Data Assimilation for Air-Quality Forecasting

In FY2019, MMM scientists worked in collaboration with colleagues at the Ulsan National Institute of Science and Technology (UNIST) to assimilate surface aerosol observations and satellite aerosol optical depth (AOD) retrievals in NCAR’s community WRF-Chemistry model and the three-dimensional variational data assimilation (3DVAR) technique in order to improve air-quality prediction. This project is sponsored by the National Institute of Environmental Research (NIER) in Korea for a possible replacement of their current operational air quality forecasting system, the Community Multiscale Air Quality (CMAQ) chemistry transport model, with the more advanced analysis and online coupled forecasting system between meteorology and chemistry.

Results of this research show that most air pollutants are very sensitive to initial errors (partly due to their short lifetime), and even with large uncertainties in chemical modeling and emission data, better initial conditions thru data assimilation can lead to improving air quality forecasts, particularly in heavy pollution events.

24-h forecast from WRF-Chem/#DVAR analysis

This project is being used to extend NCAR/MMM international collaboration to improve our own WRF-Chemistry model for various chemistry and aerosol options. In the long term, NCAR scientists want to utilize this opportunity to develop a chemistry model coupled to the Model for Prediction Across Scales (MPAS), our next-generation community atmospheric model at NCAR.