M. Schaap, R. Timmermans, H. Denier van der Gon, H. Eskes, D. Swart, P. Builtjes

1. On the estimation of emissions from earth observation data M. Schaap, R. Timmermans, H. Denier van der Gon, H. Eskes, D. Swart, P. Builtjes Experiences from TNO using the LOTOS-EUROS model TNO experience

2. TNO has a large experience in emission inventories • Emission inventories on global, European and national scale • Top-down and bottom-up • Much attention for spatial distribution • Delivered emissions to e.g. GEMS, MACC, EUCAARI, MEGAPOLI TNO experience

3. Emissions of Elemental Carbon in Europe How can satellite data help to improve these maps? TNO experience

4. Natural & Biogenic emissions – calculated online Isoprene Marine emissions How can satellite data help to improve these algorithms? TNO experience

5. Research aims at TNO i.r.t. Earth Observation • To combine earth observation data and modelling to obtain an optimal assessment of the air quality over Europe. • To quantify anthropogenic emission strengths by using EO data. • Reanalysis as well as NRT Groundbased Model Satellite TNO experience

6. Numerical formulation Explicit CTM Input data Transport Advection Turbulence Gidded hourly simulated concentrations: Gases O3 , NO2 , SO2 … AerosolsSulfate, Nitrate, sec. organic, primary… Wet, dry deposition fluxes Emissions Regional / Local Preprocessing Chemistry Gas phase Aerosol Land use Deposition Wet and Dry Meteorological forecast ECMWF Global chemical forcing climatology / explicit model LOTOS-EUROS • CTM directed at the lower troposphere (up to 5 Km) • Developed at TNO & RIVM • Used at KNMI, PBL, Univ. Berlin, Univ. Aveiro • Includes a data assimilation environment

7. Meteorology Emissons Chemistry Instantaneous Emissions Advection Aerosol physics 24Hr Data Land use Vertical exchange Dry Deposition Satellite data Boundary conditions Wet Deposition … NO2 PM O3 AOD … Chemistry transport model Observations Input EnKF filter EnKF smoother Data-assimilation Schematic of LOTOS-EUROS modelling system: TNO experience

8. Model input Meteorology Emissions Noise Model Model A priory State integration integration Observations Satellite data In-situ data Assimilation Procedure EnKF Analysed State Concentrations Emissions Other parameters Output L4 data products Emission estimates Assimilation Analysed Observations procedure State ( x ) a Weather forecast Model integration Air Quality forecast Integration of earth observation data into models TNO experience

9. Single component assimilation – Ozone Ozone measurements from the EMEP network assimilated TNO experience

10. Validation Vredepeel Assimilation station Westmaas Validation station TNO experience

11. Single component assimilation – PM10 With assimilation Without assimilation From Denby et al. 2008, Atmospheric environment 42 TNO experience

12. The big challenges: • To disentangle the uncertainty due to the emission input from other model uncertainties • The assimilation “blames” all errors to a limited amount of parameters • To keep the system realistic and balanced • To combine different sources of data – multi component TNO experience

13. Assimilation of SO2 and SO4 – a case study SO2 SO4 Annual mean for 2003 TNO experience

14. Modelled annual mean concentrationsSO2 and SO4 TNO experience

15. Results: SO2 annual cycle over all assimilation stations TNO experience

16. Results: SO2 & SO4 annual cycle over all stations by including uncertain conversion rates SO2 SO4 TNO experience

17. Annual mean estimated multiplication factors Emissions Reaction rate Also after acknowledging the shortcomings of the model it indicates that the shipping emissions and those in Poland may be too high TNO experience

18. Assimilation OMI NO2 measurements with LOTOS-EUROS Impact for ozone at the surface Analysis NOx emissions / inventory (yellow=1) TNO experience

19. Impact of assimilation on ozone peak value Assimilation Surface ozone Assimilation OMI NO2 No assimilation NO2 bias in the model effects ozone negatively Note, OMI NO2 may be ~25% high TNO experience

20. Probability AERONET AOD - model To use AOD for estimating PM concentrations and emissions Bias determination PM & AOD Daily average AOD over all stations AODaeronet = 1.6 * AODmodel Modeled AOD PM10 = 2 * PM10model AERONET AOD all data TNO experience

21. Assimilation strategy • 2006 • LOTOS-EUROS CTM • Bias corr.: AOD = AOD * 1.6 • Reduced domain • MODIS data • Uncertainty: 0.05 + 0.15AOD • All pixels used • EnKF Assimilation • Model uncertainty relative to anthropogenic emissions • 30%, Daily, time correlation of three days • 12 ensemble members • Model simulation at overpass time stored during the day • Assimilation performed once a day at midnight • Localisation (ρ = 50 Km) AERONET EMEP TNO experience

22. Model to MODIS comparison MODIS composite Modelled composite TNO experience

23. Effect of Assimilation MODIS composite After assimilation TNO experience

24. Impact of assimilation on comparison with MODIS RMSE Cor TNO experience

25. Verification with AERONET 1 Correlation Assimilation 0 1 Model RMSE Assimilation Model TNO experience

26. Correlation PM10 measurements Assimilation Model RMSE Assimilation TNO experience Model

27. May 6th May 7th MODIS Assimilation Model TNO experience

28. Hamburg 2 1 TNO experience

29. Neuglobsow AOD PM10 TNO experience

30. Exploring the impact of forest fire emissions (FMI) on calculated PM fields: May 6th, 2006 LOTOS-EUROS PM TNO experience

31. Conclusions • To quantify emissions from observations one needs a model • Data assimilation or inverse modelling of EO data is feasible • We are able to provide level 4 products • Data assimilation an objective framework • To estimate emissions challenges are: • To disentangle the uncertainty due to the emission input from other model uncertainties • To keep the system realistic and balanced • To combine different sources of data – multi component • Hence, this is a long term scientific research line TNO experience

32. Where can we use our present capabilities to provide information on emissions? • Search for trends in the parameter estimates? • Does the EO data indicate that the emission trend is not as expected? • The system does the meteo correction, etc for you. • To indentify locations of new and significant emission sources • The areas with consistently high model-measurement deviations • To identify time profiles – needed: geostationary data • Emission estimates • Only in hotspot locations, and/or with observations during the emission itself. • Direct variables such as land use, LAI, Fire Radiative Power, White cap, etc TNO experience

33. Assimilation stations TNO experience

34. Validation stations TNO experience