Uncertainty characterisation in atmospheric chemistry data assimilation and emission estimation

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 Climate forcing by gases and aerosols Long-range pollutant transport European air quality Dust outbreaks Solar energy UV radiation • •• Weather services Atmospheric environmental services provide data & information on Environmental agencies 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 http://www.gmes-atmosphere.eu/ Air quality Global Pollution Aerosol UV index

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

8. Satellite observations of air quality

9. 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. MACC: 30 yr ozone hole reanalyses 5 October, 2006 sparse abundant The October monthly mean over the Antarctic region. Ozone loss(1979 – 2009) 2007 2008 1983 1984 1985 1986 1987 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2009 1988

11. Ozone reanalyses: forecast error modelling • 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

12. Ozone reanalyses: forecast error modelling 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

13. Ozone reanalyses: forecast error modelling Test: Compare OmF as modelled with OmF observed Extension of the famous chi-square test

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

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. 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 16

17. MACC: Regional air quality forecasts

18. Ensemble forecasts: why ? Van Loon et al, Atm. Env. 41 (2007)

19. Ensemble forecasts: why ? Spread models represents uncertainty quite reasonably But why ? Vautard et al, GRL 2006

20. 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. Chemical data assimilation Chemical system strongly coupled: Chemical covariance matrix (Kalman filter) becomes singular Important to use advanced assimilationtechnique to exploit multivariate character: • 4D-Var • Ensemble Kalman Filter Khattatov, JGR 104, 18715 (1999)

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

23. NO2 observations from OMI instrument NO2 air pollution, observed by OMI, 2005-2007

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. Assimilation DOMINO v2 - 27 march 2007 Free model, Lotos-Euros OMI NO2 Analysis (NOx emission adjustment)

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. 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. Thank you for your attention

29. Analysis vs TOMS: 15 April 2001

30. Different OMI NO2 retrieval products EOMINO (EMPA) DOMINO v2 DOMINO v1

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

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

33. OMI NO2 versus AQ models BOLCHEM CAC CAMx CHIMERE EMEP EURAD MATCH SILAM OMI

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

35. Chemische reacties (oxidatie) Primaire vervuiling Secundaire vervuiling • •Ozon (O3 ) • • Stikstofdioxide (NO2 ) • • Salpeterzuur (HNO3 ) • • Zwavelzuur (H2SO4 ) • • PANs • • Aldehyden (HCHO, …) • Secundaire aerosolen … • Vluchtige organische verbindingen Koolwaterstoffen, aromaten, aldehydes, … • Stikstofoxiden (NOx) NO + NO2 • Zwaveldioxide (SO2) •Deeltjes •… 35

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