Predicting Air Quality: Current Status and Future Directions

Abstract

Air quality prediction plays an important role in the management of our environment. Computational power and efficiencies have advanced to the point where chemical transport models can predict pollution in an urban air shed with spatial resolution less than a kilometer, and cover the globe with a horizontal resolution of less than 50 km. Predicting air quality remains a challenge due to the complexity of the governing processes and the strong coupling across scales. While air quality prediction is closely aligned with weather prediction, there are important differences, including the role of pollution emissions and their associated large uncertainties. As more atmospheric chemical observations become available chemical data assimilation is expected to play an essential role in air quality forecasting. In this paper the current status of air quality forecasting is discussed and illustrated by comparison of predictions with observations. The future directions are also discussed, with an emphasis on data assimilation. Applications of the four dimensional variational method (4D-Var) and the ensemble Kalman filter (EnKF) approach are presented and discussed.

Numerical modeling of Asian dust emission and transport with adjoint inversion using LIDAR network observations

“…The third term is a smoothing term to avoid unrealistic horizontal jumps of the control parameters (Carmichael et al, 2007). For our study, ε is defined as the daily constant at each model grid.…”