Air Resources Laboratory 1 Inclusion of forest fires in North and Central America as extra-CONUS-domain intermittent sources for NAQFC: an operational.

Slides:



Advertisements
Similar presentations
Impacts of BC from fire emission on air quality ---- case study in 2010 Benpei Cao 04/25/13.
Advertisements

El Niño.
MOPITT CO Louisa Emmons, David Edwards Atmospheric Chemistry Division Earth & Sun Systems Laboratory National Center for Atmospheric Research.
Office of Research and Development National Exposure Research Laboratory, Atmospheric Modeling and Analysis Division Changes in U.S. Regional-Scale Air.
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.
Air Quality Impacts from Prescribed Burning Karsten Baumann, PhD. Polly Gustafson.
Recent Finnish PM studies / 2 examples. Characterizing temporal and spatial patterns of urban PM10 using six years of Finnish monitoring data Pia Anttila.
Satellite Imagery Meteorology 101 Lab 9 December 1, 2009.
Objective: Work with the WRAP, CenSARA, CDPHE, BLM and EPA Region 8 to use satellite data to evaluate the Oil and Gas (O&G) modeled NOx emission inventories.
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.
Tianfeng Chai 1,2, Alice Crawford 1,2, Barbara Stunder 1, Roland Draxler 1, Michael J. Pavolonis 3, Ariel Stein 1 1.NOAA Air Resources Laboratory, College.
Chapter 2: Satellite Tools for Air Quality Analysis 10:30 – 11:15.
Impact of Mexico City on Regional Air Quality Louisa Emmons Jean-François Lamarque NCAR/ACD.
Fire Products Training Workshop in Partnership with BAAQMD Santa Clara, CA September 10 – 12, 2013 Applied Remote SEnsing Training (ARSET) – Air Quality.
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,
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.
Estimates of Biomass Burning Particulate Matter (PM2.5) Emissions from the GOES Imager Xiaoyang Zhang 1,2, Shobha Kondragunta 1, Chris Schmidt 3 1 NOAA/NESDIS/Center.
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.
A Comparison of the Northern American Regional Reanalysis (NARR) to an Ensemble of Analyses Including CFSR Wesley Ebisuzaki 1, Fedor Mesinger 2, Li Zhang.
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.
Maria Val Martin and J. Logan (Harvard Univ., USA) D. Nelson, C. Ichoku, R. Kahn and D. Diner (NASA, USA) S. Freitas (INPE, Brazil) F.-Y. Leung (Washington.
Modeling volcanic and marine emissions for Hawaii Air Quality Forecast 10/24/2015Air Resources Laboratory1 Daniel Tong*, Pius Lee, Rick Saylor, Mo Dan,
MELANIE FOLLETTE-COOK KEN PICKERING, PIUS LEE, RON COHEN, ALAN FRIED, ANDREW WEINHEIMER, JIM CRAWFORD, YUNHEE KIM, RICK SAYLOR IWAQFR NOVEMBER 30, 2011.
Air Resources Laboratory CMAS meeting Chapel Hill, North Carolina Yunsoo Choi 1,2, Hyuncheol Kim 1,2, Daniel Tong 1,2, Pius Lee 1, Rick Saylor 3, Ariel.
Evaluating ammonia (NH 3 ) predictions in the NOAA National Air Quality Forecast Capability (NAQFC) using in situ aircraft measurements William Battye,
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.
Evaluation of several WRF/NMM- CMAQ vertical coupling configurations Pius Lee, Jeff McQueen, Marina Tsildulko, Geoff DiMego Sarah Lu and Bert Katz NOAA/NCEP.
Evaluation of CMAQ prediction of carbon monoxide vertical profiles against SENEX Nina Randazzo*, Daniel Tong ¥, Pius Lee ¥, Li Pan ¥, Min Huang ¥ * =CICS/UMD,
Application of Models-3/CMAQ to Phoenix Airshed Sang-Mi Lee and Harindra J. S. Fernando Environmental Fluid Dynamics Program Arizona State University.
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.
Approach: Assimilation Efficiencies The Carbon based model calculates mixed layer NPP (mg m -3 ) as a function of carbon and phytoplankton growth rate:
Continued improvements of air quality forecasting through emission adjustments using surface and satellite data & Estimating fire emissions: satellite.
Impact of Meteorological Inputs on Surface O 3 Prediction Jianping Huang 9 th CMAS Annual Conference Oct. 12, 2010, Chapel, NC.
1 Air Quality : National AQ Forecasting Capability surface O 3 and PM 2.5 Presented By: Pius Lee (OAR/ARL) Contributors: Jeffery McQueen, Jianping Huang,
Developing an Interactive Atmosphere-Air Pollutant Forecast System Jeff McQueen, Youhua Tang, Sarah Lu, Ho-Chun Huang, Dongchul Kim, Pius Lee and Marina.
Evapotranspiration Estimates over Canada based on Observed, GR2 and NARR forcings Korolevich, V., Fernandes, R., Wang, S., Simic, A., Gong, F. Natural.
Post-processing air quality model predictions of fine particulate matter (PM2.5) at NCEP James Wilczak, Irina Djalalova, Dave Allured (ESRL) Jianping Huang,
Impact of the changes of prescribed fire emissions on regional air quality from 2002 to 2050 in the southeastern United States Tao Zeng 1,3, Yuhang Wang.
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,
Global high-resolution marine isoprene emission derived from VIIRS-SNPP and MODIS-Aqua ocean color observations 1/25/2016Air Resources Laboratory1 Daniel.
Air Resources Laboratory 1 Comprehensive comparisons of NAQFC surface and column NO 2 with satellites, surface, and field campaign measurements during.
1 Using Satellite Data to Improve Operational Atmospheric Constituents Forecasting Capabilities Shobha Kondragunta, NOAA/NESDIS/STAR Xiaoyang Zhang, UMD.
Tianfeng Chai 1,2, Hyun-Cheol Kim 1,2, Daniel Tong 1,2, Pius Lee 2, Daewon W. Byun 2 1, Earth Resources Technology, Laurel, MD 2, NOAA OAR/ARL, Silver.
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.
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,
Forecasting smoke and dust using HYSPLIT. Experimental testing phase began March 28, 2006 Run daily at NCEP using the 6Z cycle to produce a 24- hr analysis.
Fire Products NASA ARSET-AQ Links Updated November 2013 ARSET Applied Remote SEnsing Training A project of NASA Applied Sciences.
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,
Earth Science Chapter 8 Climates.
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.
Chemical histories of pollutant plumes in East Asia:
17th Annual CMAS Conference
Potential Performance differences of the National Air Quality Forecasting Capability when upgrading the Chemical Transport Model Pius Lee1, Youhua Tang1,2,
Tianfeng Chai1,2,3, Hyuncheol Kim1,2,3, and Ariel Stein1
Coastal CO2 fluxes from satellite ocean color, SST and winds
Hyun Cheol Kim 1,2, Fantine Ngan 1,2, Pius Lee 1, and Rick Saylor 3
Aura Science Team meeting
Pius Lee, Youhua Tang, Jeff McQueen, Ho-Chun Huang,
El Niño.
2019 TEMPO Science Team Meeting
Current Research on 3-D Air Quality Modeling: wildfire!
Presentation transcript:

Air Resources Laboratory 1 Inclusion of forest fires in North and Central America as extra-CONUS-domain intermittent sources for NAQFC: an operational viability study Pius Lee 1*, Hyuncheol Kim 2, Daniel Tong 2, Yunhee Kim 2, Tianfeng Chai 2, Yunsoo Choi 2, Li Pan 2, Rick Saylor 1, Ariel Stein 2, Youhua Tang 3, Jianping Huang 3, Jeff McQueen 4, Marina Tsidulko 3, Hochun Huang 3, Sarah Lu 3, and Ivanka Stajner 5 *Corresponding Author Address: Pius Lee, 1 NOAA/OAR/ARL, 1315 East West Hwy, Room 3461, Silver Spring, MD Earth Resources & Technology,Inc, Annapolis Junction, MD 3 I.M. Systems Group, Inc. Rockville, MD NOAA/NWS/National Centers for Environmental Prediction, Camp Springs, MD. 5 Office of Science and Technology, National Weather Service, Silver Spring, MD.

Air Resources Laboratory 2  Deep Water Horizon offshore oil platform accident: April 20, 2010 for 87 days ~1 mile below sea level, ~ 40 miles off Louisiana coast (near the coast of Chandeleur Sounds)  burning of skimmed oil, flaring of the fuels directly from well pipe, and evaporative emissions from oil slick  Share air quality modeling effort  Entire oil spill event to study impact on regional air quality over SE US  Study distribution and fate of oil spill related air pollutants subject to uncertainties in emission inputs

Air Resources Laboratory 3 NOAA NOS OR&R (National Ocean Service / Office of Response and Restoration) oil slick trajectory model results, initialized with satellite data Near Shore map Off Shore map SHAPE files available Courtesy Nesdis/NOAA May 17, 20C10 Estimating evaporative emissions from oil slick Near shoreOff shore Sheen in imagery may corresponds to tar balls

Air Resources Laboratory 4 Equivalent area of half circle radius (April 22- June 30, 2010) slick areas Approach: Estimated fresh oil amount in the slicks for different slick types Approximate evaporation ratios: 90: 9: 1 Distributed 25% of 4.9M bbl over daily heavy, medium, light Daily Change of DWH oil slick size estimated from the oil slick map Heavy Light Medium

Burn volume (barrels)Burn Counts at each 12x12 km cell Numbers are gridded into the NAQFC 12km CONUS domain Oil burn locations used for air quality modeling

Minimal impact on surface concentration and surface fluxes between June 1 – July 8, June 21, 2011 Experimental NAQFC inclines toward high bias for [O 3 ] in warm/humid SE, DWH impact minimal. Experimental NAQFC inclines toward low bias for [pm 2.5 ] in summer

June June June June

June June June June Intra- and Exo-domain wild fire intermittent source is rather frequent *Wednesday Talk: Fernando Garcia-Menendez

Air Resources Laboratory 9 HMS wildfire detections during Apr Emission should include Exo- and intra-domain wild fires ~21x ~12x 5x Agricultural burning prevails in the months of March and April in Mexico

& Poster: Youhua Tang $ Tuesday talk: Fernando Garcia-Menedez *Poster: Hyuncheol Kim et el. “Evaluation of fire modeling systems: fire smoke extension and chemical composition” Several operationally feasible approaches to include Exo-domain Wild Fires ConfigurationEmissionmeteorology Species treatment LBC size mapping Challenges LBC include GOCART smoke plume QFED or GBBEP emission GFS- GOCART & Inline/offline BC, OC Particle aging. Mainly sub- micro particulates Plume-rise (FRP) & zero- out emission within 5x LBC include Hysplit- smoke Hms- Bluesky* NAM-Hysplit CO & pm25 Plume-rise $ (Briggs Eq) Transition through a CMAQ parent domain Can come from either method above All relevant CMAQ species Again satisfying all modal size distributions More step, more complex as there is one more “intermediate ” model-run

Total Carbon Emissions for GBBEP & QFED (JUL-SEP 2010) GBBEP QFED QFED and GBBEP produced similar spatial patterns and monthly variation in total carbon (black + organic carbons) emissions. During the simulated period, both the South America and the Africa had frequent fire activities. In general, QFED has smaller area of detected fires but with stronger carbon emissions while GBBEP has larger area of detected fires with weaker carbon emissions. There is a limited spatial coverage for geostationary satellites at high latitudes. Thus, QFED detected more fires in the Russia, the Siberia, and the Canadian Boreal forest. Hourly flux at 1 o x1 o

~21x 5x NC and GA fires impacted Discover-AQ area. Analysis day NCEP operation slot $ Tuesday Talk: Karsten Baumann Poster: Chris Misenis; $ Tuesday Talk: Karsten Baumann

Air Resources Laboratory 13 WEEWSSNN SouthernEasternNorthernWestern

10 m Windspeed ~12x 5x Lowest mid-layer Temperature ~12x Dynamic LBC fed by GOCART or HYSPLIT Sample of hourly output from premaq w.r.t parent domain – can be much coarser in temporal and spatial resolution

Time series of monthly mean event-count HMS/smoke fraction inside CONUS domain. Blue lines indicates bottom half of domain, and red color indicates upper half of the CONUS domain Hms/Bluesky/Smoke monthly mean fraction owing to Wild fires in Southern Half or Northern half Intra-domain wild fire emissions

Flight track: July 20, 2011 Spirals over Wilmington and Edgewood A long hot And muggy Working day

Air Resources Laboratory 17 NASA P-3B Flight Pathes July 1-29, aq/images/DISCOVER-AQ_2011_ALL_P3B_July1-July29.png aq/images/DISCOVER-AQ_2011_ALL_P3B_July1-July29.png

RTMA NAM predicted 10m wind Rain gauge analysis NAM precip Max Temp RTMA NAM predicted 10m wind RTMA NAM predicted 2m temp 15Z 18Z Less spurious Sharp convergence zone

19 Comparison of Wind along flight track of P3B on July Spirals over Wilmington and Edgewood Model under-predicted wind shear More frequent low Bias in higher altitudes Less turbulence may not matter as PBL well- mixed, shallow-convection may matter. Air Resources Laboratory

20 Comparison of Wind along flight track of P3B on July CMAQ471 vs obs. [NO 2 ] on July 20, 2011 CMAQ471 vs obs. [NO y ] on July 20, 2011 Models high-bias, lack of venting due to fair weather cumulus may exacerbate high-bias

Air Resources Laboratory 21 Comparison of Wind along flight track of P3B on July NO2 and TOL both are indicators of vehicular exhaust tracks well; ground-hugging

Air Resources Laboratory 22 Comparison of Wind along flight track of P3B on July CMAQ471 vs obs. [NO 2 ] on July 20, 2011 CMAQ471 vs obs. [NO y ] on July 20, 2011 Formaldehyde as proxy of VOC emission

CMAQ471 no_hms_fire AOD MODIS AOD Spatial distribution of hourly avg. column-integrated AOD Air Resources Laboratory 23 Satellite data was also attempted

Air Resources Laboratory 24 Summary  Last year’s study on DWH affirms that intra- and extra-domain wild fire emissions are dominant intermittent sources essential for Air Quality (AQ) studies – regional as well as continental  Several model configurations to include extra-domain wild fire emission through ingesting smoke-plume from these fires in the forecast model’s time-varying chemical Lateral Boundary Conditions (LBC); i.e. chemical field fed by: (1) GFS-GOCARD-smoke; (2) HYSPLIT-smoke; and (3) Either use (1) or (2) but through the in-nesting of a large parent CMAQ domain more than 2 times the geographical area as continental U.S.  These model configurations are at various degree of development. They all have pros and cons in terms of operational feasibility for real time AQ forecasting.  Rich data set obtained from the Discover-AQ campaigns and satellite-based AOD retrievals are extremely variable in evaluating the approaches considered.

Backup slides