East Texas Air Quality Forecasting Systems (ETAQ-F) Evaluation of Summer 2006 Simulations for TexAQS-II and Transition to Assessment Study Daewon W. Byun F. Ngan, X. Li, D. Lee, S. T. Kim, H.C. Kim, I.B. Oh, and F. Cheng Institute for Multi-dimensional Air Quality Studies (IMAQS) University of Houston (UH)

AQF Modeling Domains – F1 (June 2005 – Current)

2005/2006 UH AQF systems (F-1 & F-2) 1 CPU MCIP 36 km 1 st day SMOKE 36 km 1 st day 1 CPU MCIP 36 km 2 nd day SMOKE 36 km 2 nd day 1 CPU MCIP 12 km 1 st day MCIP 12 km 1 st day SMOKE 12 km 1st day SMOKE 12 km 2 nd day 36 km domain 1 st day 36 km domain 2 nd day 12 km domain 1 st day 12 km domain 2 nd day Download ETA Forecast MM5 simulations (24 CPUs) 36 km domain 1 st day 36 km domain 2 nd day 12 km domain 1 st day 12 km domain 2 nd day 04 km domain 1 st day 04 km domain 2 nd day Post-Process Visualization Statistics Web Display CMAQ simulations (36 CPUs) 04 km domain 1 st day 04 km domain 2 nd day 1 CPU MCIP 04 km 1 st day MCIP 04 km 1 st day SMOKE 04 km 1st day SMOKE 04 km 2 nd day Multi CPU Single CPU Data Flow F1=2000 imputed, Houston; F-2=2005 projected, E-Texas Batch mode operation with minimal intervention 54 hr forecasting simulation

Time series of regional daily max ozone June 2005 – May 2006

2006 June – 2006 Oct (TexAQS-II) UH (Univ. of Houston) AQF (Air Quality Forecasting) Systems Spatial Resolution 36 km : U.S. Continent 12 km : East Texas (2005) State of TX, LA, OK, AR, and MS (2006) 04 km : Houston and Galveston Area (F1) / HGA & DFW (F2 & F3) MM5 – 43 layers, CMAQ-23 layers Operation Period and Duration (May 2005 ~ Current) Spin-up : 6 hrs (0 th day 18 CST – 0 th day 23 CST) (0 th day 18 CST – 0 th day 23 CST) Forecasting : 46 hrs Forecasting : 46 hrs (1 st day 00 CST – 2 nd day 23 CST) (1 st day 00 CST – 2 nd day 23 CST) Different Air Quality Forecasting Systems Forecast 1 (F1) : MM5 modified by UH + TEI imputed for CMAQ v4.4 Forecast 2 (F2) : MM5 modified by UH + TEI imputed & projected for CMAQ v4.4

Modeling Domains – F2, TexAQS-II

Anthropogenic Emissions: for F1 (2005 & 2006) TEI 2000 Base5b –TexAQS 2000 episode used for State Implementation Plan –The day of Week Aug. 25 th  Friday, Aug. 26 th  Saturday, Aug. 27 th  Sunday, Aug. 30 th  Monday ~ Thursday –CB4, SAPRC99, and RADM2 –Area & Non-road: 2000 Emissions Inventory NEI99 (Final version 3) –CONUS 36-km domain –Particulate matters and precursors (NH3, SO2) Processor: SMOKE version 2.1 –Internal database: TCEQ’s (for spatial and temporal allocation) Default & TCEQ’s for chemical speciation

Anthropogenic Emissions for F2 (2006) Projected Texas EGU NOx emissions after State Implementation Plan (SIP) 2007 emissions inventory were projected from 2000 EI with growth and control factors from TCEQ. For HG NOx emissions for 2005, a factor of was applied on 2007 EI based on the 2005/2007 MECT ( Mass Emission Cap and Trade) allowances

Anthropogenic Emissions: for F2 (2006) VOC emissions for imputation after SIP UH AQF system uses additional VOC emissions at the 2007 level

MOBILE6 NOx emissions for 2000 and 2007 The emissions amounts for each county, vehicle type, hour and species were determined for 2005 based on those for 2000 and Then, the factor was applied on 2007 MOBILE6 emissions to get 2003 emissions

What Configurations were used for ETAQ-F 2006? ETAQF 2006 F1 & F2 Meteorology (F1 & F2 used UH MM5) * improved LULC * improved MRF for stable PBL and transition times (under development) * cloud; both the subgrid scale explicit scheme at 4-km * satellite observed sea surface temperature (in preparation for sensitivity testing) Emissions (F1 = 2000 SIP imputed TEI vs. F2 = 2005* projected) * 2005 TEI (projected from 2000 & 2007) * 2000 HRVOC (instead of 2005 projected) * Mobile projected for 2003 * satellite-observed fire events (in preparation) CMAQ (F1 = HGB 4-km vs. F2 = Extended 4-km (HGB + DFW) * with and w/o cloud attenuation * CB4 for forecasting and SAPRC99 for evaluation (on-going) * Better regional characterization at 12-km resolution

Monitoring site on Houston-Galveston domain F1 Model: F1

Monitoring sites for Dallas & Houston domain F2 Model: F2

June 2006 July 2006 August 2006 September /23 rain missed 9/14 upset event rain missed Aug 19 - pcpn

NOx daily mean time series for F1 & F2 Started using projected emissions (July 17) 2000 TEI “projected” 2005 TEI Aug 23 rd rain missed in AQF

O3 Scatter plot for F2 (daily max) F1F2

MM5 re-simulation Improving wind simulation is the most important factor for better AQM performance –FDDA is a proven technique to improve the meteorology reanalysis –Using IMAQS MM5-based Real-Time data assimilation framework, multiple observational datasets from Meteorological Assimilation Data In gest System (MADIS) and CAMS met data. A comprehensive surface obs. (SFC – from ASOS by NOAA/NWS) Improved radiosonde observations (RAOB) Aircraft sounding (ACARS) winds Improved NOAA Profiler Network (NPN) data –Tested a variety of assimilation configurations to identify the best combination to arrive at “TMNS11”  Start from 36km MM5 simulation using EDAS (to provide BC for nest domain)  Start from 36km MM5 simulation using EDAS (to provide BC for nest domain)  nest down to 12-kmMADIS & CAMS data to improve MM5 to improve

Data sets used for FDDA Multi-step FDDA Grid Nudging 3 hourly – 12 km Hourly – 4km Pink dots: CAMS Black dots: MADIS SFC (not shown) Upper air data Profiler data Sounding data Aircraft data Satellite data

Multi-Step FDDA with MM5 36km & 12km (3D nudging for u,v for everywhere, T & RH nudging in free atmosphere) 4-km domain  grid & surface nudging for wind components only Multi-step FDDA 12-, 4-km domain Multi-step nest-down assimilation Grid Nudging 3 hourly – 12 km Hourly – 4km SFC nudging

H 80 ppb 8/14 H 110 ppb 8/15 8/14 High pressure system in the Gulf, SW synoptic wind O3 peak (80 pbb) at NE of Harris county 8/15 Similar weather pattern as 8/14 O3 peak (110 ppb) at E of Harris & moved northward 8/16 Cold front near Dallas, W synoptic wind O3 peak (140 ppb) at NE of downtown 8/17 Affected by front, N/NE wind O3 peak (~150 ppb) at Deer Park 8/18 No strong system, light and variable wind High background, O3 (~120 ppb) at W of Harris 8/19 Precipitation 9 – 11 CST, NE wind O3 peak (~75 ppb) at NW of downtown 8/20 No strong system, light and variable wind O3 (110 ppb) at Clinton & passed through downtown 8/21 No strong system, SE/E wind O3 peak (~90 ppb) at NW of downtown 140 ppb 8/16 front 150 ppb front 8/ ppb 8/18 high O3 background 70 ppb 8/19 Rainfall at 9 – 11 CST 110 ppb 8/20 90 ppb 8/21 Overview of weather patterns and O3 levels

Does the Assimilation Improve Met Simulations? AQF TMNS11 8/14 8/16

Does the Assimilation Improve Met Simulations? 8/17 8/18 AQF TMNS11

Does the Assimilation Improve Met Simulations?

CMAQ re-simulation summary   Better Met.  Better Air Quality simulation? AQFn (F2 emissions) vs. TMNS11n   CMAQ re-simulation nickname & description 1) AQFn  F1 MM5 fcst + F2 level AQF emission 2) TMNS11n  TMNS11/MCIPn + F2 level AQF emission AQFnTMNS11n

AQFn vs TMNS11n : High O3 day - Met. changes in AQM  changed O3 level & spatial distribution significantly - TMNS11 reproduced O3 conc. better than AQF August 16, 2006

(1)AQFn vs TMNS11n : High O3 day August 17, 2006

AQFn vs TMNS11n : Low O3 day - TMNS11 didn ’ t reproduce O3 conc. better than AQF August 14, 2006

Evaluation of CMAQ Assessment Runs - Stats. : no big difference - high R,IOA(except 8/19) Mean Bias - low emiss.  - bias - high emiss.  + bias - all positive (except 8/19) - need further improvement

Summary  MM5 re-simulation results To improve Met simulation : several assimilation methods/data tested To improve Met simulation : several assimilation methods/data tested  TMNS11 provides better met.  TMNS11 provides better met. - removal of some not observed T-storm development - removal of some not observed T-storm development - reduction of WD bias - reduction of WD bias - more realistic wind variations overall - more realistic wind variations overall -but still unwanted flow patterns occurred : 8/18~19 -but still unwanted flow patterns occurred : 8/18~19  CMAQ re-simulation results - Assimilation provides better O3 level & spatial distributions more often - Assimilation provides better O3 level & spatial distributions more often - Not always improve met & air quality simulation results  Careful evaluation with various data necessary for each day to find causes of discrepancy to find causes of discrepancy Acknowledgement: HARC, TCEQ, EPA, NASA