1. Evaluating temporal and spatial O 3 and PM 2.5 patterns simulated during an annual CMAQ application over the continental U.S. Evaluating temporal and spatial O 3 and PM 2.5 patterns simulated during an annual CMAQ application over the continental U.S. C. Hogrefe 1, J.M. Jones 1, E. Gego 2, P.S. Porter 3, J. Irwin 4, A. Gilliland 4, and S.T. Rao 4 1 Atmospheric Sciences Research Center, SUNY at Albany, Albany, NY, USA 2 University Corporation for Atmospheric Research, Idaho Falls, ID, USA 3 University of Idaho, Idaho Falls, ID, USA 4 NOAA Atmospheric Sciences Modeling Division, On Assignment to the U.S. Environmental Protection Agency, Research Triangle Park, NC, USA Models-3 Users’ Workshop, October 18, 2004, RTP

2. Objectives Determine and compare temporal and spatial features in time series of observations and model predictions Determine and compare temporal and spatial features in time series of observations and model predictions Attempt to link behavior of different components of the modeling system (meteorological model, emissions, AQM) Attempt to link behavior of different components of the modeling system (meteorological model, emissions, AQM) Analyze predicted and observed weekend/weekday O 3 concentration differences Analyze predicted and observed weekend/weekday O 3 concentration differences

3. Database 2001 Annual CMAQ and REMSAD simulation performed by EPA with MM5/SMOKE inputs 2001 Annual CMAQ and REMSAD simulation performed by EPA with MM5/SMOKE inputs 36 km grid over the continental U.S. 36 km grid over the continental U.S. Observations: Observations: Hourly surface meteorological observations from NCAR’s TDL dataset Hourly surface meteorological observations from NCAR’s TDL dataset Hourly O 3 and PM 2.5 (TEOM) observations from AQS Hourly O 3 and PM 2.5 (TEOM) observations from AQS Daily average total and speciated PM 2.5 from STN and IMPROVE Daily average total and speciated PM 2.5 from STN and IMPROVE Weekly PM 2.5 from CASTNet Weekly PM 2.5 from CASTNet Present analysis for the Eastern U.S. Present analysis for the Eastern U.S.

4. Time series are decomposed into components containing fluctuations on different time scales: Intra-day (ID): periods < 10 hours (hourly time series only) Diurnal (DU): periods 10 hours - 2 days (hourly time series only) Synoptic (SY): periods 2 days - 21 days Baseline (BL): periods > 21 days Scale separation is accomplished with an iterative moving average filter Correlations between observations and model predictions are computed for all time scales for different variables Scale Analysis of Hourly/Daily/Weekly Time Series

5. Decay of Correlation Between Time Series of the Different Temporal Components for Ozone (left) and PM 2.5 (right) as a Function of Distance for Observations and CMAQ/REMSAD Spatial correlations are strongest for the baseline components (strong periodicity) Models do not capture the decay of correlation between intra-day components in space

6. Correlations Between Observed and Predicted Component Time Series for May 1 – September 30, 2001 Temperature (left) and Wind Speed (right) Correlations for these meteorological variables are lowest on the intra-day time scale and highest on the synoptic and baseline time scale Correlations are lower for the diurnal amplitude than the hourly diurnal component

7. Correlations Between Observed and CMAQ-Predicted Component Time Series for May 1 – September 30, 2001 O 3 (left) and PM 2.5 (right) As for temperature and wind speed, correlations are lowest on the intra-day time scale and highest on the synoptic and baseline time scale

8. Correlations between different temporal components embedded in hourly time series of observed and predicted temperature, wind speed, ozone and total PM 2.5. Median values are shown for each network/variable. Time series are based on the entire year 2001 #SitesIntra-dayDiurnalSynopticBaseline Temperature TDL/MM5 7380.180.900.950.99 Wind Speed TDL/MM5 7350.020.600.840.90 O 3 AQS/CMAQ 1930.070.700.640.87 PM 2.5 TEOM 67 CMAQREMSAD 0.010.030.250.250.700.630.040.10 Correlations are lowest on the intra-day time scale and highest on the synoptic and baseline time scale Exception: Baseline for PM 2.5 measured by TEOM – higher sampling losses in winter

9. Correlations between different synoptic and baseline components embedded in time series of observed and predicted PM 2.5 from different networks. Median values are shown for each network/variable. Time series are based on the entire year 2001 #SitesSynopticBaseline CMAQREMSADCMAQREMSAD PM 2.5 FRM (daily) 9380.680.650.600.51 PM 2.5 STN (daily) 250.600.630.380.35 SO 4 Improve (daily) 44 0.770.700.890.77 SO 4 CASTnet (weekly) 48 0.850.720.940.88 SO 4 STN (daily) 23 0.720.700.850.74 NO 3 Improve (daily) 44 0.460.540.880.78 NO 3 CASTnet (weekly) 48 0.510.460.890.83 NO 3 STN (daily) 23 0.390.420.830.66 NH 4 CASTnet (weekly) 48 0.710.720.550.45 NH 4 STN (daily) 23 0.630.660.520.37 EC STN (daily) 23 0.410.390.150.32 OC STN (daily) 22 0.480.550.240.28 Crustal STN (daily) 23 0.340.29-0.35-0.39

10. Why are the baseline correlations low for EC? Examine annual baseline time series for PM 2.5 emissions and observed and simulated EC concentrations at two STN monitors (Decatur, GA, and Bronx, NY) Baseline of CMAQ/REMSAD EC concentrations tracks well with seasonal pattern of PM 2.5 emissions

11. Correlation between Baselines of PM 2.5 Emissions and CMAQ EC Concentrations, January 1 – December 31, 2001

12. Summary Spectral Decomposition Models do not capture high-frequency fluctuations in meteorological variables, O 3, and PM 2.5 Models do not capture high-frequency fluctuations in meteorological variables, O 3, and PM 2.5 Spatial correlation structures are captured by the models with the exception of the intra-day component and the diurnal component for PM 2.5 Spatial correlation structures are captured by the models with the exception of the intra-day component and the diurnal component for PM 2.5 For temperature, wind speed, O 3, SO 4 and NO 3, correlations are highest on the synoptic and baseline time scales For temperature, wind speed, O 3, SO 4 and NO 3, correlations are highest on the synoptic and baseline time scales Relatively low baseline correlations for NH 4, EC and A25 may point to problems with the seasonal characterization of emissions Relatively low baseline correlations for NH 4, EC and A25 may point to problems with the seasonal characterization of emissions

13. Is There a Weekday/Weekend Cycle in Observed and Predicted O 3 for the Summer of 2001? Does CMAQ Capture It? Observed/Predicted Weekend Minus Weekday Amplitude (ppb) Day of Week for Observed/Predicted Weekly Maximum/Minimum

14. Weekday/Weekend Effect in The Emission Input Files Day of Week for Weekly Maximum (bottom) and Minimum (top) NO x Emissions Day of Week for Weekly Maximum (bottom) and Minimum (top) VOC Emissions Correlation Between Temperature and VOC Emissions, May – September 2001

15. Is There also a Weekday/Weekend Cycle in Observed and Predicted T for the Summer of 2001? Does MM5 Capture It? Observed/Predicted Weekend Minus Weekday Amplitude (C) Day of Week for Observed Weekly Maximum/Minimum Summer 2001 (top) and 1991 – 2000 (bottom)

16. How Can We Separate the Effects of Meteorological and Emissions Variations? 1.Restrict analysis to single events with stagnant conditions (i.e. try to find an event with ‘constant’ meteorology) 2.Perform analysis for extended time periods (i.e. multiple summers) so the transient meteorological weekday/weekend effects are “averaged out”. Model simulations may not be available for such extended periods. Observed weekend minus weekday difference for summer 2001 (top) and summers 1991-2000 (bottom) 3.Develop methods to remove the effects of meteorological variations on ozone before performing weekend/weekday analysis. This could be applied to both observations and model predictions

17. Example: “Temperature-Adjust” Ozone for the Summer of 2001 Before Determining Weekday/Weekend Differences Correlation Between Daily Maximum Temperature and Ozone for May – September, 2001. Observations (top) and CMAQ (bottom) Day of Week for Observed/Predicted Weekly Maximum/Minimum Temperature-Adjusted Ozone Residuals

18. Summary Correlations between observed and simulated component time series tend to be highest on the synoptic and baseline time scales except for PM species strongly influenced by seasonal variations in emissions Correlations between observed and simulated component time series tend to be highest on the synoptic and baseline time scales except for PM species strongly influenced by seasonal variations in emissions Weekend/weekday differences exist for O 3 during the summer of 2001 in both observations and model predictions, but appear to be mainly attributable to fluctuations in meteorology Weekend/weekday differences exist for O 3 during the summer of 2001 in both observations and model predictions, but appear to be mainly attributable to fluctuations in meteorology To further compare observed and predicted weekend/weekday differences, methods to account for meteorological fluctuations need to be developed To further compare observed and predicted weekend/weekday differences, methods to account for meteorological fluctuations need to be developed Although this work was reviewed by EPA and approved for publication, it may not necessarily reflect official Agency policy.