SIMULATION OF FOREST FIRE SMOKE DISPERSION USING THE WRF-CHEM MODEL AND FINNV2.5 INPUT DATA IN SUMATERA ISLAND IN 2015

• Main Author: Ramadhani, Sovi
• Format: Final Project
• Language: Indonesia
• Subjects: Geologi, hidrologi & meteorologi
• Online Access: https://digilib.itb.ac.id/gdl/view/75743
• Institution: Institut Teknologi Bandung

In 2015, a massive forest fire occurred from August to November, causing environmental disasters in Indonesia and neighboring countries. The forest fire released aerosol particles that could cause environmental damage and have adverse effects on human health. Additionally, the forest fire emitted CO, which could disrupt human health. Therefore, a simulation of the forest fire smoke dispersion was conducted in Sumatra Island in 2015 to predict the direction of the forest fire smoke dispersion and compare the performance of the WRF-Chem model to the FINNv2.5 and FINNv1.5 inventories. This study utilized a numerical weather prediction model combined with chemical parameters, namely the Weather Research Forecasting-Chemistry (WRF-Chem) model. This model enabled the simulation of forest fire emissions and the transportation of the forest fire smoke. The initial and boundary data used in this study were the Global Forecast System (GFS) for the global atmospheric model. Additionally, the FINNv2.5 inventory was used as the input data for forest fire emissions. The results of this study showed that the direction of the forest fire smoke dispersion in Sumatra Island was similar to the movement direction of fire clouds with smoke observed by the Himawari-8 satellite. The simulated dispersion of the forest fire smoke by the WRF-Chem model moved towards the northwest, northeast, and east-northeast, causing the forest fire smoke to spread as far as Singapore. The comparison of the performance of the WRF-Chem model to the FINNv2.5 and FINNv1.5 inventories revealed that the FINNv2.5 inventory was still less consistent in reducing biases, because the ???????????????? values of WRF-FINNv2.5 for relative humidity and wind direction variables are overestimated, and for temperature and wind speed variables, they are underestimated compared to WRF-FINNv1.5.