Alibaba Group Holding Ltd.’s (NYSE: BABA) research firm DAMO Academy has launched an artificial intelligence (AI) weather forecasting model with enhanced forecasting accuracy. Dubbed ‘Baguan,’ the AI-powered weather forecasting model can predict weather conditions for up to 10 days with hourly updates and much higher accuracy compared to current weather forecasting tools.
Industries will need tools such as Baguan to limit their impact on the environment and adapt to climate change-induced volatility. DAMO Academy developed the AI weather forecasting tool to increase meteorological precision amidst a surge in extreme climate events.
Several regions across the world are contending with extreme fires, droughts, landslides, tropical storms and floods that have taken many lives and caused significant economic damage. With the effects of climate change becoming more apparent each day, increased accuracy in meteorology will allow experts to predict such events with greater accuracy and, hopefully, help them save more lives.
According to the World Meteorological Organization (WMO), the effects of severe weather and climate change risk the planet’s and humanity’s wellbeing. Leveraging artificial intelligence and machine learning could help alleviate these risks by increasing the accuracy and speed of weather modeling tools. AI and machine learning can allow lower-income nations to access such tools by making them less costly.
Alibaba DAMO Academy Director of Decision Intelligence Lab Wotao Yin says Baguan DAMO can help improve climate science while benefiting eco-friendly practices in a wide array of industries. DAMO Academy developed the AI weather forecasting model using a new autoregressive pre-training methodology SiamMAE (Siamese Masked Auto encoders) architecture.
The model can process and understand atmospheric data at a very high level, allowing for significantly more precise weather forecast predictions. These predictions can help the energy industry optimize its production, especially renewables like wind and solar, and help the sector stabilize and optimize its power management.
Baguan demonstrated its enhanced precision during the recent Shandong Province, China temperature drop where it accurately predicted that there would be a 20% fall in electricity demand. The accurate prediction helped to reduce costs, improved the efficiency of energy distribution post-temperature drop, and boosted grid operation. Used on a much larger scale, Baguan and other similar tools can have major benefits for the grid and energy customers.
Yin noted that DAMO Academy would continue working to improve the tool’s ability to predict critical weather indicators like precipitation and cloud cover, develop novel tools to analyze different climate scenarios and support additional applications such as sports events preparations, agricultural production, and civil aviation meteorological warnings.
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