Improvement of disastrous extreme precipitation forecasting in North China by Pangu-weather AI-driven regional WRF model
Bibliographic Data
| ID | 15546271 |
|---|---|
| Authors | Hongxiong Xu (0000-0001-5067-0086, Chinese Academy of Meteorological Sciences, corresponding author), Yang Zhao (0000-0003-4913-8505, Ocean University of China), Dajun Zhao (0000-0003-1240-2835, State Key Laboratory of Severe Weather), Yihong Duan (0000-0002-1739-2907, Chinese Academy of Meteorological Sciences), Xiangde Xu (0000-0003-0772-5234, Chinese Academy of Meteorological Sciences) |
| Year | 2024 |
| Volume | 19 |
| Issue | 5 |
| Pages | 054051-054051 |
| Publication date | 2024-04-23 |
| Peer Reviewed | Yes |
| 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/ad41f0 |
| OpenAlex | W4395026284 |
| Language | EN |
| Citations received | 4 |
| References cited | 22 |
In the realm of weather forecasting, the implementation of Artificial Intelligence (AI) represents a transformative approach. However, AI weather forecasting method still faces challenges in accurately predicting meso- and smaller-scale processes and failing to directly capture extreme precipitation due to regression algorithm’s nature, coarse resolution, and limitations in key variables like precipitation. Therefore, we propose a state-of-the-art technology which integrates the strengths of the Pangu-weather AI weather forecasting with the traditional regional weather model, focusing specifically on enhancing the prediction of extreme precipitation events, as mainly exemplified by an unprecedented precipitation in North China from 29 July to 1 August 2023, and an additional extraordinary precipitation event as a supplementary validation to further ensure the accuracy of this technology. The results show that the AI-driven approach exhibits superior performance in capturing the spatial and temporal dynamics of extreme precipitation events. Remarkably, with a threshold of 400 mm, the AI-driven model secures a Threat Score (TS) of 0.1 for forecast lead time reaching up to 8.5 d. This performance notably surpasses the performance of traditional GFS-Driven models, which achieve a similar TS only within a limited 3-day forecast lead time. This considerable enhancement in forecast accuracy, especially over extended lead times illustrates the AI-driven model’s potential to advance in long-term forecasts of extreme precipitation, previously considered challenging, emphasizing the potential of AI in augmenting and refining traditional weather prediction
China · Climatology · Geography · Meteorology · Precipitation · Tropical cyclone forecast model · Weather forecasting · Weather Research and Forecasting Model · Climate variability and models · Environmental Science · Meteorological Phenomena and Simulations · Tropical and Extratropical Cyclones Research · Geology
Evaluating AI’s capability to reflect physical mechanisms
Impact of deep-inland typhoon track uncertainty on the 2023 record-breaking rainfall over North China
Evaluation of precipitation forecasting methods and an advanced lightweight model
Exploring the integration of a global AI model with traditional data assimilation in weather forecasting
| Unique citing works | 4 |
|---|---|
| Citations per year | 2 |
| Citation span | 2024 - 2026 (3) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 4 |