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Georgia Institute of Technology Evaluation of CMAQ with FAQS Episode of August 11 th -20 th, 2000 Yongtao Hu, M. Talat Odman, Maudood Khan and Armistead.

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Presentation on theme: "Georgia Institute of Technology Evaluation of CMAQ with FAQS Episode of August 11 th -20 th, 2000 Yongtao Hu, M. Talat Odman, Maudood Khan and Armistead."— Presentation transcript:

1 Georgia Institute of Technology Evaluation of CMAQ with FAQS Episode of August 11 th -20 th, 2000 Yongtao Hu, M. Talat Odman, Maudood Khan and Armistead (Ted) Russell October 29th, 2003

2 Georgia Institute of Technology 36-km 4-km 12-km Fall line Air Quality Study

3 Georgia Institute of Technology Air Quality Modeling System MM5 v3.5.3, Using ETA analysis data and ADP observational data with 4DDA, MRF PBL and OSU LSM SMOKE v1.5b, Using FAQS2000 Inventory which is based on FAQS investigations and NET99, CEM data from EPA, Spatial Surrogates data based on Census 2000, BEIS3 with BELD3 data and MOBIL6 CMAQ v4.2.2, Using SAPRC99 with MEBI solver, PPM for advections, Eddy Diffusion for vertical mixing, None of PinG

4 Georgia Institute of Technology FAQS Episode of August 11th-20th, 2000 CMAQ Daily Peak 1-hour Ozone on August 17th: 12-km vs. 4-km Daily Peak 1-hour Ozone Observations on August 17 th (ppb):

5 Georgia Institute of Technology CMAQ Evaluation Basic Method: Compare the measurements of the pollutant concentration at site locations with the CMAQ concentration predictions of the matched model species at the corresponding grid cells Statistical Measures: Mean Bias Error (MBE), Root Mean Square Error (RMSE), Mean Normalized Bias (MNB), Mean Normalized Error (MNE), Normalized Mean Bias (NMB), Normalized Mean Error (NME), Fractional Bias (FB), Fractional Error (FE)

6 Georgia Institute of Technology Daily Bias: 12-km vs. 4-km Daily Errors: 12-km vs. 4-km Ozone Performance by Using 40ppb Cutoff

7 Georgia Institute of Technology Ozone Performance of the Sites in Georgia by Using 40ppb Cutoff Bias by Sites: 12-km vs. 4-kmErrors by Sites: 12-km vs. 4-km

8 Georgia Institute of Technology Scatters Plot of Ozone in 12-km Scatters Plot of NO in 12-km Measurements vs. Predictions: Ozone and NO

9 Georgia Institute of Technology Ozone Was Overestimated at Night Ozone Time Series at Columbus, GA Midnight Surface Ozone in 12-km

10 Georgia Institute of Technology NMB 12-km1am~ 6am7am~12am1pm~ 6pm7pm~12pm O NO NME 12-km1am~ 6am7am~12am1pm~ 6pm7pm~12pm O NO Performance in Diurnal Periods by No Cutoff

11 Georgia Institute of Technology Diurnal Change of Mixing Height: Vertical Mixing in Night Time Ozone Sinking Stable Boundary Layer Mixed Boundary Layer Free Atmosphere SunriseSunset Mixing Height Collapse MidnightNoon Residual Layer Surface Layer VOCs,NOx O3 NO O3 NO Emis NO Vdiff Kzz cutoff in CMAQ ~ Default: 1.0 m 2 /s, we tested: 0.3,0.1,0.03 and m 2 /s.

12 Georgia Institute of Technology SpeciesPeriodsPairs O31am~ 6am O37pm~12pm NO1am~ 6am NO7pm~12pm CO1am~ 6am CO7pm~12pm An optimal Kzz cutoff may lie between 0.1 and 1.0 m 2 /s Nighttime 12-km NMB by using different Kzz (m 2 /s) cutoff in CMAQ

13 Georgia Institute of Technology Underestimation of CO Emissions SpeciesPairs km km CO NMB by using different Kzz (m 2 /s) cutoff in CMAQ

14 Georgia Institute of Technology Overestimation of Isoprene Emissions at Other Rural Locations LocationPairs Urban Forest Other Rural Isoprene 36-km NMB by using different Kzz (m 2 /s) cutoff in CMAQ

15 Georgia Institute of Technology Localized, aberrantly high ozone peaks during the day found by reducing Kzz cutoff Ozone Time Series at Santa Rosa, FLLate Afternoon Surface Ozone in 12-km

16 Georgia Institute of Technology Dominate Landuse was used as the only landuse in the grid cell to derive Surface Met Parameters in MM5 MM5 OSU LSM Assign Water Dominate Grid Cell as Pure Water USGS Water Fractions in 12-km

17 Georgia Institute of Technology Land-based emissions are simulated as being trapped very near surface since of water cooling Grid Emissions (significant) Grid Concentrations (too high) Grid Met Parameters (much lower Kzz) Emissions from roads, treesStrong cooling effect of water surface

18 Georgia Institute of Technology Solutions to Artificial Surface Ozone Values Aggregating surface meteorological parameters from the fractional landuse for each grid cell in the meteorological modeling. Smoothing the Kzz in CMAQ for those grid cells over the mixed landuse with water by averaging the Kzz of this grid cell with its surrounding grid cells.

19 Georgia Institute of Technology A 9-points averaging method was used in CMAQ to smooth Kzz Ozone Time Series at Santa Rosa, FLLate Afternoon Surface Ozone in 12-km

20 Georgia Institute of Technology Summary A consistent bias between simulated and observed CO suggests that there is an underestimate in CO emissions. On the contrary, isoprene might be overestimated in rural locations Analysis suggests that an optimal Kzz cutoff may lie between 0.1 and 1.0 m 2 /s. Artificially high surface ozone values were found resulting from the OSU land surface model applied in MM5. A method of using 9-point averaging was proposed to fix this problem. CMAQ had a good ozone performance for FAQS episode of August 11 th- 20 th, 2000 at day time, but not at night time.


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