Core Insights - The "Smart Warning" intelligent early warning collaborative system has been successfully established using WeChat Work as a communication platform, covering meteorological departments at national, provincial, municipal, and county levels with nearly 3,000 meteorological workers participating [1][6]. Group 1: System Overview - The "Smart Warning" system is the first nationwide flat intelligent early warning platform that connects all four administrative levels, significantly enhancing meteorological warning collaboration [1]. - The system allows for AI digital warnings to be issued from the National Meteorological Center, guiding local forecasters in issuing weather alerts, thus providing strong support for efficient collaboration in meteorological warning operations [1][6]. Group 2: Operational Efficiency - Traditional meteorological work relied heavily on fixed platforms and phone/video consultations, which limited timeliness and responsiveness. The new system enables minute-level targeted push notifications to local areas, improving the speed and accuracy of weather alerts [4]. - Since its launch, the "Smart Warning" system has established a collaborative network covering 800 meteorological departments across all four levels, ensuring 24/7 real-time access to digital warnings via WeChat Work [6]. Group 3: Real-World Impact - The system has proven effective during severe weather events, such as the recent typhoons, providing critical support for disaster prevention decisions. For instance, during the "Weipa" typhoon, timely warnings were issued that accurately predicted heavy rainfall, allowing for prompt action [7]. - As of now, the system has issued a total of 47,379 AI warning signals, 5,930 automatic station alarms, and 13,501 reminders for flood risk points, marking a shift from passive querying to proactive notifications [9]. Group 4: Future Developments - There is still room for improvement in forecasting accuracy and response mechanisms for severe convective weather warnings. The development team plans to continue optimizing algorithm models and enhancing warning technology capabilities [8].