Development of a smoke dispersion forecast system for Korean forest fires

Boknam Lee, Seungwan Cho, Seung Kii Lee, Choongshik Woo, Joowon Park

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


Smoke from forest fires is a growing concern in Korea as forest structures have changed and become more vulnerable to fires associated with climate change. In this study, we developed a Korean forest fire smoke dispersion prediction (KFSDP) system to support smoke management in Korea. The KFSDP system integrates modules from different models, including a Korean forest fire growth prediction model, grid-based geographic information system (GIS) fuel loading and consumption maps generated by national forest fuel inventory data, and the KoreanWeather Research and Forecasting Model, into a Gaussian plume model to simulate local- and regional-scale smoke dispersion. The forecast system is operated using grid-based fires and simulates a cumulative smoke dispersion of carbon monoxide (CO) and <2.5 μm and <10 μm particulate matter (PM2.5 and PM10, respectively) ground-level concentration contours at 30-min intervals during the fire in concert with weather forecasts. The simulated smoke dispersions were evaluated and agreed well with observed smoke spreads obtained from real forest fires in Korea, and the performance of the KFSDP system was also analyzed using "what-if" scenarios. This is the first study to develop an integrated model for predicting smoke dispersion from forest fires in Korea.

Original languageEnglish
Article number219
Issue number3
StatePublished - 2019


  • Forecast system
  • Forest fire
  • Gaussian plume model
  • Smoke dispersion


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