Comparison of different ensemble precipitation forecast system evaluation, integration and hydrological applications

• Autorzy: Tang Yehai , Wu Qiang , Soomro Shan-e-hyder , Li Xiang , Sun Yue , Hu Caihong

Identyfikatory

DOI

10.1007/s11600-022-00877-6

• Języki publikacji: EN

Abstrakty

EN

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 food peaks during the food season.

Słowa kluczowe

EN

TIGGE; precipitation; numerical weather prediction; integration methods

PL

TIGGE; opad atmosferyczny; numeryczna prognoza pogody; metody integracji

• Wydawca: Instytut Geofizyki PAN ; Springer
• Czasopismo: Acta Geophysica
• Rocznik: 2023
• Tom: Vol. 71, no. 1
• Strony: 405--421
• Opis fizyczny: Bibliogr. 55 poz.

Twórcy

autor

Tang Yehai

• School of Water Conservancy Science and Engineering, Zhengzhou University, Zhengzhou 450001, China
• Yellow River Institute for Ecological Protection & Regional Coordinated Development, Zhengzhou 450001, China

autor

Wu Qiang

• School of Water Conservancy Science and Engineering, Zhengzhou University, Zhengzhou 450001, China
• Yellow River Institute for Ecological Protection & Regional Coordinated Development, Zhengzhou 450001, China

autor

Soomro Shan-e-hyder

• School of Water Conservancy Science and Engineering, Zhengzhou University, Zhengzhou 450001, China
• Yellow River Institute for Ecological Protection & Regional Coordinated Development, Zhengzhou 450001, China

autor

Li Xiang

• School of Water Conservancy Science and Engineering, Zhengzhou University, Zhengzhou 450001, China
• Yellow River Institute for Ecological Protection & Regional Coordinated Development, Zhengzhou 450001, China

autor

Sun Yue

• School of Water Conservancy Science and Engineering, Zhengzhou University, Zhengzhou 450001, China
• Yellow River Institute for Ecological Protection & Regional Coordinated Development, Zhengzhou 450001, China

autor

Hu Caihong

• School of Water Conservancy Science and Engineering, Zhengzhou University, Zhengzhou 450001, China
• Yellow River Institute for Ecological Protection & Regional Coordinated Development, Zhengzhou 450001, China
• Henan Yellow River Hydrological Survey and Design Institute, Zhengzhou 450004, China

• https://orcid.org/0000-0002-1839-6352

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).