Alibaba DAMO Academy, the research and development arm of Alibaba Group, recently announced the official launch of “Baguan” weather forecasting model.

Named after the Chinese concept of “observing from different perspectives,” Baguan harnesses cutting-edge AI to revolutionize weather prediction capabilities.

Baguan offers unprecedented accuracy in weather forecasts, ranging from one hour to ten days ahead. The machine-learning model stands out with its high spatial resolution, delivering detailed meteorological predictions down to a 1 x 1 kilometer grid, updated hourly.

These capabilities make Baguan an essential tool for applications in climate science, electricity load forecast, renewable energy forecast and natural disaster prevention. 



The technical backbone of Baguan is its innovative use of the Siamese Masked Autoencoders (SiamMAE) structure and a robust pre-training methodology. These innovations empower the model to uncover intricate patterns gleaned from complex dynamic atmospheric data.

Furthermore, through an autoregressive pre-training approach, Baguan is able to make precise predictions across various spatio-temporal scales, from one hour to 10 days in advance.

Baguan leverages ERA5, the European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis of the global weather from 1979 to present, to construct the foundational model for weather forecasting.

Baguan is further refined with key regional meteorological indicators such as regional temperature, irradiance, and wind speed. This meticulous global-regional modeling approach not only boosts Baguan’s forecasting accuracy down to regional level but also tailors its insights to specific local conditions.



With the surging global demand for renewable energy, Baguan’s precise weather predictions have become vitally important. The model significantly enhances the reliability of renewable energy forecasts, facilitating more stable power management and supporting the expansion of green energy consumption. 

Baguan’s capability in weather forecasting has already been used in the power and energy sectors in China, supporting critical applications such as electricity load forecasting and renewable energy forecasting.

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