Work

Incorporating Short-Term Forecast Mean Winds and NWP Maximum Gusts into Effective Wind Speed for Extreme Weather-Aware Wildfire Spread Prediction

Year: 2026

Type: article

Abstract: Because wildfire spread is strongly influenced by instantaneous gusts, models that use only mean wind speed typically underestimate spread. In contrast, incorporating suppression effects often leads to overestimation. To reduce these errors, this paper newly proposes the concepts of an effective wind speed (EWS) and an EWS coefficient that jointly account for short-range forecast mean wind speed and the maximum gust from numerical weather prediction. The EWS is defined as an EWS coefficient-weighted average of the mean wind speed and maximum gust, so that the simulated perimeter matches the observed wildfire perimeter as closely as possible. Here, EWS refers exclusively to near-surface horizontal wind speed; vertical wind components are not considered. The EWS coefficient is modeled as a function of elapsed time since ignition, thereby implicitly reflecting the level of suppression resource deployment. The proposed frameworks are described in detail using time-stamped perimeters from multiple large-scale wildfires that occurred concurrently in South Korea during a specific period. On this basis, an EWS coefficient suitable for operational use in South Korea is derived. Using the derived EWS for spread prediction, the Sørensen index increased by up to 0.4 compared with predictions based on maximum gust alone. Incorporating the proposed EWS and coefficient into Korean wildfire spread simulators can improve the accuracy and robustness of predictions under extreme weather conditions, supporting safer and more efficient wildfire response.

Source: Fire

DOI: 10.3390/fire9010031

Authors: Seungmin Yoo, Sohyun Lee, Chungeun Kwon, Sungeun Cha

Institution: National Institute of Forestry Science

Language: English

Topic: Fire effects on ecosystems

Subfield: Global and Planetary Change

Field: Environmental Science

Domain: Physical Sciences

SDG: Climate action

Open Access status: gold

Estimated APC paid: $2,896