Abstract
The examination and integration of numerical forecast products are essential for using and developing numerical forecasts and hydrological forecasts. In this paper, the control forecast products from 2010 to 2014 of four model data (China Meteorological Administration (CMA), the National Centers for Environmental Prediction (NCEP), the European Centre for Medium-Range Weather Forecasts (ECMWF), and the United Kingdom Meteorological Office (UKMO)) from The Interactive Grand Global Ensemble (TIGGE) data center were evaluated comprehensively. On this basis, a study of runoff forecasting based on multi-model (multiple regression (MR), random forest (RF), and convolutional neural network-gradient boosting decision tree (CNN-GBDT)) precipitation integration is carried out. The results show that the CMA model performs the worst, while the other models have their advantages and disadvantages in different evaluation indexes. Compared with the single-index optimal model, CMA model had a higher root-mean-square error (RMSE) of 18.4%, and a lower determination coefficient (R2) of 14.7%, respectively. The integration of multiple numerical forecast information is better than that of a single model, and CNN-GBDT method is superior to the multiple regression method and random forest method in improving the precision of rainfall forecast. Compared with the original model, the RMSE decreases by 13.1 ~ 27.9%, PO decreases to 0.538 at heavy rainfall, and the R2 increases by 4 ~ 15.2%, but the degree of improvement decreases gradually with the increase in rainfall order. The method of multi-model ensemble rainfall forecasting based on a machine learning model is feasible and can improve the accuracy of short-term rainfall forecasting. The runoff forecast based on multi-model precipitation integration has been improved, and NSE increases from 0.88 to 0.935, but there is still great uncertainty about flood peaks during the flood season.
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Data availability
TIGGE database used in this study in a 24-h lead time (https://conflfluence.ecmwf.int/display/TIGGE). TIGGE (The Interactive Grand Global Ensemble) is part of the THORPEX project, which is an initiative of the World Weather Research Programme (WWRP).
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Acknowledgements
The authors would like to acknowledge the financial support received from Projects of the National Natural Science Foundation of China (51979250), National key research priorities program of China (2016YFC040240203), Key projects of National Natural Science Foundation of China (51739009) and Key Research and Promotion Projects (technological development) in Henan Province (202102310587). This work is based on TIGGE data. TIGGE (The Interactive Grand Global Ensemble) is an initiative of the World Weather Research Programme (WWRP).
Funding
Projects of National Natural Science Foundation of China: Key technology study on flooding forecasting driven by intensive data in the middle reaches of Yellow watershed (51979250); Key projects of National Natural Science Foundation of China: Research on Theory and Method of Urban Flood Forecasting and Early Warning Based on Big Data (51739009); Key Research and Promotion Projects in Henan Province: Study on flood forecasting technology by deep learning based on water droplet migration path.
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YT, QW and CH: involved in conceptualization; YT and XL: contributed to methodology; YT and YS: involved in analysis; YT: contributed to writing—original draft; YT and SS: contributed to writing—review and editing; CH: involved in supervision.
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Tang, Y., Wu, Q., Soomro, Seh. et al. Comparison of different ensemble precipitation forecast system evaluation, integration and hydrological applications. Acta Geophys. 71, 405–421 (2023). https://doi.org/10.1007/s11600-022-00877-6
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DOI: https://doi.org/10.1007/s11600-022-00877-6