ENSEMBLE PREDICTION OF VOLCANIC ASH DISPERSION USING PYTHON-FALL3D MODEL (CASE STUDY: MT. AGUNG) - Permatasari Pratami, Wulan
Volcanic eruption can cause a variety impacts on various fields, especially in the environment, health, aviation and economics. To anticipate the impacts, we can using prediction of volcanic ash dispersion. The deterministic prediction is considered not good enough to predict the dispersion of volcanic ash. This is caused by the prediction model has uncertainties such as wind direction, stability, precipitation, wind speed and mix layer depth. Therefore, in this study prediction ensemble of volcanic ash dispersion was performed using Python-FALL3D model. Python-FALL3D requires a set of wind field data as an input. Wind field datasets which derived from National Center for Environmental Prediction – Global Forecast System (NCEP-GFS) was used. Before wind field datasets used in Python-FALL3D, wind field datasets will be processed in Weather Forecast System (WRF). The predicted dispersion of airbone volcanic ash is compared to satellite images of Himawari-8. Member ensemble is formed by combining different parameterization scheme. The method of verification is based on dispersion angle calculation (?) and dispersion area ratio of volcanic ash. The results showed that ensemble prediction using Python-FALL3D can produce a better direction of ash dispersion than deterministic prediction.