Evaluation of Real-time Air Quality Forecasts from WRF-NMM/CMAQ and NMMB/CMAQ Using Discover-AQ P-3B Airborne Measurements Youhua Tang 1,2, Jeffery T.

Slides:



Advertisements
Similar presentations
Inventory Issues and Modeling- Some Examples Brian Timin USEPA/OAQPS October 21, 2002.
Advertisements

Constraining Anthropogenic Emissions of Fugitive Dust with Dynamic Transportable Fraction and Measurements Chapel Hill, NC October 22, 2009 Daniel Tong.
CO budget and variability over the U.S. using the WRF-Chem regional model Anne Boynard, Gabriele Pfister, David Edwards National Center for Atmospheric.
11 th CMAS Meeting, RTP October 15 – 17, Fine Resolution Air Quality Forecasting Capability for limited-area domains – tested over Eastern Texas.
Improving the Representation of Atmospheric Chemistry in WRF William R. Stockwell Department of Chemistry Howard University.
Regional Air Quality Modeling Progress at NOAA/NWS/NCEP
Effects of climate change on future wildfire and its impact on regional air quality Hyun Cheol Kim, Dae-Gyun Lee, and Daewon Byun 1 Institute for Multidimensional.
Impact of chemistry scheme complexity on UK air quality modelling Met Office FitzRoy Road, Exeter, Devon, EX1 3PB United Kingdom Tel: Fax:
Transitioning CMAQ for NWS Operational AQ Forecasting Jeff McQueen*, Pius Lee*, Marina Tsildulko*, G. DiMego*, B. Katz* R. Mathur,T. Otte, J. Pleim, J.
Simulation of Houston-Galveston Airshed Ozone Episode with EPA’s CMAQ Daewon Byun: PI Soontae Kim, Beata Czader, Seungbum Kim Emissions input Chemical.
Air Resources Laboratory Yunsoo Choi 12, Daewon Byun 1, Pius Lee 1, Rick Saylor 1, Ariel Stein 12, Daniel Tong 12, Hyun-Cheol Kim 12, Fantine Ngan 13,
Air Resources Laboratory 1 Inclusion of forest fires in North and Central America as extra-CONUS-domain intermittent sources for NAQFC: an operational.
Prototyping the Emergency Smoke Response System (ESRS) Sim Larkin, Tara Strand, Robert Solomon (US Forest Service AirFire Team) Sean Raffuse, Dana Raffuse,
A Case Study Using the CMAQ Coupling with Global Dust Models Youhua Tang, Pius Lee, Marina Tsidulko, Ho-Chun Huang, Sarah Lu, Dongchul Kim Scientific Applications.
Performance of the National Air Quality Forecast Capability, Urban vs. Rural and Other Comparisons Jerry Gorline and Jeff McQueen  Jerry Gorline, NWS/OST/MDL.
Modeling Aerosol Formation and Transport in the Pacific Northwest with the Community Multi-scale Air Quality (CMAQ) Modeling System Susan M. O'Neill Fire.
Comparison of three photochemical mechanisms (CB4, CB05, SAPRC99) for the Eta-CMAQ air quality forecast model for O 3 during the 2004 ICARTT study Shaocai.
Simulating prescribed fire impacts for air quality management Georgia Institute of Technology M. Talat Odman, Yongtao Hu, Fernando Garcia-Menendez, Aika.
Remote Sensing and Modeling of the Georgia 2007 Fires Eun-Su Yang, Sundar A. Christopher, Yuling Wu, Arastoo P. Biazar Earth System Science Center University.
1 Using Hemispheric-CMAQ to Provide Initial and Boundary Conditions for Regional Modeling Joshua S. Fu 1, Xinyi Dong 1, Kan Huang 1, and Carey Jang 2 1.
Modeling volcanic and marine emissions for Hawaii Air Quality Forecast 10/24/2015Air Resources Laboratory1 Daniel Tong*, Pius Lee, Rick Saylor, Mo Dan,
Evaluation and Application of Air Quality Model System in Shanghai Qian Wang 1, Qingyan Fu 1, Yufei Zou 1, Yanmin Huang 1, Huxiong Cui 1, Junming Zhao.
MELANIE FOLLETTE-COOK KEN PICKERING, PIUS LEE, RON COHEN, ALAN FRIED, ANDREW WEINHEIMER, JIM CRAWFORD, YUNHEE KIM, RICK SAYLOR IWAQFR NOVEMBER 30, 2011.
Evaluating ammonia (NH 3 ) predictions in the NOAA National Air Quality Forecast Capability (NAQFC) using in situ aircraft measurements William Battye,
Melanie Follette-Cook Christopher Loughner (ESSIC, UMD) Kenneth Pickering (NASA GSFC) CMAS Conference October 27-29, 2014.
Rick Saylor 1, Barry Baker 1, Pius Lee 2, Daniel Tong 2,3, Li Pan 2 and Youhua Tang 2 1 National Oceanic and Atmospheric Administration Air Resources Laboratory.
Evaluation of modeled surface ozone biases as a function of cloud cover fraction Hyun Cheol Kim 1,2, Pius Lee 1, Fong Ngan 1,2, Youhua Tang 1,2, Hye Lim.
Wildland Fire Impacts on Surface Ozone Concentrations Literature Review of the Science State-of-Art Ned Nikolov, Ph.D. Rocky Mountain Center USDA FS Rocky.
A detailed evaluation of the WRF-CMAQ forecast model performance for O 3, and PM 2.5 during the 2006 TexAQS/GoMACCS study Shaocai Yu $, Rohit Mathur +,
Operational Evaluation and Comparison of CMAQ and REMSAD- An Annual Simulation Brian Timin, Carey Jang, Pat Dolwick, Norm Possiel, Tom Braverman USEPA/OAQPS.
Evaluation of CMAQ prediction of carbon monoxide vertical profiles against SENEX Nina Randazzo*, Daniel Tong ¥, Pius Lee ¥, Li Pan ¥, Min Huang ¥ * =CICS/UMD,
Comparison of CMAQ Lightning NOx Schemes and Their Impacts Youhua Tang 1,2, Li Pan 1,2, Pius Lee 1, Jeffery T. McQueen 4, Jianping Huang 4,5, Daniel Tong.
Impact of Meteorological Inputs on Surface O 3 Prediction Jianping Huang 9 th CMAS Annual Conference Oct. 12, 2010, Chapel, NC.
Recent Advances in the Use of Chemical Transport Models in Atmospheric Chemistry Studies G. Carmichael, I. Uno, Y. Tang, J. Woo, D. Streets, G. Kurata,
Applications of Models-3 in Coastal Areas of Canada M. Lepage, J.W. Boulton, X. Qiu and M. Gauthier RWDI AIR Inc. C. di Cenzo Environment Canada, P&YR.
An Exploration of Model Concentration Differences Between CMAQ and CAMx Brian Timin, Karen Wesson, Pat Dolwick, Norm Possiel, Sharon Phillips EPA/OAQPS.
Developing an Interactive Atmosphere-Air Pollutant Forecast System Jeff McQueen, Youhua Tang, Sarah Lu, Ho-Chun Huang, Dongchul Kim, Pius Lee and Marina.
Post-processing air quality model predictions of fine particulate matter (PM2.5) at NCEP James Wilczak, Irina Djalalova, Dave Allured (ESRL) Jianping Huang,
Office of Research and Development Atmospheric Modeling and Analysis Division, National Exposure Research Laboratory Simple urban parameterization for.
1 Aika Yano, Yongtao Hu, M. Talat Odman, Armistead Russell Georgia Institute of Technology October 15, th annual CMAS conference.
Using combined Lagrangian and Eulerian modeling approaches to improve particulate matter estimations in the Eastern US. Ariel F. Stein 1, Rohit Mathur.
Boundary layer depth verification system at NCEP M. Tsidulko, C. M. Tassone, J. McQueen, G. DiMego, and M. Ek 15th International Symposium for the Advancement.
Comparison of NOAA/NCEP 12km CMAQ Forecasts with CalNEX WP-3 Measurements Youhua Tang 1,2, Jeffery T. McQueen 2, Jianping Huang 1,2, Marina Tsidulko 1,2,
Office of Research and Development National Exposure Research Laboratory, Atmospheric Modeling and Analysis Division Office of Research and Development.
Air Resources Laboratory 1 Comprehensive comparisons of NAQFC surface and column NO 2 with satellites, surface, and field campaign measurements during.
WRAP Regional Modeling Center, Attribution of Haze Meeting, Denver CO 7/22/04 Introduction to the the RMC Source Apportionment Modeling Effort Gail Tonnesen,
The Impact of Lateral Boundary Conditions on CMAQ Predictions over the Continental US: a Sensitivity Study Compared to Ozonsonde Data Youhua Tang*, Pius.
Ozone and PM 2.5 verification in NAM-CMAQ modeling system at NCEP in relation to WRF/NMM meteorology evaluation Marina Tsidulko, Jeff McQueen, Pius Lee.
W. T. Hutzell 1, G. Pouliot 2, and D. J. Luecken 1 1 Atmospheric Modeling Division, U. S. Environmental Protection Agency 2 Atmospheric Sciences Modeling.
Wildfire activity as been increasing over the past decades Cites such as Salt Lake City are surrounded by regions at a high risk for increased wildfire.
The regional chemical transport model, STEM, with advanced photochemical modules including SAPRC99 mechanism, KPP chemical solver and explicit photolysis.
1 NOAA-EPA’s National Air Quality Forecast Capability: Progress and Plans October 17, 2006 Paula M. Davidson 1, Nelson Seaman 1, Jeff McQueen 1, Rohit.
The Influence of Lateral and Top Boundary Conditions on Regional Air Quality Prediction: a Multi-Scale Study Coupling Regional and Global Chemical Transport.
Meteorological Development Laboratory / OST / National Weather Service  1200 and 0600 UTC OZONE 48-h experimental, 8-h (daily max) 48-h experimental,
On the Verification of Particulate Matter Simulated by the NOAA-EPA Air Quality Forecast System Ho-Chun Huang 1, Pius Lee 1, Binbin Zhou 1, Jian Zeng 6,
V:\corporate\marketing\overview.ppt CRGAQS: CAMx Sensitivity Results Presentation to the Gorge Study Technical Team By ENVIRON International Corporation.
Understanding the impact of isoprene nitrates and OH reformation on regional air quality using recent advances in isoprene photooxidation chemistry Ying.
Robin L. Dennis, Jesse O. Bash, Kristen M. Foley, Rob Gilliam, Robert W. Pinder U.S. Environmental Protection Agency, National Exposure Research Laboratory,
Ship emission effect on Houston Ship Channel CH2O concentration ——study with high resolution model Ye Cheng.
Daniel Tong NOAA Air Resources Lab & George Mason University
Potential use of TEMPO AOD & NO2 retrievals to support wild fire plume & O3 & PM2.5 forecast in National Air Quality Forecasting Capability (NAQFC) Pius.
C. Nolte, T. Spero, P. Dolwick, B. Henderson, R. Pinder
Forecasting the Impacts of Wildland Fires
Potential Performance differences of the National Air Quality Forecasting Capability when upgrading the Chemical Transport Model Pius Lee1, Youhua Tang1,2,
Evaluation of Operational and Experimental CMAQ Ozone and PM
Impact of Assimilated Boundary Conditions on WRF-NMM/CMAQ Air Quality Predictions Researchers at NESDIS, NCEP and the NWS used global ozone analyses from.
SELECTED RESULTS OF MODELING WITH THE CMAQ PLUME-IN-GRID APPROACH
Pius Lee, Youhua Tang, Jeff McQueen, Ho-Chun Huang,
Current Research on 3-D Air Quality Modeling: wildfire!
Presentation transcript:

Evaluation of Real-time Air Quality Forecasts from WRF-NMM/CMAQ and NMMB/CMAQ Using Discover-AQ P-3B Airborne Measurements Youhua Tang 1,2, Jeffery T. McQueen 2, Marina Tsidulko 1,2, Jianping Huang 1,2, Pius Lee 3, Ivanka Stajner 4 and NASA Discover-AQ Measurement Team 1. I.M. Systems Group Inc., Camp Springs, MD 2. NOAA/NCEP/EMC 3. NOAA Air Resource Laboratory, Silver Spring, MD 4. NOAA Office of Science and Technology, Silver Spring, MD

2 NAM (WRF-NMM) Meteorology NAMB (NMM-B) Meteorology CMAQ CB04 CMAQ CB05 Operational and Developmental 12km NAQFC Systems (July 2011) CMAQ CB04 CMAQ CB05 without wildfire emission CMAQ CB05 with wildfire emission NOAA Hazard Mapping System (HMS) fire locations BlueSky emission inventory CMAQ/SMOKE for processing plume rise and generating gridded wildfire emissions

Discover-AQ P-3B Flight on 07/01

The models generally undepredicted biogenic emitted species, especially for terpenes. The bias is related to certain areas. For all flights (July 1-29) below 1km

Model predicted wildfire CO compared to HMS plumes

Missed wildfire plumes On 07/01 identified by Acetontrile

Acetonitrile is highly correlated with CO enhancement in wildfire plumes. Black carbon or aerosol absorption seems not a good tracer for long- range transported fire plumes as it could be wet removed. For All flights above 1km

WRF-NMM and NMM-B yield very different background CO concentrations over the east coast. NMM-B has slightly higher PBL heights (black-and-white lines show the PBL heights). The wildfire run yields remarkable fire-emitted CO, but tends to be too diffusive.

The flight on 07/14. The model missed wildfire plumes The flight on 07/27. The model overpredicted wildfire plumes below 3km

The model overpredicted the wildfire emissions in Northern Virginia/Maryland and missed those in Tennessee and Carolinas.

Meteorology change has significant impact on transported species. NMM- B/CMAQ yield lower concentrations than the WRF- NMM/CMAQ for almost all species for both CB4 and CB05 mechanism.

The models have relatively poor performance during wildfire events, especially for fire-emitted species, like CO.

Summary Meteorology has a large impact on CMAQ predictions. –NMM-B/CMAQ yields lower concentrations than the WRF- NMM/CMAQ for almost all species, –Most pronounced for long-lived and long-range transported species, like CO and NO y. In July 2011, there were many wildfire events that through long- range transport affected forecasts along P3-B flight tracks: –Underprediction (07/01 and 07/14) –Good prediction (07/11), –Overpredicted for a nearby fire event (07/27). – In general, poorer predictions for wildfire events than for no-fire events. There are some systematic biases for primary emitted species –toluene, black carbon (high bias), –methanol (low bias) –isoprene and monoterpenes (low bias in certain non-urban areas).