Monte Carlo uncertainty analysis
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Transcript of Monte Carlo uncertainty analysis
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Part 1
Monte Carlo uncertainty evaluationof emission reduction scenarios
constrained by observations from the ESQUIF campaign
M. Beekmann (LISA), C. Derognat (Aria-Technologies)
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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),
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What is the uncertainty in the simulation of emission reduction scenarios ?
Case of Paris agglomeration
Monte Carlo uncertainty analysis
Model output uncertainty due to uncertainty in input parameters
Constraint by measurements (ESQUIF campaign)
(Bayesian Monte Carlo uncertainty analysis)
Reduced uncertainty
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METHODOLOGY (1)SET-up of the CHIMERE model for the Paris region (version 2002)
Domain 150 km x 150 km with 6 km horizontal resolution
5 vertical levels from surface to ~3 km
Forced by ECMWF first guess or forecast
Gas phase chemistry: MELCHIOR with 82 compounds, 338 reactions
Emissions, refined for regional scale from AIRPARIF, also biogenic
Boundary conditions: from CHIMERE at continental scale
OX, NOy 16/7/99 14h POI6
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METHODOLOGY (2)Definition of the probability density function for input parameters
EMISSIONS : anthropogenic VOC 40 % (log.,1) Hanna et al, 1998 anthropogenic NOx 40 % (log.,1) as for VOC biogenic VOC 50 % (log.,1) Hanna et al, 1998, 2001
RATE CONSTANTS : NO + O3 10 % (log.,1) Atkinson et al, 1997 NO2 + OH 10 % (log.,1) Atkinson et al, 1997 NO + HO2 10 % (log.,1) Atkinson et al, 1997 NO + RO2 30 % (log.,1) Atkinson et al, 1997 HO2 + HO2 10 % (log.,1) Atkinson et al, 1997 RO2 + HO2 30 % (log.,1) Atkinson et al, 1997 RH + OH 10 % (log.,1) Atkinson et al, 1997 CH3COO2 + NO 20 % (log.,1) Atkinson et al, 1997 CH3COO2 + NO2 20 % (log.,1) Atkinson et al, 1997 PAN + M 30 % (log.,1) Atkinson et al, 1997
PHOTOLYSIS FREQUENCIES + RADIATION :
actinic flux 10 % (log.,1) see text J(O3 -> -> 2 OH) 30 % (log.,1) DeMore et al, 1997 J(NO2->NO+O3) 20 % (log.,1) DeMore et al, 1997 J(CH2O->CO+2 HO2) 40 % (log.,1) DeMore et al, 1997 J(CH3COCO-> ....) + 50 % (one sided, 1) S 95, RM 96 J(carbonyl compound from o-xylene) 40 % (log.,1 Atkinson al, 1997
METEOROLOGICAL PARAMETERS:
zonal wind speed 1 m/s (absolute,1) see text meridional wind speed 1 m/s (absolute,1) see text mixing layer height 20 % (log.,1) see text temperature 1.5 K (absolute,1) Hanna et al, 1998 relative humidity 20 % (log.,1) after Hanna et al, 1998/2001 vertical mixing coefficient 50 % (log.,1) see text deposition velocity 25 % (log.,1) Hanna et al, 1998/2001
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METHODOLOGY (3) Constraints from ESQUIF observations
From circular flights (DIMONA, MERLIN) OX, NOy, NOx, (VOC)
C = C (plume) – C (background)
From airquality network (AIRPARIF)OX = OX (urban) – OX (background)
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Flight tracks around the Paris agglomeration during ESQUIF
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METHODOLOGY (3) Constraints from ESQUIF observations
From circular flights (DIMONA, MERLIN) OX, NOy, NOx , (VOC)
C = C (plume) – C (background)
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METHODOLOGY (4)mathematical formulation of the constraint
For each Monte Carlo simulation k:
Likelihood L for model output Yk to be correct for observations Oi (Bayesian Monte Carlo analysis Bergin and Milford, 2000):
1 (Oi – Yk,i)
2
L(YkY | Oi) = _____________ EXP [ -0.5 _______________ ] (2 ii
2
L(Yk | O) = L(Yk,,1 | O1) * L(Yk,2 | O2) * …….
Measurement errors i of observations Oi are assumed as normally distributed independent They stem from instrumental errors uncertainty in representativity for model grid
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METHODOLOGY (5) Simulations performed
For 3 days in POI’s 2 and 6: August7, 1998 and July 16,17
500 Monte Carlo simulations with base line emissions
500 Monte Carlo simulations with reduced emissions
- 50 % anthropogenic VOC - 50 % anthropogenic. NOx - 50 % anthro. VOC + NOx
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RESULTS (1)
• Cumulative probability plots
Surface O3 maxima for baseline and 50% reduced emissions
With (____) and without (- - - -) constraint
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RESULTS (2)
Surface O3 maxima for baseline and 50% reduced emissions
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RESULTS (3)Chemical regime averaged over the pollution plume:
Difference in surface O3 between a
NOx emissions –50 % and a
VOC emissions –50% scenario
Positive values : VOC limited chemical regime
Average over 1998/1999 :
VOC sensitive or intermediate chemical regime (thesis C. Derognat)
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RESULTS (4)
OH averaged over the pollution plume
at 14 UT (layer 2 50-600 m):
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RESULTS (5)
A posteriori
and a priori
probability of
input parameters :
NOx and VOC
emissions
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CONCLUSIONS
Uncertainty in simulated max. ozone (for baseline and reduced emissions) reduced by a factor 1.5 to 3 due to measurement constraint
Uncertainty in VOC limited regime is reduced for two days, shift from slightly VOC limited to slightly NOx limited for anaother day
For OH, the uncertainty is less reduced, but very low values are rejected, remaining uncertainty factor 1.5 – 2.5
Weighting procedure through likelihood function changes distribution in input parameters namely NOx emissions
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Limitations of this study:
Uncertainty in model formulation is neglected (transport, model chemistry)
Uncertainty in the definition of pdf’s for input parameters
Uncertainty in error distribution of observations (covariance always zero ?)
Perspectives :
Application to continental scale
Application to air quality forecast
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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),
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Model set up
Domain covering EU to Ural + Mediterranean regions with 0.5 ° horizontal resolution
8 vertical levels from surface to 500 hPa
Forced by NCEP forecast (2.5°) and MM5 (1° res.)
Gas phase chemistry: MELCHIOR reduced
Emissions from EMEP and EDGAR, if needed
Boundary conditions: from MOZART
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Time series
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Error statistics
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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
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CHIMERE tropospheric NO2 columns versus
GOME tropospheric NO2 columns
Average June – August 1997 Western Europe Eastern Europe
Slope = 0.75R = 0.91
Slope = 0.70R = 0.77
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differences in GOME / CHIMERE tropospheric NO2 columns versus
tropospheric NO2 columns (1015mol.)
Random error in monthly mean (in a spatial sens) is mainly of multiplicative nature (25-30%), no attribution to GOME or CHIMERE possible
Western Europe
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differences in GOME / CHIMERE tropospheric NO2 columns versus
tropospheric NO2 columns (1015mol.)
Random error in monthly mean (in a spatial sens) is less clearly of multiplicative nature for Eastern Europe than for Western Europe
Eastern Europe
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CONCLUSIONS
CHIMERE domain has been extended to Eastern EU and Mediteranean region
Correlation with surface O3 obs. larger in WE (>80%) than in Central and EE <60-70%)
Comparison with GOME tropospheric NO2 :* No bias* slope 0.70-0.75* multiplicative spatial random error 15% EE – 30% WE