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Regional Haze Modeling: Recent Modeling Results for VISTAS and WRAP October 27, 2003, CMAS Annual Meeting, RTP, NC University of California, Riverside.

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Presentation on theme: "Regional Haze Modeling: Recent Modeling Results for VISTAS and WRAP October 27, 2003, CMAS Annual Meeting, RTP, NC University of California, Riverside."— Presentation transcript:

1 Regional Haze Modeling: Recent Modeling Results for VISTAS and WRAP October 27, 2003, CMAS Annual Meeting, RTP, NC University of California, Riverside

2 Modeling Team Participants UC Riverside: Gail Tonnesen, Zion Wang, Chao- Jung Chien, Mohammad Omary, Bo Wang Ralph Morris et al., ENVIRON Corporation Zac Adelman et al., Carolina Environmental Program Tom Tesche et al., Alpine Geophysics Don Olerud, BAMS

3 Acknowledgments Western Regional Air Partnership: John Vimont, Mary Uhl, Kevin Briggs, Tom Moore, VISTAS: Pat Brewer, Jim Boylan, Shiela Holman

4 Topics Model Performance Evaluation WRAP 1996 Model Performance Evaluation VISTAS 2002 Sensitivity Results CMAQ Benchmarks

5 WRAP Modeling 1996 Annual Modeling 36 km grid for western US, 95x85x18 layers MM5 by Olerud et al.

6 WRAP Emissions Updates Corrections to point sources MOBILE6 beta for WRAP states Monthly corrections for NH3 based on EPA/ORD inverse modeling. Updated non-road model Typical fires used for results shown here 1996 NEI for non-WRAP states

7 WRAP - CMAQ revisions v0301, released in March 2001 –Used as the base case and all sensitivity cases for WRAP’s 309 simulations. v0602, released in June 2002 v4.2.2, released in March 2003 v4.3, released in Sept. 2003

8 Comparisons based on IMPROVE evaluation

9 How well does the model reproduces mean, modal, and variational characteristics ? –Using observations to normalize model error & bias result in misleading conclusion: if observation is very small  large bias or error if model under prediction  bounded by -1 model over prediction is weighted more than under prediction We used Mean Normalized Err & Bias in 309: –Poor metric for clean conditions Model Performance Metrics

10 Use fractional error and bias: –bias and error is bounded symmetrical limits of +2 Normalized Mean Error & Bias: –Divide the sum of the errors by the sum of the observations. Coefficient of determination (R 2 ) –explains how much of the variability in the model predictions can be explained by the fact that they are related to ambient observation, i.e. how close the points are to the observations. Recommended Performance Metrics

11 Statistical measures used in model performance evaluation Measure Mathematical Expression Notation Accuracy of unpaired peak (Au) O peak = peak observation; P u peak = unpaired peak prediction within 2 grid cells of peak observation site Accuracy of paired peak (Ap) P = paired in time and space peak prediction Coefficient of determination Pi = prediction at time and location i; Oi =observation at time and location i; =arithmetic average of Pi, i=1,2,…, N; =arithmetic average of Oi, i=1,2,…,N Normalized Mean Error (NME) Reported as % Root Mean Square Error (RMSE) Fractional Gross Error (FE)

12 Statistical measures used in model performance evaluation Measure Mathematical Expression Notation Mean Absolute Gross Error (MAGE) Mean Normalized Gross Error (MNGE); Mean Normalized Error (MNE) Reported as % Mean Bias (MB) Mean Normalized Bias (MNB) Reported as % Mean Fractionalized Bias (Fractional Bias, MFB) Reported as % Normalized Mean Bias (NMB) Reported as %

13 Statistical measures used in model performance evaluation In addition… –Mean observation –Mean prediction –Standard deviation (SD) of observation –Standard deviation (SD) of prediction –Correlation variance

14 Expanded Model Evaluation Software to include… Ambient data evaluation for air quality monitoring networks: –IMPROVE (24-Hour average PM) –CASTNet (Weekly average PM & Gas) –STN (24-Hour average PM) –AQS (Hourly Gas) –NADP (weekly total deposition) –SEARCH 17 statistical measures in model performance evaluation All performance metrics can be analyzed in an automated process for model and data selected by: · allsite_daily · onesite_daily · allsite_yearly · onesite_monthly · allsite_monthly · onesite_yearly

15 Facilitate model evaluation. Benefit from shared development of tool. Share monitoring data. UCR software available at website: Community Model Evaluation Tool?

16 WRAP 1996 Evaluation, CMAQ v4.3

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20 WRAP 1996 cases in progress New fugitive dust emissions model New NH3 emissions model Actual Prescribed & Ag burning emissions 2002 annuals simulations being developed.

21 VISTAS Model 12 km Domain 34 L MM5 by Olerud 1999 NEI CMAQ v3

22 VISTAS Sensitivity Cases 3 Episodes: Jan 2002, July 1999, July 2001 Sensitivity Cases –MM5 MRF and ETA-MY, –PBL height, Kz_min, Layer collapsing –CB –SAPRC99 –CMAQ-AIM –GEO-CHEM for BC –NH3 emissions

23 VISTAS Key Findings NO3 over predictions in winter, under predictions in summer. –Thorton et al  N2O5 had small benefit, July MNB increased from –50% to –45% SO4 performance reasonably good Problems with PBL height –Kz_min = 1 improved performance –Investigating PBL height corrections Minor differences in 19 vs 34 layers

24 Benchmarks Athlon MP 2000 (1.66 GHz) Opteron 246 (2.0 GHz) –32 bit code –64 bit code Compare 1, 4 and 8 CPUs. Ported CMAQ to the 64 bit SuSE –Pointers & memory allocation for 64 bit

25 Test Case for benchmarks VISTAS 12 km domain –168 x 177 x 19 layers Benchmarks for CMAQ 4.3 One day simulation, CB4, MEBI Single CPU run time hour:minutes –Athlon 2 GHz: 14:10 –Opteron 32bit 2 GHz: 12:49 –Opteron 64 bit 2 GHz: 10:57

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29 Optimal Cost Configuration Small cluster < 8 CPUs use Athlon Large cluster >16 CPUs use Opterons?

30 Conclusions Major Improvements in WRAP 1996 Model WRAP 2002 annual modeling underway VISTAS Sensitivity Studies – still have problems in NO3 –Need better NH3 inventory –Need more attention to PBL heights in MM5 Community model evaluation tool?


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