1. Eskes, Leiden workshop, 10 Nov 2011 1 Uncertainty characterisation in atmospheric chemistry data assimilation and emission estimation Henk Eskes KNMI, the Netherlands

2. The Chemical Weather Local, regional and global distributions of important trace gases and aerosols and their variabilities on time scales of minutes to hours to days, particularly in light of their various impacts, such as on human health, ecosystems, the meteorological weather and climate. M. Lawrence et al, Environ. Chem., 2005

3. Monitoring and short-range forecasting of atmospheric composition Towards an operational GMES service Adrian Simmons European Centre for Medium-Range Weather Forecasts

4. GMES Atmosphere Weather services Atmospheric environmental services Climate forcing by gases and aerosols Long-range pollutant transport European air quality Dust outbreaks Solar energy UV radiation Environmental agencies provide data & information on Services related to the chemical and particulate content of the atmosphere

5. Project structure MACC: 45 partners, plus third parties MACC-II: 36 partners, plus third parties Coordinated by the European Centre for Medium-Range Weather Forecasts

6. MACC Daily Service Provision Air quality Global Pollution AerosolUV index http://www.gmes-atmosphere.eu/

7. MACC Reanalysis Service Reanalysis Ozone records Flux Inversions http://www.gmes-atmosphere.eu/

8. Eskes, Leiden workshop, 10 Nov 2011 8 Satellite observations of air quality

9. Eskes, Leiden workshop, 10 Nov 2011 MACC: 30 yr ozone layer reanalyses latitude ozone layer thickness (DU) - Use all available ozone column satellite data sets - Assimilate in a chemistry-transport model for ozone, based on a sub-optimal Kalman filter approach

10. Eskes, Leiden workshop, 10 Nov 2011 10 The October monthly mean over the Antarctic region. sparse abundant 2009 20082007 5 October, 2006 20062005200420032002200120001999199819971996199519941993199219911990198919871985 Ozone loss (1979 – 2009) 1988 198619841983 MACC: 30 yr ozone hole reanalyses

11. Eskes, Leiden workshop, 10 Nov 2011 11 Sub-optimal Kalman filter approach: Forecast covariance = time-dependent variance * fixed correlations Correlation matrix: (static) function of the distance only functional form determined from OmF statistics Variance: (time dependent) Model error, growth of the forecast variance with time consistent with OmF Advection of the forecast variance (extra tracer) Solve Kalman filter analysis equation for forecast variance Ozone reanalyses: forecast error modelling

12. Eskes, Leiden workshop, 10 Nov 2011 12 Forecast covariance = time-dependent variance * fixed correlations Variance: Model error, growth of the forecast variance with time Advection of the forecast variance Analysis equation for forecast variance Ozone reanalyses: forecast error modelling

13. Eskes, Leiden workshop, 10 Nov 2011 13 Test: Compare OmF as modelled with OmF observed Extension of the famous chi-square test Ozone reanalyses: forecast error modelling

14. Eskes, Leiden workshop, 10 Nov 2011 14 1% level RMS of OmF (dotted) typically 2% Bias OmF (blue) and OmA (red) are less than 1% -1% level OmF and OmA: typical performance Example for January 2008

15. Eskes, Leiden workshop, 10 Nov 2011 15 Example: Ozone retrieval bug Validation of the O3 column retrieval for the SCIAMACHY satellite instrument Plot OmF as a function of parameters relevant for the retrieval

16. 16 MACC - Regional Air Quality Regional air quality forecasts and reanalyses Ensemble approach, based on the models EMEP, EURAD, CHIMERE, MATCH, SILAM, MOCAGE, LOTOS-EUROS Data assimilation of surface and satellite data is developed for each of the models individually Surface observations considered: Ozone, NOx, PM10, PM2.5, SO2,... Satelite data considered: NO2 (OMI, SCIAMACHY, GOME-2) Tropospheric Ozone (IASI) AOD Idea: ensemble spread represents uncertainty

17. Eskes, Leiden workshop, 10 Nov 2011 MACC: Regional air quality forecasts

18. Eskes, Leiden workshop, 10 Nov 2011 18 Van Loon et al, Atm. Env. 41 (2007) Ensemble forecasts: why ?

19. Eskes, Leiden workshop, 10 Nov 2011 19 Spread models represents uncertainty quite reasonably But why ? Vautard et al, GRL 2006 Ensemble forecasts: why ?

20. Eskes, Leiden workshop, 10 Nov 2011 MACC: Assimilation techniques used Large-scale problem model grid: longitude * latitude * altitude * component order (100)^4 or 10^8 model state variables Global reanalysis and daily analyses Based on ECMWF model Atmospheric composition + meteorological observations 4D-Var, 12h time window Ozone 30-year reanalysis: sub-optimal Kalman filter Regional analyses Several techniques used: Statistical interpolation, 3D-Var, 4D-Var, Ensemble Kalman filter Flux inversions Inverse modelling techniques, 4D-Var, EnKF

21. Eskes, Leiden workshop, 10 Nov 2011 21 Khattatov, JGR 104, 18715 (1999) Chemical system strongly coupled: Chemical covariance matrix (Kalman filter) becomes singular Important to use advanced assimilation technique to exploit multivariate character: 4D-Var Ensemble Kalman Filter Chemical data assimilation

22. Eskes, Leiden workshop, 10 Nov 2011 22 7. August 8. August 1997 + observations no optimisation initial value opt. emis. rate opt. joint emis + ini val opt. Source: Hendrik Elbern, Köln Assimilation of state + emission sources assimilation intervalforecast Information in species concentrations quickly gets lost. Emissions may store the information for longer periods.

23. Eskes, Leiden workshop, 10 Nov 2011 23 NO2 observations from OMI instrument NO2 air pollution, observed by OMI, 2005-2007

24. Eskes, Leiden workshop, 10 Nov 2011 24 NO2 observations with satellites Error analysis of NO2 retrieval Boersma et al, 2004 Error related to cloud fraction Error related to surface albedo

25. Eskes, Leiden workshop, 10 Nov 2011 25 Assimilation DOMINO v2 - 27 march 2007 Free model, Lotos-Euros OMI NO2 Analysis (NOx emission adjustment)

26. ACCENT+ AirQuality-Climate 26 OMI NO2 assimilation, 23 mar - 29 apr 2007 NOx emission adjustment factor Emission scaling factor averaged over 5 week period Only significant > 2 σ points OMI NO2 satellite data

27. Eskes, Leiden workshop, 10 Nov 2011 27 Conclusions OmF and OmA To specify spatial error correlations, time dependent error growth. Extension of chi-square test: OmF observed vs. modelled Powerful tool for satellite validation: OmF vs retrieval parameters Model ensemble air quality forecasts Spread of models represents uncertainty. But why? Chemical data assimilation Chemical system stiff, strongly coupled Near surface: little memory, information lost in hours to one day: focus on update of model parameters, such as emissions Satellite observations of air pollution (example NO2) Complicated retrieval errors: partly random, partly systematic First applications to infer emissions (4D-Var, EnKF) MACC project: http://www.gmes-atmosphere.eu/

28. Eskes, Leiden workshop, 10 Nov 2011 28 Thank you for your attention

29. Eskes, Leiden workshop, 10 Nov 2011 29 Analysis vs TOMS: 15 April 2001

30. ACCENT+ AirQuality-Climate 30 Different OMI NO2 retrieval products EOMINO (EMPA) DOMINO v2 DOMINO v1

31. Eskes, Leiden workshop, 10 Nov 2011 31 Meteorology Emissions Land use Boundary conditions … Input Instantaneous 24Hr Data Satellite data Observations NO2 PM O3 AOD EmissonsChemistry Aerosol physics Advection Wet Deposition Vertical exchange Dry Deposition … Chemistry transport model EnKF filter EnKF smoother Data-assimilation Lotos-Euros model

32. MACC Forecasts and reanalyses European Air quality Global Pollution Aerosol UV index Ozone records Flux Inversions

33. Eskes, Leiden workshop, 10 Nov 2011 33 OMI NO2 versus AQ models BOLCHEMCACCAMx CHIMEREEMEPEURAD MATCHSILAMOMI

34. Eskes, Leiden workshop, 10 Nov 2011 34 Relaties weer, chemie, uitstoot en gezondheid Radiation ChemistryMeteorology Pollutant concentrations Emissions Human health Photolyse rates Temperature Aerosols Active radiative gases Dispersion Chemical regimes… Deposition Height of the boundary layer Transport… Convection Transport Intensity Temperature Reduction of emissions Policies Clean technologies Chemical regimes speciation Primary pollutants

35. Eskes, Leiden workshop, 10 Nov 2011 35 NO + NO 2 Chemische reacties (oxidatie) Primaire vervuiling  Vluchtige organische verbindingen  Stikstofoxiden (NOx)  Deeltjes  Zwaveldioxide (SO 2 )  … … Koolwaterstoffen, aromaten, aldehydes, … Ozon (O 3 )  Ozon (O 3 )  Stikstofdioxide (NO 2 )  Salpeterzuur (HNO 3 )  Zwavelzuur (H 2 SO 4 )  PANs  Aldehyden (HCHO, …)  Secundaire aerosolen … Secundaire vervuiling 35

36. Example: Ozone analyses Example: Ozone hole simulations Neumayer ozone sondes Assimilation: MLS, OMI, SBUV Model without assimilation August 2008October 2008 Flemming et al, ACPD 10, 9173-9217, 2010