Stochastic Description of Subgrid Pollutant Variability in CMAQ. Jerold A. Herwehe Atmospheric Turbulence & Diffusion Division Air Resources Laboratory National Oceanic and Atmospheric Administration 456 S. Illinois Ave., P.O. Box 2456 Oak Ridge, Tennessee 37831-2456 - PowerPoint PPT Presentation
Jerold A. Herwehe Atmospheric Turbulence & Diffusion Division Air Resources Laboratory National Oceanic and Atmospheric Administration 456 S. Illinois Ave., P.O. Box 2456 Oak Ridge, Tennessee 37831-2456 (E-mail: [email protected]) Stochastic Description of Subgrid Pollutant Variability in CMAQ Jason K. S. Ching and Jenise L. Swall NOAA/ARL/Atmospheric Sciences Modeling Division on assignment to USEPA/NERL Research Triangle Park, North Carolina
Transcript
Jerold A. HerweheAtmospheric Turbulence & Diffusion Division
Air Resources LaboratoryNational Oceanic and Atmospheric Administration
Stochastic Description of Subgrid Pollutant Variability in CMAQ
Jason K. S. Ching and Jenise L. SwallNOAA/ARL/Atmospheric Sciences Modeling Division
on assignment to USEPA/NERLResearch Triangle Park, North Carolina
Motivation
Regional scale air quality (AQ) models are currently limited to relatively coarse (≥ 1 km) grids.
Emergency management, human exposure and risk assessment require more detailed information on hazardous pollutant, or air toxics, concentration “hot spots.”
Research Objectives
Develop methodology and associated software tools to perform statistical analyses on available fine resolution gridded model results in order to quantify subgrid pollutant variability not represented in current AQ models.
Provide linkage between the Community Multiscale Air Quality (CMAQ) modeling system (Ching and Byun 1999; http://www.epa.gov/asmdnerl/models3/cmaq.html) and the Hazardous Air Pollutant Exposure Model (HAPEM; http://www.epa.gov/ttn/fera/human_hapem.html).
Current Approach
Utilize objective Exploratory Data Analysis (EDA) approach (NIST/SEMATECH 2003) with companion freeware statistical analysis Dataplot package from NIST (http://www.itl.nist.gov/div898/software/dataplot/) to develop subgrid concentration analysis program dubbed CDFware (Concentration Distribution Function –ware).
Apply CDFware to sample AQ model output to produce probability density functions (pdfs).
CDFware conducts suite of statistical tests before determining best fit distribution.
Acetaldehyde Mean Mixing RatioCMAQ 14 July 1995 15:00 LST (1.33 km)2 Grid Cells
Acetaldehyde Mean Mixing RatioCMAQ 14 July 1995 15:00 LST (12 km)2 Grid Cells
Derived from (1.33 km)2 Grid Data of Figure (a)
CMAQ 14 July 1995 15:00 LST Acetaldehyde Histograms for (12 km)2 Grid Cells
1 2 3 4 5 6 7 8 9 10
C ell I N um ber
1
2
3
4
5
6
7
8
9
10
Cel
l J N
umbe
r
-1 .0
-0.9
-0.8
-0.7
-0.6
-0.5
-0.4
-0.3
-0.2
-0.1
-0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
Tukey-Lambda Shape Parameter for AcetaldehydeCMAQ 14 July 1995 15:00 LST (12 km)2 Grid Cells
1 2 3 4 5 6 7 8 9 10
C ell I Num ber
1
2
3
4
5
6
7
8
9
10
Cel
l J N
umbe
r
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16D istribution
U niform
N orm al
W eibull (+skew)
Lognorm al
G am m a
Pow er N orm al
Pow er Lognorm al
Skewed N orm al
Frechet (+skew)
G en. Extrem e Val.
Inverted W eibull
Chi-Squared
W eibull (-skew )
Frechet (-skew )
Logistic
Best-Choice Distribution for AcetaldehydeCMAQ 14 July 1995 15:00 LST (12 km)2 Grid Cells
10 20 30 40 50 60 70 80 90
C ell I N um ber
10
20
30
40
50
60
70
80
90C
ell J
Num
ber
0.002
0.004
0.006
0.008
0.010
0.012
0.014
0.016
0.018
0.020
0.022
0.024
0.026
FO RM (ppm v)
Form aldehyde M ean M ixing R atioC M AQ 14 July 1995 15:00 LST 1.33 km G rid
0 1 2 3 4 5 6 7 8 9 10
C ell I N um ber
0
1
2
3
4
5
6
7
8
9
10
Cel
l J N
umbe
r
0.002
0.004
0.006
0.008
0.010
0.012
0.014
0.016
0.018
0.020
0.022
0.024
0.026
C M AQ 14 July 1995 15:00 LST 12 km G rid
Form aldehyde M ean M ixing R atioFO RM (ppm v)
1 2 3 4 5 6 7 8 9 10
C ell I N um ber
1
2
3
4
5
6
7
8
9
10
Cel
l J N
umbe
r
-1 .0
-0.9
-0.8
-0.7
-0.6
-0.5
-0.4
-0.3
-0.2
-0.1
-0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
Tukey-Lam bda Shape Param eter for Form aldehydeC M AQ 14 July 1995 15:00 LST 12 km G rid
1 2 3 4 5 6 7 8 9 10
C ell I N um ber
1
2
3
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Cel
l J N
umbe
r
0
1
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9
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13
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15
16Distribution
Best-C hoice D istribution for Form aldehydeC M AQ 14 July 1995 15:00 LST 12 km G rid
U niform
N orm al
W eibull (+skew )
Lognorm al
G am m a
Pow er N orm al
Pow er Lognorm al
Skewed Norm al
Frechet (+skew)
G en. Extrem e Val.
Inverted W eibull
C hi-Squared
W eibull (-skew)
Frechet (-skew)
Logistic
10 20 30 40 50 60 70 80 90
Cell I Num ber
10
20
30
40
50
60
70
80
90C
ell J
Num
ber
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
0.10
0.11
0.12
0.13
0.14
0.15
0.16
0.17
O 3 (ppm v)
O zone M ean M ixing RatioC M AQ 14 July 1995 15:00 LST 1.33 km G rid
0 1 2 3 4 5 6 7 8 9 10
Cell I Num ber
0
1
2
3
4
5
6
7
8
9
10
Cel
l J N
umbe
r
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
0.10
0.11
0.12
0.13
0.14
0.15
0.16
0.17
O zone M ean M ixing R atioC M AQ 14 July 1995 15:00 LST 12 km G rid
O 3 (ppm v)
1 2 3 4 5 6 7 8 9 10
C ell I N um ber
1
2
3
4
5
6
7
8
9
10
Cel
l J N
umbe
r
-1 .0
-0.9
-0.8
-0.7
-0.6
-0.5
-0.4
-0.3
-0.2
-0.1
-0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
Tukey-Lam bda Shape Param eter for O zoneC M AQ 14 July 1995 15:00 LST 12 km G rid
1 2 3 4 5 6 7 8 9 10
C ell I N um ber
1
2
3
4
5
6
7
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10
Cel
l J N
umbe
r
0
1
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16Distribution
Best-C hoice D istribution for O zoneC M AQ 14 July 1995 15:00 LST 12 km G rid
U niform
N orm al
W eibull (+skew )
Lognorm al
G am m a
Pow er N orm al
Pow er Lognorm al
Skewed Norm al
Frechet (+skew)
G en. Extrem e Val.
Inverted W eibull
C hi-Squared
W eibull (-skew)
Frechet (-skew)
Logistic
CMAQ 14 July 1995 15:00 LST Acetaldehyde Histograms with Fitted Weibull PDFs
1 2 3 4 5 6 7 8 9 10
C ell I N um ber
1
2
3
4
5
6
7
8
9
10
Cel
l J N
umbe
r
0.938
0.942
0.946
0.950
0.954
0.958
0.962
0.966
0.970
0.974
0.978
0.982
0.986
0.990
0.994
0.998m ax. PPC C
(a)
1 2 3 4 5 6 7 8 9 10
Cell I N um ber
1
2
3
4
5
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7
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9
10
Cel
l J N
umbe
r
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
2.0
2.2
2.4
2.6
2.8
3.0
3.2
3.4
3.6
3.8
(b)
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C ell I N um ber
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2
3
4
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6
7
8
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Cel
l J N
umbe
r
0.0020
0.0022
0.0024
0.0026
0.0028
0.0030
0.0032
0.0034
0.0036
0.0038
0.0040
0.0042
0.0044
0.0046
0.0048
0.0050
0.0052
0.0054
(c)
1 2 3 4 5 6 7 8 9 10
C ell I N um ber
1
2
3
4
5
6
7
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Cel
l J N
umbe
r
0.0001
0.0002
0.0003
0.0004
0.0005
0.0006
0.0007
0.0008
0.0009
0.0010
0.0011
0.0012
0.0013
0.0014
0.0015
0.0016
0.0017
0.0018
0.0019
0.0020
0.0021
0.0022
(d)
Weibull Maximum PPCC Value for AcetaldehydeCMAQ 14 July 1995 15:00 LST 12 km Grid
Weibull Shape Parameter for AcetaldehydeCMAQ 14 July 1995 15:00 LST 12 km Grid
Weibull Scale Parameter for AcetaldehydeCMAQ 14 July 1995 15:00 LST 12 km Grid
Weibull Location Parameter for AcetaldehydeCMAQ 14 July 1995 15:00 LST 12 km Grid
Results and Conclusions
Concentration Distribution Function –ware (CDFware) tool was developed using EDA and Dataplot to statistically analyze fine resolution model output to determine best-fit distributions representing subgrid pollutant concentration variability.
Initial application of CDFware to example 1.33 km grid pollutant “data” from a CMAQ case study produced complex statistical results from numerous distribution family fits.
Restricting to a Weibull-only analysis did not produce any readily discernible spatial or temporal patterns in the PPCC, shape, location, or scale parameter fields.
Despite the current complexity of the CDFware results, these quantitative statistical products could still enhance the input stream to human risk and exposure models based on census tract scales. Extreme concentration values are represented in the distribution fits.
Development and refinement of CDFware will continue. Desirable additions include ability to detect and fit multimodal concentration distributions. CDFware will be applied to higher resolution output from neighborhood-scale coupled large-eddy simulation (LES)-photochemical model and computational fluid dynamics (CFD) simulations to possibly yield more coherent distribution parameter fields suitable for developing parameterizations of subgrid pollutant concentration variation within regional AQ model grid cells.
References
Bury, K., 1999: Statistical Distributions in Engineering. Cambridge University Press, 362 pp.
Ching, J., and D. Byun, 1999: Introduction to the Models-3 framework and the Community Multiscale Air Quality model (CMAQ). In Science Algorithms of the EPA Models-3 Community Multiscale Air Quality (CMAQ) Modeling System, edited by D. W. Byun and J. K. S. Ching, EPA-600/R-99/030, Chapter 1, National Exposure Research Laboratory, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina.
NIST/SEMATECH, cited 2003: NIST/SEMATECH e-Handbook of Statistical Methods. [Available online at http://www.itl.nist.gov/div898/handbook/.]
Acknowledgments
This research was supported by the National Oceanic and Atmospheric Administration’s Air Resources Laboratory and the U.S. Environmental Protection Agency’s National Exposure Research Laboratory.
Disclaimer: This work has been reviewed in accordance with the United States Environmental Protection Agency’s peer and administrative review policies and approved for presentation and publication.
