Evaluating temporal and spatial OEvaluating temporal and spatial O33 and PMand PM2.52.5 patterns simulated during an patterns simulated during an

annual CMAQ application over the annual CMAQ application over the continental U.S.continental U.S.

C. HogrefeC. Hogrefe11, J.M. Jones, J.M. Jones11, E. Gego, E. Gego22, P.S. Porter, P.S. Porter33, J. Irwin, J. Irwin44,,A. GillilandA. Gilliland44, and S.T. Rao, and S.T. Rao44

11Atmospheric Sciences Research Center, SUNY at Albany, Albany, Atmospheric Sciences Research Center, SUNY at Albany, Albany, NY, USANY, USA22University Corporation for Atmospheric Research, Idaho Falls, ID, University Corporation for Atmospheric Research, Idaho Falls, ID, USAUSA33University of Idaho, Idaho Falls, ID, USAUniversity of Idaho, Idaho Falls, ID, USA44NOAA Atmospheric Sciences Modeling Division, On Assignment NOAA Atmospheric Sciences Modeling Division, On Assignment to the U.S. Environmental Protection Agency, Research Triangle to the U.S. Environmental Protection Agency, Research Triangle Park, NC, USAPark, NC, USA

Models-3 Users’ Workshop, October 18, 2004, RTP

ObjectivesObjectives

Determine and compare temporal and spatial Determine and compare temporal and spatial features in time series of observations and features in time series of observations and model predictionsmodel predictions

Attempt to link behavior of different Attempt to link behavior of different components of the modeling system components of the modeling system (meteorological model, emissions, AQM)(meteorological model, emissions, AQM)

Analyze predicted and observed Analyze predicted and observed weekend/weekday Oweekend/weekday O33 concentration concentration differencesdifferences

DatabaseDatabase 2001 Annual CMAQ and REMSAD simulation 2001 Annual CMAQ and REMSAD simulation

performed by EPA with MM5/SMOKE inputsperformed 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 Hourly surface meteorological observations from NCAR’s TDL datasetNCAR’s TDL dataset

Hourly OHourly O33 and PM and PM2.52.5 (TEOM) observations from (TEOM) observations from AQSAQS

Daily average total and speciated PMDaily average total and speciated PM2.52.5 from STN from STN and IMPROVEand IMPROVE

Weekly PMWeekly PM2.52.5 from CASTNet from CASTNet

Present analysis for the Eastern U.S.Present analysis for the Eastern U.S.

• 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

Decay of Correlation Between Time Series of the Different Temporal Components for Ozone (left) and PM2.5 (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

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

Correlations Between Observed and CMAQ-Predicted Component Time Series for May 1 – September 30, 2001

O3 (left) and PM2.5 (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

Correlations between different temporal components embedded in Correlations between different temporal components embedded in hourlyhourly time series of time series of observed and predicted temperature, wind speed, ozone and total PM observed and predicted temperature, wind speed, ozone and total PM

2.52.5. Median values . Median values

are shown for each network/variable. Time series are based on the entire year 2001are shown for each network/variable. Time series are based on the entire year 2001

#Sites#Sites Intra-dayIntra-day DiurnalDiurnal SynopticSynoptic BaselineBaseline

Temperature Temperature TDL/MM5TDL/MM5 738738 0.180.18 0.900.90 0.950.95 0.990.99

Wind Speed Wind Speed TDL/MM5TDL/MM5 735735 0.020.02 0.600.60 0.840.84 0.900.90

OO33 AQS/CMAQ AQS/CMAQ 193193 0.070.07 0.700.70 0.640.64 0.870.87

PMPM2.52.5 TEOM TEOM 6767

CMAQCMAQ REMSADREMSAD 0.010.01 0.030.03 0.250.25 0.250.25 0.700.70 0.630.63 0.040.04 0.100.10

• Correlations are lowest on the intra-day time scale and highest on the synoptic and baseline time scale

• Exception: Baseline for PM2.5 measured by TEOM – higher sampling losses in winter

Correlations between different synoptic and baseline components embedded in time series of Correlations between different synoptic and baseline components embedded in time series of observed and predicted PM observed and predicted PM

2.52.5 from different networks. Median values are shown for each from different networks. Median values are shown for each

network/variable. Time series are based on the entire year 2001network/variable. Time series are based on the entire year 2001

#Sites#Sites SynopticSynoptic BaselineBaseline

CMAQCMAQ REMSADREMSAD CMAQCMAQ REMSADREMSAD

PMPM2.52.5 FRM (daily) FRM (daily) 938938 0.680.68 0.650.65 0.600.60 0.510.51

PMPM2.52.5 STN (daily) STN (daily) 2525 0.600.60 0.630.63 0.380.38 0.350.35

SOSO44 Improve (daily) Improve (daily) 4444 0.770.77 0.700.70 0.890.89 0.770.77

SOSO44 CASTnet (weekly) CASTnet (weekly) 4848 0.850.85 0.720.72 0.940.94 0.880.88

SOSO44 STN (daily) STN (daily) 2323 0.720.72 0.700.70 0.850.85 0.740.74

NONO33 Improve (daily) Improve (daily) 4444 0.460.46 0.540.54 0.880.88 0.780.78

NONO33 CASTnet (weekly) CASTnet (weekly) 4848 0.510.51 0.460.46 0.890.89 0.830.83

NONO33 STN (daily) STN (daily) 2323 0.390.39 0.420.42 0.830.83 0.660.66

NHNH44 CASTnet (weekly) CASTnet (weekly) 4848 0.710.71 0.720.72 0.550.55 0.450.45

NHNH44 STN (daily) STN (daily) 2323 0.630.63 0.660.66 0.520.52 0.370.37

ECEC STN (daily) STN (daily) 2323 0.410.41 0.390.39 0.150.15 0.320.32

OCOC STN (daily) STN (daily) 2222 0.480.48 0.550.55 0.240.24 0.280.28

CrustalCrustal STN (daily) STN (daily) 2323 0.340.34 0.290.29 -0.35-0.35 -0.39-0.39

Why are the baseline correlations low for EC? Examine annual baseline time series for PM2.5 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 PM2.5 emissions

Correlation between Baselines of PM2.5 Emissions and CMAQ EC Concentrations, January 1 – December 31, 2001

Summary Spectral Summary Spectral DecompositionDecomposition

Models do not capture high-frequency Models do not capture high-frequency fluctuations in meteorological variables, Ofluctuations in meteorological variables, O33, , and PMand PM2.52.5

Spatial correlation structures are captured by Spatial correlation structures are captured by the models with the exception of the intra-day the models with the exception of the intra-day component and the diurnal component for component and the diurnal component for PMPM2.52.5

For temperature, wind speed, OFor temperature, wind speed, O33, SO, SO44 and and NONO33, correlations are highest on the synoptic , correlations are highest on the synoptic and baseline time scalesand baseline time scales

Relatively low baseline correlations for NHRelatively low baseline correlations for NH44, , EC and A25 may point to problems with the EC and A25 may point to problems with the seasonal characterization of emissionsseasonal characterization of emissions

Is There a Weekday/Weekend Cycle in Observed and Predicted O3 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

Weekday/Weekend Effect in The Emission Input Files

Day of Week for Weekly Maximum (bottom) and Minimum (top) NOx Emissions

Day of Week for Weekly Maximum (bottom) and Minimum (top) VOC Emissions

Correlation Between Temperature and VOC Emissions, May – September 2001

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)

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

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

SummarySummary Correlations between observed and simulated Correlations between observed and simulated

component time series tend to be highest on component time series tend to be highest on the synoptic and baseline time scales except the synoptic and baseline time scales except for PM species strongly influenced by for PM species strongly influenced by seasonal variations in emissionsseasonal variations in emissions

Weekend/weekday differences exist for OWeekend/weekday differences exist for O33 during the summer of 2001 in both during the summer of 2001 in both observations and model predictions, but observations and model predictions, but appear to be mainly attributable to appear to be mainly attributable to fluctuations in meteorologyfluctuations in meteorology

To further compare observed and predicted To further compare observed and predicted weekend/weekday differences, methods to weekend/weekday differences, methods to account for meteorological fluctuations need account for meteorological fluctuations need to be developedto be developedAlthough this work was reviewed by EPA and approved for publication, it may not necessarily

reflect official Agency policy.