Significant advancement in subseasonal-to-seasonal summer precipitation ensemble forecast skills in China mainland through an innovative hybrid CSG-Unet method
Bibliographic Data
| ID | 15544237 |
|---|---|
| Authors | Yang Lyu (0000-0002-2333-4383, Nanjing University of Information Science and Technology), Shoupeng Zhu (0000-0002-4741-1179, China Meteorological Administration, corresponding author), Xiefei Zhi (0000-0003-4414-0497, Ministry of Education), Jingyu Wang (0000-0003-0433-9549, Nanyang Technological University), Yan Ji (0000-0002-6801-4666, Nanjing University of Information Science and Technology), Yi Fan (0000-0002-3598-5239), Fan Yi (0000-0002-3231-6208, Nanjing University of Information Science and Technology), Dong Fu (0000-0002-6715-1896, Nanjing University of Information Science and Technology), Fu Dong |
| Year | 2024 |
| Volume | 19 |
| Issue | 7 |
| Pages | 074055-074055 |
| Publication date | 2024-06-07 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Environmental Research Letters (JOURNAL) |
| Journal identifiers | ISSN: 1748-9326 • E-ISSN: 1748-9326 |
| Publisher | IOP Publishing (PUBLISHER • GB) |
| DOI | 10.1088/1748-9326/ad5577 |
| OpenAlex | W4399419004 |
| Language | EN |
| References cited | 48 |
Reliable Subseasonal-to-Seasonal (S2S) forecasts of precipitation are critical for disaster prevention and mitigation. In this study, an innovative hybrid method CSG-UNET combining the UNET with the censored and shifted gamma distribution based ensemble model output statistic (CSG-EMOS), is proposed to calibrate the ensemble precipitation forecasts from ECMWF over the China mainland during boreal summer. Additional atmospheric variable forecasts and the data augmentation are also included to deal with the potential issues of low signal-to-noise ratio and relatively small sample sizes in traditional S2S precipitation forecast correction. The hybrid CSG-UNET exhibits a notable advantage over both individual UNET and CSG-EMOS in improving ensemble precipitation forecasts, simultaneously improving the forecast skills for lead times of 1–2 weeks and further extending the effective forecast timeliness to ∼4 weeks. Specifically, the climatology-based Brier Skill Scores are improved by ∼0.4 for the extreme precipitation forecasts almost throughout the whole timescale compared with the ECMWF. Feature importance analyze towards CSG-EMOS model indicates that the atmospheric factors make great contributions to the prediction skill with the increasing lead times. The CSG-UNET method is promising in subseasonal precipitation forecasts and could be applied to the routine forecast of other atmospheric and ocean phenomena in the future
China · Climatology · Forecast skill · Forecast verification · Geography · Mainland China · Meteorology · Precipitation · Quantitative precipitation forecast · Statistic · Statistics · Arctic and Antarctic ice dynamics · Climate variability and models · Environmental Science · Mathematics · Meteorological Phenomena and Simulations
| Highly cited | No |
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