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

2. 26 Nov 2009TNO experience2 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

3. 26 Nov 2009TNO experience3 Emissions of Elemental Carbon in Europe How can satellite data help to improve these maps?

4. 26 Nov 2009TNO experience4 Natural & Biogenic emissions – calculated online Isoprene Marine emissions How can satellite data help to improve these algorithms?

5. 26 Nov 2009TNO experience5 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

6. Meteorological forecast ECMWF Emissions Regional / Local Preprocessing Transport Advection Turbulence Deposition Wet and Dry Chemistry Gas phase Aerosol Gidded hourly simulated concentrations: Gases O 3, NO 2, SO 2 … Aerosols Sulfate, Nitrate, sec. organic, primary… Wet, dry deposition fluxes Explicit CTM Global chemical forcing climatology / explicit model Land use Input data Numerical formulation 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. 26 Nov 2009TNO experience7 Meteorology Emissions Land use Boundary conditions … Input Instantaneous 24Hr Data Satellite data Observations NO2 PM O3 AOD Schematic of LOTOS-EUROS modelling system: EmissonsChemistry Aerosol physics Advection Wet Deposition Vertical exchange Dry Deposition … Chemistry transport model EnKF filter EnKF smoother Data-assimilation

8. 26 Nov 2009TNO experience8 Integration of earth observation data into models A priory State Assimilation procedure Observations Analysed State (x a ) Model integration Assimilation Procedure EnKF Observations Satellite data In-situ data Analysed State Concentrations Emissions Other parameters Model integration Model integration Air Quality forecast Weather forecast Model input Meteorology Emissions Noise Output L4 data products Emission estimates

9. 26 Nov 2009TNO experience9 Ozone measurements from the EMEP network assimilated Single component assimilation – Ozone

10. 26 Nov 2009TNO experience10 Validation Vredepeel Assimilation station Westmaas Validation station

11. 26 Nov 2009TNO experience11 Single component assimilation – PM10 Without assimilation With assimilation From Denby et al. 2008, Atmospheric environment 42

12. 26 Nov 2009TNO experience12 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

13. 26 Nov 2009TNO experience13 Assimilation of SO2 and SO4 – a case study SO2 SO4 Annual mean for 2003

14. 26 Nov 2009TNO experience14 Modelled annual mean concentrations SO2 and SO4 SO2SO4 OBS1.62.5 MOD2.31.8 OBS/ MOD 1.40.7 RESID1.51.4 RMSE2.1 Cor0.480.47

15. 26 Nov 2009TNO experience15 Results: SO2 annual cycle over all assimilation stations

16. 26 Nov 2009TNO experience16 Results: SO2 & SO4 annual cycle over all stations by including uncertain conversion rates SO2SO4

17. 26 Nov 2009TNO experience17 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

18. 26 Nov 2009TNO experience18 Assimilation OMI NO2 measurements with LOTOS-EUROS Analysis NOx emissions / inventory (yellow=1) Impact for ozone at the surface

19. 26 Nov 2009TNO experience19 Assimilation Surface ozone Impact of assimilation on ozone peak value No assimilation Assimilation OMI NO 2 NO2 bias in the model effects ozone negatively Note, OMI NO2 may be ~25% high

20. 26 Nov 2009TNO experience20 Bias determination PM & AOD AERONET AOD - model Probability AERONET AOD all data Modeled AOD Daily average AOD over all stations AOD aeronet = 1.6 * AOD model PM10 = 2 * PM10 model To use AOD for estimating PM concentrations and emissions

21. 26 Nov 2009TNO experience21 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) AERONETEMEP

22. 26 Nov 2009TNO experience22 Model to MODIS comparison MODIS compositeModelled composite

23. 26 Nov 2009TNO experience23 Effect of Assimilation After assimilationMODIS composite

24. 26 Nov 2009TNO experience24 Impact of assimilation on comparison with MODIS RMSE Cor

25. 26 Nov 2009TNO experience25 Verification with AERONET Correlation Model Assimilation RMSE 1 1 0

26. 26 Nov 2009TNO experience26 PM10 measurements Correlation RMSE Assimilation Model

27. 26 Nov 2009TNO experience27 May 6thMay 7th Assimilation Model MODIS

28. 26 Nov 2009TNO experience28 Hamburg 1 2

29. 26 Nov 2009TNO experience29 Neuglobsow AOD PM10

30. 26 Nov 2009TNO experience30 Exploring the impact of forest fire emissions (FMI) on calculated PM fields: May 6 th, 2006 LOTOS-EUROS PM

31. 26 Nov 2009TNO experience31 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

32. 26 Nov 2009TNO experience32 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

33. 26 Nov 2009TNO experience33 Assimilation stations

34. 26 Nov 2009TNO experience34 Validation stations