Part 1  Monte Carlo uncertainty evaluation of emission reduction scenarios constrained by observations from the ESQUIF cam...
Part 2  Extension of CHIMERE to Eastern Europe and evaluation with surface and satellite data I. Konovalov (Institute of A...
What is the uncertainty in the simulation of emission reduction scenarios ?      Case of Paris agglomeration <ul><li>Mont...
METHODOLOGY (1) SET-up of the CHIMERE model  for the Paris region  (version 2002) <ul><li>Domain 150 km x 150 km with 6 km...
METHODOLOGY (2) Definition of the probability density function for input parameters
METHODOLOGY (3) Constraints from ESQUIF observations <ul><li>From circular flights (DIMONA, MERLIN)  </li></ul><ul><li> ...
Flight tracks around the Paris agglomeration during ESQUIF
METHODOLOGY (3) Constraints from ESQUIF observations <ul><li>From circular flights (DIMONA, MERLIN)  </li></ul><ul><li> ...
METHODOLOGY (4) mathematical formulation of the constraint <ul><li>For each Monte Carlo simulation  k :  </li></ul><ul><li...
METHODOLOGY (5)  Simulations performed <ul><li>For 3 days in POI’s 2 and 6: August7, 1998 and July 16,17 </li></ul><ul><li...
RESULTS  (1) <ul><li>Cumulative probability plots </li></ul><ul><li>Surface O3 maxima for  baseline  and  50% reduced  emi...
RESULTS  (2) <ul><li>Surface O3 maxima for  baseline  and  50% reduced  emissions </li></ul>
RESULTS  (3) <ul><li>Chemical regime averaged over the pollution plume: </li></ul><ul><li>Difference in surface O3 between...
RESULTS  (4) <ul><li>OH averaged over the pollution plume </li></ul><ul><li>at 14 UT (layer 2  50-600 m): </li></ul>
  RESULTS  (5) <ul><li>A posteriori  </li></ul><ul><li>and a priori </li></ul><ul><li>probability of </li></ul><ul><li>inp...
CONCLUSIONS  <ul><li>Uncertainty in simulated max. ozone  (for baseline and reduced emissions) reduced by a factor  1.5 to...
Limitations of this study: <ul><li>Uncertainty in model formulation is neglected (transport, model chemistry) </li></ul><u...
Part 2  Extension of CHIMERE to Eastern Europe and evaluation with surface and satellite data I. Konovalov (Institute of A...
Model set up <ul><li>Domain covering EU to Ural + Mediterranean regions with 0.5 ° horizontal resolution </li></ul><ul><li...
Time series
Error statistics
Comparison between GOME  and CHIMERE derived  tropospheric NO2 columns,  June – August 1997 University of Bremen, GOME ver...
CHIMERE tropospheric NO2 columns  versus  GOME tropospheric NO2 columns Average June – August 1997   Western Europe  Easte...
differences  in  GOME / CHIMERE tropospheric NO2 columns  versus  tropospheric NO2 columns (10 15 mol.)  <ul><li>Random er...
differences  in  GOME / CHIMERE tropospheric NO2 columns  versus  tropospheric NO2 columns (10 15 mol.)  <ul><li>Random er...
CONCLUSIONS  <ul><li>CHIMERE domain has been extended to Eastern EU and Mediteranean region </li></ul><ul><li>Correlation ...
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Part 1 Monte Carlo uncertainty evaluation

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Part 1 Monte Carlo uncertainty evaluation

  1. 1. Part 1 Monte Carlo uncertainty evaluation of emission reduction scenarios constrained by observations from the ESQUIF campaign M. Beekmann (LISA), C. Derognat (Aria-Technologies)
  2. 2. Part 2 Extension of CHIMERE to Eastern Europe and evaluation with surface and satellite data I. Konovalov (Institute of Appplied Physics, Nizhny Novgorod) M. Beekmann (LISA) R. Vautard (LMD/IPSL) A. Richter (IUP, University of Bremen) J. Burrows (IUP, University of Bremen) ,
  3. 3. What is the uncertainty in the simulation of emission reduction scenarios ?  Case of Paris agglomeration <ul><li>Monte Carlo uncertainty analysis </li></ul><ul><li> </li></ul><ul><li>Model output uncertainty due to uncertainty in input parameters </li></ul><ul><li> </li></ul><ul><li>Constraint by measurements (ESQUIF campaign) </li></ul><ul><li>(Bayesian Monte Carlo uncertainty analysis) </li></ul><ul><li> </li></ul><ul><li>Reduced uncertainty </li></ul>
  4. 4. METHODOLOGY (1) SET-up of the CHIMERE model for the Paris region (version 2002) <ul><li>Domain 150 km x 150 km with 6 km horizontal resolution </li></ul><ul><li>5 vertical levels from surface to ~3 km </li></ul><ul><li>Forced by ECMWF first guess or forecast </li></ul><ul><li>Gas phase chemistry: MELCHIOR with 82 compounds, 338 reactions </li></ul><ul><li>Emissions, refined for regional scale from AIRPARIF, also biogenic </li></ul><ul><li>Boundary conditions: from CHIMERE at continental scale </li></ul>OX, NOy 16/7/99 14h POI6
  5. 5. METHODOLOGY (2) Definition of the probability density function for input parameters
  6. 6. METHODOLOGY (3) Constraints from ESQUIF observations <ul><li>From circular flights (DIMONA, MERLIN) </li></ul><ul><li> OX,  NOy,  NOx, (  VOC) </li></ul><ul><li> C = C (plume) – C (background) </li></ul><ul><li>From airquality network (AIRPARIF) </li></ul><ul><li> OX = OX (urban) – OX (background) </li></ul>
  7. 7. Flight tracks around the Paris agglomeration during ESQUIF
  8. 8. METHODOLOGY (3) Constraints from ESQUIF observations <ul><li>From circular flights (DIMONA, MERLIN) </li></ul><ul><li> OX,  NOy,  NOx , (  VOC) </li></ul><ul><li> C = C (plume) – C (background) </li></ul>
  9. 9. METHODOLOGY (4) mathematical formulation of the constraint <ul><li>For each Monte Carlo simulation k : </li></ul><ul><li>Likelihood L for model output Y k to be correct for observations O i (Bayesian Monte Carlo analysis Bergin and Milford, 2000): </li></ul><ul><li>1 (O i – Y k,i ) 2 </li></ul><ul><li>L(Y kY | O i ) = _____________ EXP [ -0.5 _______________ ] </li></ul><ul><li> (2    i  i 2 </li></ul><ul><li>L( Y k | O ) = L(Y k,,1 | O 1 ) * L(Y k,2 | O 2 ) * ……. </li></ul><ul><li>Measurement errors  i of observations O i are assumed as </li></ul><ul><li>normally distributed </li></ul><ul><li>independent </li></ul><ul><li>They stem from </li></ul><ul><li>instrumental errors </li></ul><ul><li>uncertainty in representativity for model grid </li></ul>
  10. 10. METHODOLOGY (5) Simulations performed <ul><li>For 3 days in POI’s 2 and 6: August7, 1998 and July 16,17 </li></ul><ul><li>500 Monte Carlo simulations with base line emissions </li></ul><ul><li>500 Monte Carlo simulations with reduced emissions </li></ul><ul><li>- 50 % anthropogenic VOC </li></ul><ul><li>- 50 % anthropogenic. NOx </li></ul><ul><li>- 50 % anthro. VOC + NOx </li></ul>
  11. 11. RESULTS (1) <ul><li>Cumulative probability plots </li></ul><ul><li>Surface O3 maxima for baseline and 50% reduced emissions </li></ul><ul><li>With ( ____ ) and without (- - - -) constraint </li></ul>
  12. 12. RESULTS (2) <ul><li>Surface O3 maxima for baseline and 50% reduced emissions </li></ul>
  13. 13. RESULTS (3) <ul><li>Chemical regime averaged over the pollution plume: </li></ul><ul><li>Difference in surface O3 between a </li></ul><ul><li>NOx emissions –50 % and a </li></ul><ul><li>VOC emissions –50% scenario </li></ul><ul><li>Positive values : VOC limited chemical regime </li></ul><ul><li>Average over 1998/1999 : </li></ul><ul><li>VOC sensitive or intermediate chemical regime (thesis C. Derognat) </li></ul>
  14. 14. RESULTS (4) <ul><li>OH averaged over the pollution plume </li></ul><ul><li>at 14 UT (layer 2 50-600 m): </li></ul>
  15. 15. RESULTS (5) <ul><li>A posteriori </li></ul><ul><li>and a priori </li></ul><ul><li>probability of </li></ul><ul><li>input parameters : </li></ul><ul><li>NOx and VOC </li></ul><ul><li>emissions </li></ul>
  16. 16. CONCLUSIONS <ul><li>Uncertainty in simulated max. ozone (for baseline and reduced emissions) reduced by a factor 1.5 to 3 due to measurement constraint </li></ul><ul><li>Uncertainty in VOC limited regime is reduced for two days, shift from slightly VOC limited to slightly NOx limited for anaother day </li></ul><ul><li>For OH, the uncertainty is less reduced, but very low values are rejected, remaining uncertainty factor 1.5 – 2.5 </li></ul><ul><li>Weighting procedure through likelihood function changes distribution in input parameters namely NOx emissions </li></ul>
  17. 17. Limitations of this study: <ul><li>Uncertainty in model formulation is neglected (transport, model chemistry) </li></ul><ul><li>Uncertainty in the definition of pdf’s for input parameters </li></ul><ul><li>Uncertainty in error distribution of observations (covariance always zero ?) </li></ul><ul><li>Perspectives : </li></ul><ul><li>Application to continental scale </li></ul><ul><li>Application to air quality forecast </li></ul>
  18. 18. Part 2 Extension of CHIMERE to Eastern Europe and evaluation with surface and satellite data I. Konovalov (Institute of Appplied Physics, Nizhny Novgorod) M. Beekmann (LISA) R. Vautard (LMD/IPSL) A. Richter (IUP, University of Bremen) J. Burrows (IUP, University of Bremen) ,
  19. 19. Model set up <ul><li>Domain covering EU to Ural + Mediterranean regions with 0.5 ° horizontal resolution </li></ul><ul><li>8 vertical levels from surface to 500 hPa </li></ul><ul><li>Forced by NCEP forecast (2.5°) and MM5 (1° res.) </li></ul><ul><li>Gas phase chemistry: MELCHIOR reduced </li></ul><ul><li>Emissions from EMEP and EDGAR, if needed </li></ul><ul><li>Boundary conditions: from MOZART </li></ul>
  20. 20. Time series
  21. 21. Error statistics
  22. 22. Comparison between GOME and CHIMERE derived tropospheric NO2 columns, June – August 1997 University of Bremen, GOME version V2 320 * 40 km resolution I. B. Konovalov, M. Beekmann, R. Vautard, J. P. Burrows, A. Richter, H. Nüß, N. Elansky , ACP, 2005
  23. 23. CHIMERE tropospheric NO2 columns versus GOME tropospheric NO2 columns Average June – August 1997 Western Europe Eastern Europe Slope = 0.75 R = 0.91 Slope = 0.70 R = 0.77
  24. 24. differences in GOME / CHIMERE tropospheric NO2 columns versus tropospheric NO2 columns (10 15 mol.) <ul><li>Random error in monthly mean (in a spatial sens) is mainly of multiplicative </li></ul><ul><li>nature (25-30%), no attribution to GOME or CHIMERE possible </li></ul>Western Europe
  25. 25. differences in GOME / CHIMERE tropospheric NO2 columns versus tropospheric NO2 columns (10 15 mol.) <ul><li>Random error in monthly mean (in a spatial sens) is less clearly of multiplicative </li></ul><ul><li>nature for Eastern Europe than for Western Europe </li></ul>Eastern Europe
  26. 26. CONCLUSIONS <ul><li>CHIMERE domain has been extended to Eastern EU and Mediteranean region </li></ul><ul><li>Correlation with surface O3 obs. larger in WE (>80%) than in Central and EE <60-70%) </li></ul><ul><li>Comparison with GOME tropospheric NO2 : * No bias * slope 0.70-0.75 * multiplicative spatial random error 15% EE – 30% WE </li></ul>

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