Forecasting the unpredictable: where human insight still matters

According to Mai Van Khiem, Director of the National Center for Hydro-Meteorological Forecasting, the creation of weather, hydrological, and marine forecasts is a highly structured process governed by a standardized eight-step professional procedure. This rigorous sequence involves everything from data collection and the analysis of current conditions to the development of specific warning scenarios and the final evaluation of forecast quality. Whether operating under normal or hazardous conditions, the goal remains the same: providing a reliable social product that protects lives and property.

Despite these protocols, the inherent chaos of the atmosphere means that forecasting models always carry a degree of uncertainty. Nguyen Van Huong, Head of Weather Forecasting, notes that experts do not simply rely on a single automated model. Instead, they must combine vast data sets with practical observation and years of experience. This human element is especially critical when dealing with localized phenomena like heavy rain, flash floods, or landslides, which develop rapidly across small areas and offer very little lead time for early warnings.

The complexity of the natural landscape further complicates these predictions. Predicting a landslide, for instance, requires precise data on a specific mountain's soil saturation, vegetation, and slope gradient—variables that change from one location to the next. Because of these challenges, current warnings for flash floods and landslides are typically issued as risk levels—such as "high" or "moderate"—rather than pinpoint mathematical certainties. This requires forecasters to act as risk communicators, helping authorities make high-stakes decisions regarding evacuations and infrastructure safety.

In recent years, the National Center has integrated Artificial Intelligence as a "digital assistant" to streamline technical tasks and reduce human error. While AI excels at processing massive volumes of satellite and radar data to identify patterns, experts believe it is unlikely to replace humans in the foreseeable future. AI systems rely heavily on historical data, which can be an unreliable guide as climate change produces unprecedented and "non-typical" extreme weather events that fall outside of recorded history.

Ultimately,