What happens when weather forecasts do not match reality? How do the public emotionally respond when a disaster unfolds differently from what they expected? A research team led by Professor Jonghun Kam and Kiru Kim from the Department of Environmental Engineering at POSTECH investigated how forecast error types influenced public emotion during the landfall of Typhoon Khanun. Using an artificial intelligence (AI), Natural Language Processing (NPL), the researchers found that different types of forecast error (e.g., over/underestimation) triggered distinct emotional responses among the public. The study has been published in GeoHealth.

Weather forecasts are essential for disaster preparedness, but they are inherently uncertain. During extreme weather events such as typhoons, forecasts may either overestimate rainfall that never occurs or underestimate rainfall that turns out to be severe. The research team focused on how these mismatches between expectation and reality affect public perceptions and emotional responses before and after the landfall of Typhoon Khanun.

The researchers assessed the Korean Meteorological Administration’s prediction skill of Typhoon Khanun, which crossed the Korean Peninsula in August 2023 against observed rainfall records from 613 weather stations. They also analyzed more than 43,000 online posts from NAVER Report Talk using an AI-based natural language processing model.

The results revealed clear spatial differences in forecast performance. Forecasts tended to underestimate heavy rainfall in the eastern and southeastern regions of the Korean Peninusla while overestimating rainfall in the western and metropolitan areas. In regions where rainfall was overestimated, anxiety, worry, and fatigue became dominant emotion types in online discourses while the public from the regions with underestimated rainfall showed a high level of certain emotion types like confusion, embarrassment, and sadness.

The researchers also examined changes in regional emotion. In the eastern and southeastern regions, where actual rainfall exceeded forecast amounts, online discussions frequently expressed confusion, uncertainty, and anxiety as Typhoon Khanun passed by the Korean Peninsula. The western and metropolitan regions, where rainfall forecasts were higher than observed rainfall, showed dominant emotions of worry and concern, followed by increasing a level of emotion types, relief and reassurance, as the typhoon passed. These findings suggest that people respond not only to the disaster itself but also to the gap between what they expected and what they actually experienced.

Overall, approximately 55% of all online discourses expressed negative emotions, with anxiety and worry being the most common. The researchers also found that information-seeking activities peaked before the typhoon’s landfall, whereas online reporting and experience sharing surged during the event itself. This indicates that the public shift from passive information consumers to active information providers as disaster impacts become more immediate.

The findings demonstrate that forecast accuracy is not only a technical issue but also an important factor influencing public perception and emotional well-being. More importantly, the study highlights that the mismatch between anticipated risk and experienced reality plays a critical role in shaping public emotions and their perceived risk during disasters. The researchers suggest that communicating forecast uncertainty more effectively could improve public trust and reduce emotional distress during future extreme weather events.

Kiru Kim, the lead author, noted, “This study demonstrates that in disaster situations, it is important not only to improve forecast accuracy but also to develop risk communication strategies that effectively convey uncertainty to the public.”

Professor Jonghun Kam said, “This study demonstrates how AI can be used to analyze large-scale public discourse and monitor the emotional impacts of forecast errors. The findings provide new insights into how to develop effective risk communication strategies during landfalling typhoons and other natural disasters.”

Journal

GeoHealth

DOI

10.1029/2026GH001837