100500), National Natural Science Foundation of China program (no. 41890822), the National Nature Science Foundation of China program (41801339, 41971351, 41771422, 41601406) and the Nature Science Foundation of Hubei Province (2017CFB616).

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Authors and Affiliations

  1. State Key Laboratory of Information Engineering in Surveying, Map**, and Remote Sensing, Wuhan University, Wuhan, 430079, China

    Lei Xu, Nengcheng Chen, **ang Zhang & Zeqiang Chen

  2. Collaborative Innovation Center of Geospatial Technology, Wuhan, 430079, China

    Nengcheng Chen

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  1. Lei Xu
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Xu, L., Chen, N., Zhang, X. et al. A data-driven multi-model ensemble for deterministic and probabilistic precipitation forecasting at seasonal scale. Clim Dyn 54, 3355–3374 (2020). https://doi.org/10.1007/s00382-020-05173-x

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