Ponente
Descripción
Wildfires are important emission sources that generate large amounts of aerosols into the atmosphere. These hazardous events have been increasing rapidly due to the climate change effects, leading to poor air quality, which causes impacts on the society, including adverse health effects, life and property losses, and the economic burden. To mitigate these effects, many regional and global numerical models have been developed and used by state and local agencies to study and predict the dispersion of aerosols to protect the public from poor air quality. However, the accuracy of these forecast models is dominantly affected by uncertainties in errors in emission and meteorological input data as well as model simulation. Therefore, in addition to individual models, ensemble forecast is increasingly being used to reduce model uncertainties and improve model forecasting performance. This study aims to develop a multi-model ensemble forecast of wildfires using models from different institutes, including the three National Air Quality Forecast Capability (NAQFC) regional models: GMU-CMAQ, NACC-CMAQ, and HYSPLIT, and the three International Cooperative for Aerosol Prediction (ICAP) global models: GEOS-5, GEFS-Aerosol, and NAAPS. Aerosol optical depth (AOD) and particulate matter less than 2.5 µm in diameter (PM2.5) forecasting performances of individual models, and ensemble mean were evaluated by analysing statistical metrics using the ground observations (AirNOW) and satellite data sets (MAIAC and VIIRS) for the 2020 Giga fire period (August-September 2020) over the Continental United States (CONUS). The ensemble then will be used to improve the real-time wildfire forecasting system over North America to support the key-decision making processes for the air quality at local and national levels.
