WRAP 2002 Visibility Modeling: Emission, Meteorology Inputs and CMAQ Performance Evaluation

Transcription

1 WRAP 2002 Visibility Modeling: Emission, Meteorology Inputs and CMAQ Performance Evaluation Gail Tonnesen, Bo Wang, Chao-Jung Chien, Zion Wang, Mohammad Omary University of California, Riverside Zac Adelman, Andy Holland University of North Carolina Ralph Morris et al. ENVIRON Corporation Int., Novato, CA WRAP Attribution of Haze Meeting, Denver, CO July 22, 2004

2 Summary of RMC 2002 Modeling Annual MM5 Simulations run at the RMC Emissions processed with SMOKE Preliminary 2002 Scenario C used here. CMAQ version 4.3 (released October 2003) Data summaries, QA, results are posted on the RMC web page:

3 MM5 Modeling Domain (36 & 12 km) National RPO grid Lambert conic Projection Center: -97 o, 40 o True lat: 33 o, 45 o MM5 domain 36 km: (165, 129, 34) 12 km: (220, 199, 34) 24-category USGS data 36 km: 10 min. (~19 km) 12 km: 5 min. (~9 km)

4 MM5 Physics Physics Option Configuration Configure.user Microphysics Reisner2 (with graupel) IMPHYS = 7 Cumulus Scheme Kain-Fritsch ICUPA = 6 PBL Pleim-Chang (ACM) IBLTYP = 7 Radiation RRTM FRAD = 4 Land-surface model Pleim-Xiu ISOIL = 3 Shallow Convection No ISHALLO = 0 Snow Cover Effect Simple snow model ISNOW = 2 Thermal Roughness Garrat IZ0TOPT = 1 Varying SST Yes ISSTVAR = 1 Time step 90 seconds (PX uses an internal timestep of 40 seconds)

5 Subdomains for 36/12-km Model Evaluation 1 = Pacific NW 2 = SW 3 = North 4 = Desert SW 5 = CenrapN 6 = CenrapS 7 = Great Lakes 8 = Ohio Valley 9 = SE 10 = NE 11 = MidAtlantic

6 Evaluation Review Evaluation Methodology Synoptic Evaluation Statistical Evaluation using METSTAT and surface data WS, WD, T, RH Evaluation against upper-air obs Statistics: Absolute Bias and Error, RMSE, IOA (Index of Agreement) Evaluation Datasets: NCAR dataset ds472 airport surface met observations Twice-Daily Upper-Air Profile Obs (~120 in US) Temperature Moisture

7 METSTAT Evaluation Package Statistics: Absolute Bias and Error, RMSE, IOA Daily and, where appropriate, hourly evaluation Statistical Performance Benchmarks Based on an analysis of > 30 MM5 and RAMS runs Not meant as a pass/fail test, but to put modeling results into perspective Wind Speed Wind Direction Temperature Humidity RMSE 2 m/s Mean Bias ±0.5m/s ±10 ±0.5K ±1g/kg Index of Agreement Gross Error 30 2K 2g/kg

8 Evaluation of 36-km WRAP MM5 Results Model performed reasonably well for eastern subdomains, but not the west (WRAP region) General cool moist bias in Western US Difficulty with resolving Western US orography? May get better performance with higher resolution Pleim-Xiu scheme optimized more for eastern US? More optimization needed for desert and rocky ground? MM5 performs better in winter than in summer Weaker forcing in summer July 2002 Desert SW subdomain exhibits low temperature and high humidity bias 2002 MM5 Model Evaluation 12 vs. 36 km Results Chris Emery, Yiqin Jia, Sue Kemball-Cook, and Ralph Morris (ENVIRON International Corporation) & Zion Wang (UCR CE-CERT), Western Regional Air Partnership (WRAP) National RPO Meeting, May 25, 2004

9 WRAP 36km/12km July Wind Performance Comparison 120 Wind Direction Error (degrees) SW PacNW DesertSW 20 North Wind Speed RMSE (m/s) Benchmark 12 km Subdomains MM5/RAMS Runs 36 km Subdomains

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11 WRAP 36km/12km July Temperature Performance Comparison 4.5 Desert SW 4 North 3.5 Temeprature Error (K) DesertSW SW North SW PacNW 3 PacNW Temperature Bias (K) Benchmark 12 km Subdomain MM5/RAMS Runs 36 km Subdomains

12 WRAP 36km/12km July Humidity Performance Comparison 5 4 Humidity Error (g/kg) PacNW 3 2 SW North DesertSW Humidity Bias (g/kg) Benchmark 12km Subdomains MM5/RAMS Runs 36 km Subdomains

13 MM5 Implications for AoH The RMC is continuing to test alternative MM5 configurations to be completed at the end of Expect some reduction in bias &error in the WRAP states, however even in the best case we will have error & bias in MM5 that must be considered when using CMAQ for source attribution.

14 Emissions Inventory Summary Preliminary 2002 Scenario C based on the 1996 NEI, grown to 2002, with many updates by WRAP contractors and other RPOs. Processed for CMAQ using SMOKE. Extensive QA plots on the web page Both SMOKE QA and post-smoke QA

15 Emissions Sources by Category & RPO Region Source WRAP CENRAP VISTAS LADCO MANE-VU Mexico Area Pecahn - v1 Pechan - v1 96 NEI (grown) 96 NEI (grown) 96 NEI (grown) BRAVO Point Pecahn - v1 Pecahn - v1 96 NEI (grown) 96 NEI (grown) 96 NEI (grown) BRAVO Paved Road Dust ENVIRON ENVIRON ENVIRON ENVIRON ENVIRON BRAVO Seasonal, Interpolation between 1996 and 2018 Unpaved Road Dust ENVIRON ENVIRON ENVIRON ENVIRON ENVIRON BRAVO Seasonal, Interpolation between 1996 and 2018 On Road Mobile ENVIRON 1999 NEI 96 NEI (grown) 1999 NEI 1999 NEI BRAVO VMT VMT VMT VMT Off Road Mobile ENVIRON 96 NEI (grown) 96 NEI (grown) 96 NEI (grown) 96 NEI (grown) BRAVO Biogenic VISTAS VISTAS VISTAS VISTAS VISTAS VISTAS Ag Fires Air Sci. (2018 base) Non included in the Area included in the Area included in the Area Non Rx Fires Air Sci. (2002) Non included in the Area included in the Area included in the Area Non Wild Fires Air Sci. (2002) Non Non Non Non Non

16 WRAP 2002 Annual NOx Emissions Area Biogenic On Road Non Road Road Dust Point Rx Fire Ag Fire Wildfire Offshore

17 2002 WRAP NOx Emissions by Source & State [Tons/Yr] Arizona California Colorado Idaho Montana Nevada New Mexico North Dakota Oregon South Dakota Utah Washington Wyoming 0 Ag Fire Rx Fire Wildfire Area Point Nonroad Onroad

18 WRAP 2002 Annual SO2 Emissions Area Biogenic On Road Non Road Road Dust Point Rx Fire Ag Fire Wildfire Offshore

19 Onroad 2002 WRAP SO2 Emissions by Source & State Ag Fire Rx Fire 3.00E+05 Wildfire 2.50E+05 Area Nonroad 2.00E+05 Point 1.50E E E+04 Arizona California Colorado Idaho Montana Nevada New Mexico North Dakota Oregon South Dakota Utah Washington Wyoming 0.00E+00 [Tons/Yr]

20 2002 WRAP NH3 Emissions by Source Category 2.50E+05 Nonroad 2.00E+05 Ag Fire Rx Fire Point 1.50E E+05 Onroad Wildfire Area Tons/Yr 5.00E+04 Arizona California Colorado Idaho Montana 0.00E+00 Nevada New Mexico North Dakota Oregon South Dakota Utah Washington Wyoming

21 Emissions Summary Preliminary 2002 EI Used here. Updates for final 2002 EI will include: New EI data from other RPOs and Canada 2002 NEI to replace grown 1996 NEI Reprocess in SMOKE with final MM5 All final inputs ready now except Canada & MM5

22 CMAQ Simulations CMAQ v km grid, 112x148x19 Annual Run CB4 chemistry Evaluated using: IMPROVE, CASTNet, NADP, STN, AIR/AQS

23 PM Performance Criteria Guidance from EPA not yet ready: Difficult to assert that model is adequate. Therefore, we use a variety of ad hoc performance goals and benchmarks to display CMAQ results. We completed a variety of analyses: Compute over 20 performance metrics Scatter-plots & time-series plots Soccer plots Bugle plots

24 Goal of Model Evaluation We completed a variety of analyses: Compute over 20 performance metrics Scatter-plots & time-series plots Soccer plots Bugle plots Goal is to decide whether we have enough confidence to use the model for AoH: Is this a valid application of the model?

25 Soccer Goal Plots Plot error as as a function of bias. Ad hoc performance goal: 15% bias, 35% error based on O3 modeling goals. Larger error & bias are observed among different PM data methods and monitoring networks. Performance benchmark: 30% bias, 70% error (2x performance goals) PM models can achieve this level in many cases.

26 Annual CMAQ vs IMPROVE

27 Spring Summer Fall Winter

28 Annual CMAQ vs CASTNet

29 Spring Summer Fall Winter

30 Annual CMAQ vs STN

31 Spring Summer Fall Winter

32 Annual CMAQ vs NADP

33 Spring Summer Fall Winter

34 Performance Goals and Criteria - Proposed by Jim Boylan Based on FE and FB calculations Vary as a function of species concentrations Goals: FE +50% and FB ±30% Criteria: FE +75% and FB ±60% Less abundant species should have less stringent performance goals and criteria

35 Performance Goals and Criteria - Proposed by Jim Boylan PM Performance Goals FB ± 170e 0.5( C o + C 0.5µ g / m m 3 ) + 30 FE 150e 0.5( C o + C 0.75µ g / m m 3 ) + 50 Proposed PM Performance Criteria FE FB 125e ± 140e 0.5( C o + C 0.75µ g / m 0.5( C o m 3 ) + C 0.5µ g / m + m 3 )

36 Monthly SO4 Fractional Bias SO4 Fractional Bias (%) Average Concentration (ug/m3) IMPROVE CASTNet STN (+) Goal (-) Goal (+) Criteria (-) Criteria

37 Monthly SO4 Fractional Error SO4 Fractional Error (%) IMPROVE CASTNet STN Goal Criteria Average Concentration (ug/m3)

38 Monthly NO3 Fractional Bias NO3 Fractional Bias (%) Average Concentration (ug/m3) IMPROVE CASTNet STN (+) Goal (-) Goal (+) Criteria (-) Criteria

39 Monthly NO3 Fractional Error NO3 Fractional Error (%) IMPROVE CASTNet STN Goal Criteria Average Concentration (ug/m3)

40 Monthly NH4 Fractional Bias NH4 Fractional Bias (%) CASTNet STN (+) Goal (-) Goal (+) Criteria (-) Criteria Average Concentration (ug/m3)

41 Monthly NH4 Fractional Error NH4 Fractional Error (%) CASTNet STN Goal Criteria Average Concentration (ug/m3)

42 Monthly OC Fractional Bias OC Fractional Bias (%) IMPROVE STN (+) Goal (-) Goal (+) Criteria (-) Criteria Average Concentration (ug/m3)

43 Monthly OC Fractional Error OC Fractional Error (%) IMPROVE STN Goal Criteria Average Concentration (ug/m3)

44 Monthly EC Fractional Bias EC Fractional Bias (%) IMPROVE STN (+) Goal (-) Goal (+) Criteria (-) Criteria Average Concentration (ug/m3)

45 Monthly EC Fractional Error EC Fractional Error (%) IMPROVE STN Goal Criteria Average Concentration (ug/m3)

46 Monthly PM25 Fractional Bias PM25 Fractional Bias (%) IMPROVE STN (+) Goal (-) Goal (+) Criteria (-) Criteria Average Concentration (ug/m3)

47 Monthly PM25 Fractional Error 200 PM25 IMPROVE Fractional Error (%) STN Goal Criteria Average Concentration (ug/m3)

48 CMAQ & EI Versions TSSA results are run in CMAQ v4.4 with emissions version Preliminary 2002 C Performance evaluation used CMAQ 4.3 Previous CMAQ runs used CMAQ 4.3 with Preliminary 2002 B emissions (no fires)

49 CMAQ v4.3 & v4.4 versus IMPROVE July

50 CMAQ Ozone Performance CMAQ v4.3 Mean fractional bias (no filter) January +25% MFB July 20% mean MFB Slightly worse January O3 performance in v4.4

51 CMAQ Emissions B & C versus IMPROVE Summer

52 Issues for AoH Is this set of Emissions/MM5/CMAQ adequate for studying AoH? Analysis of CMAQ performance on best & worst days still in progress: However, we expect CMAQ will tend to over predict lows & under predict highs. Should we use CMAQ results unpaired in time?

53 Options for future work Continue CMAQ source apportionment with current data sets. Wait for new MM5 and emissions. Investigate other CMAQ configurations: Unlikely to see large improvements