The review of major 3-D global and

The review of major 3-D global and regional real-time air quality forecasting (RT-AQF) models in Part I
identifies several areas of improvement in meteorological forecasts, chemical inputs, and model treatments
of atmospheric physical, dynamic, and chemical processes. Part II highlights several recent
scientific advances in some of these areas that can be incorporated into RT-AQF models to address model
deficiencies and improve forecast accuracies. Current major numerical, statistical, and computational
techniques to improve forecasting skills are assessed. These include bias adjustment techniques to
correct biases in forecast products, chemical data assimilation techniques for improving chemical initial
and boundary conditions as well as emissions, and ensemble forecasting approaches to quantify the
uncertainties of the forecasts. Several case applications of current 3-D RT-AQF models with the state-ofthe-
science model treatments, a detailed urban process module, and an advanced combined ensemble/
data assimilation technique are presented to illustrate current model skills and capabilities. Major
technical challenges and research priorities are provided. A new generation of comprehensive RT-AQF
model systems, to emerge in the coming decades, will be based on state-of-the-science 3-D RT-AQF
models, supplemented with efficient data assimilation techniques and sophisticated statistical models,
and supported with modern numerical/computational technologies and a suite of real-time observational
data from all platforms.

Results (English) 3:

review of 3 major The - D global and regional real-time air quality forecasting (RT - AQF) models in Part I
identifies several areas of improvement in meteorological forecasts, chemical inputs, and model of atmospheric treatments
physical, dynamic, and chemical processes. Part II highlights several recent
scientific advances in some of these areas that can be incorporated into RT - AQF models to address deficiencies and improve forecast Model
accuracies. Current major numerical, statistical, and computational techniques to improve
forecasting skills are assessed. These include bias adjustment techniques to correct biases in
forecast products,chemical data assimilation techniques for improving chemical boundary conditions and initial
as well as emissions, and ensemble forecasting approaches to quantify the uncertainties of
the forecasts. Several case applications of current 3-D RT - AQF models with the state-ofthe-science model
treatments, a detailed urban process module, and an advanced combined ensemble/
Data assimilation technique are presented to illustrate current model skills and capabilities. Major
technical challenges and research priorities are provided. A new generation of comprehensive RT - AQF
model systems, to emerge in the coming decades, will be based on state-of-the-science 3 - D RT - AQF
models,supplemented with efficient data assimilation techniques and sophisticated statistical models,
and supported with modern numerical/computational technologies and a suite of real-time observational
data from all platforms.