Full
Outline
Based on subseasonal-to-seasonal (S2S) datasets obtained from four models (ECMWF, CMA, UKMO, and NCEP) and ground-observed precipitation data for April–September 1997–2022, an improved deep residual network (ResNet) model was developed for the extended-range probabilistic forecasting of persistent extreme precipitation (PEP). The deterministic and probabilistic forecasts of the improved ResNet model were compared with those of five other methods (single-model ensemble, multi-model ensemble, principal component regression, and Bayesian model averaging). Results demonstrated that the improved ResNet model outperformed other methods in probabilistic forecasting. The enhanced performance of the ResNet model is attributed to the improvement in spatial correlation coefficients, which stems from its ability to deeply extract PEP features from the ECMWF S2S data and the optimization of the ResNet structure. As a result, the improved ResNet model achieves a positive Brier skill score for forecast lead times up to 30 d, highlighting its considerable potential for extended-range probabilistic forecasting of PEP in eastern China.
148
67
| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
关闭全屏
No. 86 Xueyuan South Road, Haidian District, Beijing
010-62662699(Operation and Technical Support)
qkjq@cast.org.cn
Copyright © 2025 China Association for Science and Technology. All rights reserved. For all open access content, the relevant licensing terms apply.
Sponsored by the Office of the Leading Group for Cybersecurity and Informatization of CAST, and supported by Science and Technology Review Publishing House
