1. Comparison between Forecasting and Retrospective Air Quality Simulations of 2006 TexAQS-II Daewon W. Byun* D.-G. Lee, F. Ngan, H.-C. Kim, B. Czader Arastoo Biazar (UAH) B. Rappenglueck, B. Lefer Postdocs & Students University of Houston Institute for Multidimensional Air Quality Studies * Present Affiliation: Air Resources Laboratory, NOAA

2. Governing Equation and Inputs Affecting AQF Results Quality of forecasting depends on both model formulations and inputs. For AQF, daily meteorology is the main driver but IC, BC, and emissions can affect forecasting quality as well. Demonstrate how AQF can be affected by wind & cloud (photolysis), emissions, and IC

3. Reduce AQ modeling biases by improving meteorology through data assimilation? O3 averaged over the CAMS sites in the HGB domain for Aug. 16-Sept. 14, 2006 (upper) and Sept. 15 – Oct. 6, 2006 period (lower). High bgrnd O3, southerly flow Too little cloud Low pm PBL over G-Bay Bogus LA plume Missing rainMissing rain, lingering effects Rain/cloud not correct Emissions & Flow direction (IC) (TS) (EI) (TS) (BC?) (PBL/SST?) O3 (MET?)

4. D36 (36km)E12 (12km)E04 (4km) Horizontal grid 157 * 127175 * 136175 * 175 Initialization NAM dataNest-down of D36 + weighted first guess + obj analysis Nest-down of E12 + weighted first guess + obj analysis Land use USGS 24UT-CSR LULC + TFS LULC Microphysics Simple Ice Radiation scheme RTTM Land surface model UH-modified NOAH PBL scheme UH-modified MRF Convective scheme GrellK-F Nudging Grid nudgingGrid nudging for U/V/T/Q (SFC nudging for U/V) Grid & SFC nudging for U/V Simulation period: August 23 – September 9, 2006 Model was initialized every 2 days and each run was 54 hours long. The first 6 hours was not used for air quality modeling. Thick lines: MM5 domain, Thin lines: CMAQ domain Simulation period: August 23 – September 9, 2006 Model was initialized every 2 days and each run was 54 hours long. The first 6 hours was not used for air quality modeling. Thick lines: MM5 domain, Thin lines: CMAQ domain D36 E12 E04

5. MUltiscale Nest-down Data Assimilation System (MUNDAS) CAMS: surface met., only in TX, concentrating in big city MADIS: surface – METARS & Buoy etc. upper level – NPN, aircraft sounding & radiosonde  Utilizes existing objective analysis and nudging tools in the MM5 system.  Incorporate extensive OBS available in the simulated domain for the retrospective simulation of the TexAQS-II period.  Update SFC characteristics inputs in MM5 with satellite observation-based land use/land cover (UT-CSR and TFS) and sea surface temperature (GOES).  Utilizes existing objective analysis and nudging tools in the MM5 system.  Incorporate extensive OBS available in the simulated domain for the retrospective simulation of the TexAQS-II period.  Update SFC characteristics inputs in MM5 with satellite observation-based land use/land cover (UT-CSR and TFS) and sea surface temperature (GOES). Difference plot of vegetation fraction Updated - Original + Updated LULC for E04

6. Forecast vs. Retrospective Met – impact on AQF Westerly wind component averaged over the CAMS sites in the HGB domain for Aug. 16-Sept. 14, 2006 (upper) and Sept. 15 – Oct. 6, 2006 period (lower). U (westerly) component Missing rainMissing rain, lingering effects Rain/cloud not correct Flow direction Too little cloud / wrong place Flow direction/speed

7. Forecasting vs. Retrospective Meteorology Relative humidity averaged over the CAMS sites in the HGB domain for Aug. 16-Sept. 14, 2006 (upper) and Sept. 15 – Oct. 6, 2006 period (lower). Relative Humidity (RH) Nighttime humidity biases – related with thick model layer daytime humidity – very little bias except for the rainy days

8. Forecasting vs. RetrospectiveMM5 Simulation 1.5 m temperature averaged over the CAMS sites in the HGB domain for Aug. 16-Sept. 14, 2006 (upper) and Sept. 15 – Oct. 6, 2006 period (lower). 1.5 m temperature improved Worse… rain Still problematic..rain improved Rain? improved No improvement Cloud problem?

9. Impact of assimilated wind on CMAQ simulation: Episode August 31, 2006 O3 spatial plot at 15 CST August 31, 2006 (shaded: CMAQ, circle: OBS) Time series of wind vector at CAMS sites on 8/31 (black: MM5, red: OBS) AQF AQF O3 was located too far southwest and the intensity was less than observed. AQF failed to predict ozone peak since too strong northerly and delay of bay breeze onset were simulated in MM5. In assimilated run, the light easterly wind was predicted that match better with OBS and the bay breeze was generated earlier in the afternoon. AQF O3 was located too far southwest and the intensity was less than observed. AQF failed to predict ozone peak since too strong northerly and delay of bay breeze onset were simulated in MM5. In assimilated run, the light easterly wind was predicted that match better with OBS and the bay breeze was generated earlier in the afternoon. assimilated AQFassimilated

10. Ozone comparison: AQF vs RS_m for 8/30~9/5/2006 After met is improved, then consider emissions uncertainty Tested emissions sensitivity for 8/30~9/5/2006 Modeling cases (1) AQF (base case) : AQF met + AQF emission (2) RS_m : assimilated met + AQF emission (3) RS_m+e : assimilated met + BEMR emission (4) RS_m+e_adjusted : assimilated met + BEMR emission with adjustment RS_m (orange) case better than AQF in general (captured high peak ozone on 8/31, 9/1 ) but overpredictions on other days AQF: based on 2000 imputed emissions for 2006 TexAQS-II and suspected too high emissions used

11. Comparison b/w AQF and BEMR emissions for HGB 8 counties BEMR emission, compared to AQF Point VOC & NOx: ~50% decreased Mobile VOC & NOx: ~15% decreased Nonroad VOC: 30% decreased Nonroad NOx: almost same Area VOC: ~10% increased Area NOx: almost same CO from all sources: ~10% decreased BEMR from all sources in HGB area VOC: reduced by ~200 ton/day NOx: reduced by ~350 ton/day Frequent high ozone episode in HGB area may be attributed to Huge amount of VOC from petrochemical industries + NOx from vehicles

12. Ozone comparison: AQF vs RS_m vs RS_m+e for 8/30~9/5/2006 RS_m+e (green), compared to RS_m updated emissions for 2006 used In general, lower ozone peak than RS_m better simulated on ordinary days (9/2, 9/3) but, underpredicted on high ozone episode days (8/31, 9/1) worse in simulating peak ozone events RS_m (orange), compared to AQF better than AQF, in general captured high peak ozone on 8/31, 9/1 overpredictions on other days but, based on 2000 imputed emissions

13. ETH, ozone comparison in supersites: RS_m vs RS_m+e for 8/30~9/5/2006 Lynchburg UH MT Lynchburg UH MT HRM- 3 TexAQS 2000: HRVOC(e.g. ETH, OLE) are responsible for THOE (Transient High Ozone Episode) in HGB area ETH RS_m: highly overpredicted RS_m+e: better simulated slight underprediction Ozone in RS_m+e (orange) lower ozone peak than RS_m better on ordinary days underpredicted high peak O 3 worse simulating high peak O 3 Suggesting BEMR emissions adjustment on high ozone episode days (8/31~9/1)

14. BEMR adjustments & its impacts on CMAQ ozone predictions: RS_m+e vs RS_m+e_adjusted BEMR emissions adjustments (1) Point source OLE emission in the Houston Ship Channel by 12 times for layers 1-5 (Cuclis, 2009) (2) Mobile source CO emissions in the HGB area by 0.5 times (TCEQ, 2007) (3) Mobile source NOx emissions in the HGB area by 1.5 times (TCEQ, 2007) HSC, Urban O 3 increased by 10~20ppb match better with obs. peak West downwind urban underprediction possibly due to wind

15. Ozone comparison: RS_m+e vs RS_m+e_adjusted R=0.70 R=0.73 RS_m+e_adjusted (orange), compared to RS_m+e HSC, Urban, downwind urban (N,W,S): well predicted high peak ozone West downwind urban: still underpredicted peak ozone HSC, Urban, Downwind of UrbanHouston Ship Channel Houston Urban Downwind of Urban

16. Clear sky on August 31 2006 In E04 domain, model generated too much clouds associated with the low- pressure system (inherited from coarse domain E12/D36). Cloudiness suppresses the photochemical process of ozone over Dallas area. Clear sky on August 31 2006 In E04 domain, model generated too much clouds associated with the low- pressure system (inherited from coarse domain E12/D36). Cloudiness suppresses the photochemical process of ozone over Dallas area. E12 E04 Cloud fraction from MM5 E04 Cloud fraction from GOES satellite prepared by University of Alabama, Huntsville (UAH) Difference plot of O3 at 11 CST on 8/31 2006 CMAQ run with cloud from MM5 – GOES satellite Observed Cloud fraction What can we do with wrong cloud & precipitation forecasting????  Test with satellite-obs clouds to modulate J-value

17. 1-hr Precip. from MM5 (shaded) & CMAS observation (circle) at 13 CST on August 23, 2006 O3 spatial plot at 15 CST on August 23, 2006 Over-prediction of O3 due to inaccurate precipitation simulation from MM5. O3 spatial plot at 15 CST on August 23, 2006 Over-prediction of O3 due to inaccurate precipitation simulation from MM5.

18. Difference plot of O3 at 15 CST on Aug. 23rd 2006 CMAQ run with cloud from MM5 – GOES satellite Ozone difference can be easily ~ 20 ppb! Observed Cloud fraction from GOES satellite (UAH) Cloud fraction from MM5

19. IC Example NorthwestDeer Park Bayland Galveston AQF Clean IC

20. IC Example NorthwestDeer Park Bayland Galveston AQF Clean IC IC for August 24 was too high due to missed Precipitation events in the met simulations. Fixing IC corrects overprediction problems

21. Conclusive Remarks So many things can go wrong leading to bad air quality forecasting Investigation of causes of bad forecasting may lead to future improvements First, look at the impact of meteorological forecasting (winds, clouds, precipitation, temperature, humidity …) If met forecasting was quite wrong previous day, consider “reinitializing” before next forecasting (not easy!) Uncertainty in emissions need to be improved but most likely for next forecasting season Need to prepare for real-time data assimilation tools, methods, and data (e.g., intermittent emissions from forest fire, volcanic ashes, long-range transport) Continued improvement of model algorithms