Real-time Analytics
Chongqing needed more accurate weather forecasting and faster warning delivery across a large observation network, but conventional numerical forecasting and fragmented infrastructure were limiting the bureau's ability to process data and respond to severe weather quickly.
Reported impact
Chongqing improved forecast precision, accelerated warning delivery, and created a cloud-based weather-intelligence backbone that supports both emergency response and domain-specific public services.
Baidu AI Cloud helped the Chongqing Meteorological Bureau build a cloud-and-AI meteorological platform that links data ingestion, model training, alerting, and public-service delivery. At the data layer, the Tianzhu big-data platform and meteorological cloud-resource center aggregate observations from satellites, Doppler radars, and thousands of weather stations. On top of that base, PaddlePaddle-powered deep-learning workflows support the Tianzi intelligent forecasting system and related severe-weather models, while Yutian warning services connect hazard signals to responsibility networks and mass-message delivery. The same stack also supports domain services such as agriculture, tourism, and water conservancy through the Zhitian service layer. Instead of treating forecasting, warning, and sector services as separate systems, the bureau now uses one cloud foundation where data collection, AI prediction, emergency communication, and industry-facing meteorological services reinforce each other.
Observation data from satellites, radars, and weather stations enters the Tianzhu platform and cloud resource center, where deep-learning models are trained through PaddlePaddle. Forecasting outputs feed Tianzi prediction services and Yutian warning workflows, which then distribute alerts or domain services to agricultural, tourism, water-management, and public-safety stakeholders.
Deep learning improved monthly temperature forecasts by 16 points and precipitation forecasts by 6 points, while hail and gale recognition reached 100% in severe events (original: 月气温预测准确率提高了16分，月降水预测准确率提高了6分…冰雹、大风识别命中率达到100%).
The Tianzhu platform connects 8 satellites, 4 local Doppler radars, 2,000+ local stations, plus 10 shared radars and nearly 10,000 shared stations (original: 对接8颗观测卫星、自建4部多普勒雷达和2000多个地面气象观测站、与周边省份气象部门共享10部多普勒雷达和近万个地面观测站的数据).
The bureau built an internationally leading meteorological AI deep-learning platform with Baidu AI Cloud (original: 重庆市气象局联手百度智能云打造国际领先的气象AI深度学习平台).
Warning delivery speed increased from 160 messages per second to 3,000 per second, reducing 1.6 million-recipient notification time from over 2 hours to about 9 minutes (original: 可以将短信发布速率从每秒160条提升到每秒3000条…将预警信息发送给160万预警责任人，以前要2个多小时，现在只需要9分钟左右).
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Scoring methodology
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Traceability
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