Details zur Publikation |
| Kategorie | Textpublikation |
| Referenztyp | Zeitschriften |
| DOI | 10.1029/2026JH001291 |
Lizenz  |
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| Titel (primär) | Short-term hourly weather forecasting using PredRNN with image preprocessing |
| Autor | Tran, H.; Li, H.; Tran, V.N.; Nguyen, V.T.
; Le, M.-H.; Dang, T.D.; Do, H.X.; Pham, H.T.; Bui-Thanh, T.; Leung, L.R. |
| Quelle | Journal of Geophysical Research-Machine Learning and Computation |
| Erscheinungsjahr | 2026 |
| Department | HDG; MIBITECH |
| Band/Volume | 3 |
| Heft | 4 |
| Seite von | e2026JH001291 |
| Sprache | englisch |
| Topic | T5 Future Landscapes |
| Daten-/Softwarelinks | https://doi.org/10.5281/zenodo.20621688 |
| Keywords | weather forecast; deep learning; image processing |
| Abstract |
Global weather forecast models are vital tools with
numerous applications, including public safety, agriculture, and
transportation. Recent advancements in artificial intelligence (AI) and
deep learning (DL) have shown the potential to enhance weather
forecasting accuracy and speed. In this study, we developed a short-term
hourly weather forecast framework with a wavelet transform function for
data preprocessing and a spatiotemporal DL model, PredRNN, for
predicting five surface atmospheric variables, including wind speed and
direction, mean sea level pressure (MSLP), temperature, and
precipitation. The framework demonstrated promising results. It produces
global forecasts at 0.25° (∼25 km) with a 1-day lead time RMSE of
1.8 m/s for wind components, 180 Pa for MSLP, and 1.8 K for temperature.
Although our model does not surpass state-of-the-art AI weather
forecast models across all metrics, it outperforms these models in
precipitation forecasting and wind prediction at short lead times and
achieves comparable accuracy for MSLP. Its native hourly forecasting
capability, together with training on widely accessible GPU hardware,
contributes meaningfully to the advancement of accessible DL weather
forecasting methods. Our work highlights the importance of integrating
temporal components and data transformation techniques to improve the
predictability and accuracy of weather forecasts. |
| Tran, H., Li, H., Tran, V.N., Nguyen, V.T., Le, M.-H., Dang, T.D., Do, H.X., Pham, H.T., Bui-Thanh, T., Leung, L.R. (2026): Short-term hourly weather forecasting using PredRNN with image preprocessing J. Geophys. Res.: Mach. Learn. Comput. 3 (4), e2026JH001291 10.1029/2026JH001291 |
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