Introduction and Overview of Course

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

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

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

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

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

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

Section 3 – Introduction and Overview of Course