Vietnam’s forecasting infrastructure has evolved substantially in recent years. Advanced numerical weather prediction models running on CRAY supercomputers can now produce high-resolution forecasts on 3km by 3km grids, enabling more localized weather analysis than ever before. This technological leap allows forecasters to better monitor storms, rainfall intensity, and severe weather patterns at regional and provincial scales. Combined with integrated data from satellites, radar, automated weather stations, and lightning detection systems, Vietnam has developed an increasingly sophisticated real-time weather surveillance network.

This integration has dramatically improved early warnings for dangerous phenomena such as thunderstorms, tornadoes, hail, and localized extreme rainfall. In many cases, warning lead times now range from 30 minutes to three hours, offering communities more time to prepare and reduce losses. For flood forecasting, river basin models—especially those linked to the Mekong system and international hydrological data—have also extended warning windows by several days in some scenarios.

Artificial intelligence is becoming one of the sector’s most transformative tools. AI systems can now automatically identify storm positions, estimate cyclone intensity, compare current weather systems with historical analogues, and improve forecast outputs through machine learning. In short-term storm intensity prediction, AI has already contributed to forecast improvements of approximately 10–20 percent. AI is also increasingly applied to flash flood, saltwater intrusion, and landslide risk analysis.

However, important limitations remain. Forecasting storm intensity, particularly sudden strengthening over the East Sea, continues to present major technical challenges. Heavy rainfall prediction accuracy also remains relatively low, especially for large-volume rain events beyond 50mm per day. Similarly, highly localized flash floods and landslides are still difficult to predict precisely due to insufficient geological and terrain monitoring data.

To address these gaps, experts emphasize four strategic pillars: mastering core forecasting technologies such as AI and ensemble modeling; expanding smart real-time observation networks; strengthening Big Data processing systems; and improving public communication channels. The goal is not only better science but also faster, clearer delivery of warnings to citizens through mobile platforms, digital applications, and multi-channel communication systems.

Human expertise remains essential. While AI functions as a powerful “virtual assistant,” experienced meteorologists are still crucial for interpreting anomalies, making judgment calls, and managing unprecedented emergencies.

Ultimately, disaster resilience depends not only on predicting hazards but on transforming forecasts into actionable protection. Vietnam’s future success will require combining scientific innovation, institutional reform, infrastructure investment, and public accessibility.

In an era of escalating climate threats, forecasting is no longer simply about weather—it is about national security, economic stability, and saving lives. By continuing to strengthen technology and preparedness, Vietnam is laying the foundation for a smarter, safer, and more climate-resilient future.