1. Introduction and Overview of Course

2. Chemical, Aerosol, Removal modules
Air Quality Modeling: Improving Predictions of Air Quality (analysis and forecasting perspectives)
Met model
Predicted Quantity: e.g., ozone AQ violation
Chemical, Aerosol, Removal modules
CTM
How confident are we in the models & predictions?
Models can do this…. You find that you can attribute a certain percentage to emissions w/in the region…. But the remainder is outside the region via BCs… Also the analysis state itself depends upon the assumed bCs…CTMs ALL with Uncertainties. They incorporate chemical models, aerosol models, radiation. They use information from meteorological simulations like wind and temperature fields, turbulent diffusion parameterizations, etc. They also use information from emission inventories to produce “chemical weather forecasts”, i.e. forecasts of the distribution of chemical pollutants in the atmosphere.
Yellow arrows – data flow for simulation using the first principles
Another source of information for concentrations of pollutants in the atmosphere are observations – ground stations, ozone balloons, plane flights, satellite observations. Observation data can be archived or on-line. Data assimilation combines these 2 sources of information to produce …
Pink arrows – show data flow for dynamic feedback and control loop from measurements/data assimilation to simulation
Optimal analysis state = Recognizing that both the model forecast and observations are corrupted by uncertainties, data assimilation produces a best estimate of the state of the atmosphere. This state is consistent with both the physical/chemical laws of evolution through the model (first principles) and with reality through measurement information.
Targeted observations means: design of observational network, by locating the observations in space and time such that the uncertainty in predictions is minimized (here we use the simulation to control the measurement process)
Emissions
Observations
Section 3 – Introduction and Overview of Course

3. Air Quality Forecasting
Chemical
Section 3 – Introduction and Overview of Course

4. AQ Modeling Is A System Many Meteorological Services Already Supply Operational Chemical Weather Products (e.g., FMI)
Fire Assimilation
System
Satellite
observations
Phenological
observations
Final AQ products
Phenological
models
SILAM
AQ model
Aerobiological
observations
EVALUATION:
NRT model-measurement
comparison
Physiography,
forest mapping
Assimilation
GURME to learn and share best practices…..
HIRLAM
NWP model
UN-ECE CLRTAP/EMEP
emission database
Aerobiological
observations
Online AQ
monitoring
Meteorological
data: ECMWF
Real-time data needs!
Section 3 – Introduction and Overview of Course

5. Section 3 – Introduction and Overview of Course
Audience for Course
Decision makers
Overview of air quality analysis & forecasting
Uses of air quality models and general steps to develop/improve an air quality modeling program
Meteorologists and forecasters
Overview of air quality emissions and chemistry
Discussion of tools used to forecast air quality
Air quality scientists
Introduction to varies tools and techniques in air quality analysis
Section 3 – Introduction and Overview of Course

6. Section 3 – Introduction and Overview of Course
Day-by-Day Guide
Monday
AQ fundamentals
Tuesday
Meteorological Modeling – WRF
Wednesday
WRF/Chem
Thursday
Friday -applications
Forecasting, evaluation, impacts, management
Section 3 – Introduction and Overview of Course

7. Section 3 – Introduction and Overview of Course
Summary of Course – Introduction to Air Quality Modeling
Section 3 – Introduction and Overview of Course

8. Section 3 – Introduction and Overview of Course