Meteorology and Air Pollution: as a joint problem

1. Meteorology and Air Pollution:as a joint problem Meteorology is a main source of uncertainty in APMs => needs for NWP model improvements Complex & combined effects of meteo- and pollution components (e.g., Paris, Summer 2003) Effects of pollutants/aerosols on meteo&climate (precipitation, thunderstorms, etc) Three main stones for Atmospheric Environment modelling: Meteorology / ABL, Chemistry, =>Integrated Approach Aerosol/pollutant dynamics(“chemical weather forecasting”) Effects and Feedbacks ???

2. Luftforureningsmodellering ved DMI Modellering i byområder er aktuel til de fleste formål, og det er vigtigt at inddrage effekter heraf i NWP og forureningsmodellerne. Ligeledes er aerosoldannelse og -dynamik et fokusområde med betydning for helbred og sigtbarhed samt effekter på meteorologi og klima.

3. WG2 Integrated systems of MetM and CTM: strategy, interfaces and module unification The overall aim of WG2 will be to identify the requirements for the unification of MetM and CTM modules and to propose recommendations for a European strategy for integrated mesoscale modelling capability.

4. WG2 activities: • Overview of existing integrated (off-line and on-line) systems in Europe and outside Europe. • Identification of the advantages and disadvantages of strategies for integrating of MetMs and CTMs. • Development of guidance and strategy for on-line coupling of MetMs and CTMs and for their off-line interfacing. • Overview of existing module structures of MetMs and CTMs, along with recommendations and requirements for module unification. • Formulation of requirements of mesoscale MetMs suitable as input to air pollution models and improved meteorological pre-processors and model interfaces, including deposition processes, capable of connecting mesoscale MetM results to CTM models. • Recommended methods for the model down-scaling and nesting, as well as assimilation techniques. • Identifying requirements (including observation data needs) for an integrated mesoscale modelling capability/strategy for Europe.

5. WG2 Deliverables: • Overview of existing integrated (off-line and on-line) mesoscale systems. • Overview of existing module structures of MetMs and CTMs, recommendations and requirements for module unification. • Requirements of meso-scale MetMs suitable as input to CTMs, assessment of meteorological pre-processors and model interfaces between MetMs and CTMs. • Recommended methods for the model down-scaling and nesting, as well as assimilation techniques. • Requirements for an integrated mesoscale modelling capability/strategy for Europe, including data needs.

6. FUMAPEX: Integrated Systems for Forecasting Urban Meteorology, Air Pollution and Population ExposureProject objectives: • the improvement of meteorological forecasts for urban areas, • the connection of NWP models to urban air quality (UAQ) and population exposure (PE) models, • the building of improved Urban Air Quality Information and Forecasting Systems (UAQIFS), and • their application in cities in various European climates.

7. Scheme of the suggested improvements of meteorological forecasts (NWP) in urban areas and interfaces to urban air pollution (UAP) and population exposure (PE) models

8. Structure of the Danish nuclear emergency modelling system DMI-HIRLAM system • T version: 0.15° • S version: 0.05° • L version: 0.014° DERMA model • 3-D trajectory model • Long-range dispersion • Deposition of radionuclides • Radioactive decay ARGOS system • Radiological monitoring • Source term estimation • Local-Scale Model Chain • Health effects ECMWF global model

9. Aerosol Dynamics Modelling The following aerosol physical processes are solved 1. Nuclei mode (i): • nucleation, N(i), • condensation growth, G(i), • intramodal coagulation, C(i->i), • intermodal transfer of moment from nuclei mode, C(i->j), d M(i)/dt = N(i) + G(i) + C(i->i) 2. Accumulation mode (j): • condensation growth, G(j), • intramodal coagulation C(j->j) • intermodal transfer of moment to accumulation mode, C(i->j), • primary emission, E(k,j) d M(k,j)/dt = G(j) - C(j->j) + C(i->j) + E(j) 3. Mechanical generation mode (k): emission, condensation growth and coagulation Realisation: 1. Sectional numerical approach, 2. Analytical solutions using the modal approach.

10. Schematic Illustration of the Chemistry-Aerosol-Cloud (CAC) System being developed at DMI

11. Solid lines: particle number concentrations Dashed lines: mass concentrations

12. Integrated (on-line coupled) modeling system structure for predicting climate change and atmospheric composition

13. Source-Receptor problem & Risk Assessments • Source-term /position estimation /inverse modelling • Sensitivity /vulnerability /risk functions • Inverse problems /source-receptor problem • Adjoint equations • Illumination / smoothing of measurement function

14. Backward and Adjoint Simulations • Sensitivity of Receptors or Source-term estimation. • Trajectory modelling to calculate backward/forward individual or multiyear trajectory data sets for sensitivity studies. • Cluster and Probability fields analysis of trajectory/ dispersion data sets by month, season, and year (Mahura and Baklanov, 2002). • Adjoint problem methodology- based on variational principles, combination of direct and inverse modeling, and sensitivity theory - for atmospheric pollution problemto calculate unknown source term based on monitoring data for local- and global scales (Penenko and Baklanov, 2001).

15. Determination of source location by inverse (adjoint) model calculation using DERMA based on measured data Bio-terror: Hypothetical release of 100 g Anthrax spores Inhalation dose calculated by DERMA Sensitivity function based on inverse modelling by DERMA Measurements: