Short- to Medium-Term Weather Forecast Skill of the AI-Based Pangu-Weather Model Using Automatic Weather Stations in China
Pangu is an AI-based model designed for rapid and accurate numerical weather forecasting. To evaluate Pangu’s short- to medium-term weather forecasting skill over various meteorological parameters, this paper validated its performance in predicting temperature, wind speed, wind direction, and barome...
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MDPI AG
2025-01-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/2/191 |
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| author | Siyi Xu Yize Zhang Junping Chen Yunlong Zhang |
| author_facet | Siyi Xu Yize Zhang Junping Chen Yunlong Zhang |
| author_sort | Siyi Xu |
| collection | DOAJ |
| description | Pangu is an AI-based model designed for rapid and accurate numerical weather forecasting. To evaluate Pangu’s short- to medium-term weather forecasting skill over various meteorological parameters, this paper validated its performance in predicting temperature, wind speed, wind direction, and barometric pressure using data from over 2000 weather stations in China. Pangu’s performance was compared with ECMWF-HRES and GFS to assess its effectiveness relative to traditional high-precision NWP models under real meteorological conditions. Furthermore, the more recent FuXi and FengWu models were included in the analysis to further validate Pangu’s forecasting skill. The study examined Pangu’s forecast performance from spatial perspectives, evaluated the dispersion of forecast deviations, and analyzed its performance at different lead times and with various initial fields. The iteration precision of Pangu’s four forecast models with lead times of 1 h, 3 h, 6 h, and 24 h was also assessed. Finally, a case study on typhoon track forecasting was conducted to evaluate Pangu’s performance in predicting typhoon paths. The results indicate that Pangu surpasses traditional NWP systems in temperature forecasting, while its performance in predicting wind direction, wind speed and pressure is comparable to them. Additionally, the forecast skill of Pangu diminishes as the lead time extends, but it tends to surpass traditional NWP systems with longer lead times. Moreover, FuXi and FengWu demonstrate even higher accuracy compared to Pangu. Pangu’s performance is also dependent on initial fields, and the temperature forecasting of Pangu is more sensitive to the initial field compared with other meteorological parameters. Furthermore, the iteration precision of Pangu’s 1 h forecast model is significantly lower than that of the other models, but this discrepancy in precision may not be prominently reflected in Pangu’s actual forecasting process due to the greedy algorithm employed. In the case study on typhoon forecasting, Pangu, along with FuXi and FengWu, demonstrates comparable performance in predicting Bebinca’s track compared to ECMWF and outperforms GFS in its track predictions. This study demonstrated Pangu’s applicability in short- to medium-term forecasting of meteorological parameters, showcasing the significant potential of AI-based numerical weather models in enhancing forecast performance. |
| format | Article |
| id | doaj-art-f52b44e94cc740c3bf1af2b9d29610ab |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-f52b44e94cc740c3bf1af2b9d29610ab2025-01-24T13:47:40ZengMDPI AGRemote Sensing2072-42922025-01-0117219110.3390/rs17020191Short- to Medium-Term Weather Forecast Skill of the AI-Based Pangu-Weather Model Using Automatic Weather Stations in ChinaSiyi Xu0Yize Zhang1Junping Chen2Yunlong Zhang3Shanghai Astronomical Observatory, Chinese Academy of Sciences, Shanghai 200030, ChinaShanghai Astronomical Observatory, Chinese Academy of Sciences, Shanghai 200030, ChinaShanghai Astronomical Observatory, Chinese Academy of Sciences, Shanghai 200030, ChinaChina Railway Design Corporation, Tianjin 300308, ChinaPangu is an AI-based model designed for rapid and accurate numerical weather forecasting. To evaluate Pangu’s short- to medium-term weather forecasting skill over various meteorological parameters, this paper validated its performance in predicting temperature, wind speed, wind direction, and barometric pressure using data from over 2000 weather stations in China. Pangu’s performance was compared with ECMWF-HRES and GFS to assess its effectiveness relative to traditional high-precision NWP models under real meteorological conditions. Furthermore, the more recent FuXi and FengWu models were included in the analysis to further validate Pangu’s forecasting skill. The study examined Pangu’s forecast performance from spatial perspectives, evaluated the dispersion of forecast deviations, and analyzed its performance at different lead times and with various initial fields. The iteration precision of Pangu’s four forecast models with lead times of 1 h, 3 h, 6 h, and 24 h was also assessed. Finally, a case study on typhoon track forecasting was conducted to evaluate Pangu’s performance in predicting typhoon paths. The results indicate that Pangu surpasses traditional NWP systems in temperature forecasting, while its performance in predicting wind direction, wind speed and pressure is comparable to them. Additionally, the forecast skill of Pangu diminishes as the lead time extends, but it tends to surpass traditional NWP systems with longer lead times. Moreover, FuXi and FengWu demonstrate even higher accuracy compared to Pangu. Pangu’s performance is also dependent on initial fields, and the temperature forecasting of Pangu is more sensitive to the initial field compared with other meteorological parameters. Furthermore, the iteration precision of Pangu’s 1 h forecast model is significantly lower than that of the other models, but this discrepancy in precision may not be prominently reflected in Pangu’s actual forecasting process due to the greedy algorithm employed. In the case study on typhoon forecasting, Pangu, along with FuXi and FengWu, demonstrates comparable performance in predicting Bebinca’s track compared to ECMWF and outperforms GFS in its track predictions. This study demonstrated Pangu’s applicability in short- to medium-term forecasting of meteorological parameters, showcasing the significant potential of AI-based numerical weather models in enhancing forecast performance.https://www.mdpi.com/2072-4292/17/2/191numerical weather predictiondeep learningweather forecastprediction skill |
| spellingShingle | Siyi Xu Yize Zhang Junping Chen Yunlong Zhang Short- to Medium-Term Weather Forecast Skill of the AI-Based Pangu-Weather Model Using Automatic Weather Stations in China Remote Sensing numerical weather prediction deep learning weather forecast prediction skill |
| title | Short- to Medium-Term Weather Forecast Skill of the AI-Based Pangu-Weather Model Using Automatic Weather Stations in China |
| title_full | Short- to Medium-Term Weather Forecast Skill of the AI-Based Pangu-Weather Model Using Automatic Weather Stations in China |
| title_fullStr | Short- to Medium-Term Weather Forecast Skill of the AI-Based Pangu-Weather Model Using Automatic Weather Stations in China |
| title_full_unstemmed | Short- to Medium-Term Weather Forecast Skill of the AI-Based Pangu-Weather Model Using Automatic Weather Stations in China |
| title_short | Short- to Medium-Term Weather Forecast Skill of the AI-Based Pangu-Weather Model Using Automatic Weather Stations in China |
| title_sort | short to medium term weather forecast skill of the ai based pangu weather model using automatic weather stations in china |
| topic | numerical weather prediction deep learning weather forecast prediction skill |
| url | https://www.mdpi.com/2072-4292/17/2/191 |
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