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National Air Quality Forecasting Capability: Nationwide Prediction and Future Enhancements Ivanka Stajner 1,5 , Daewon Byun 2,† , Pius Lee 2 , Jeff McQueen 3 , Roland Draxler 2 , Phil Dickerson 4 , Kyle Wedmark 1,5

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National Air Quality Forecasting

Capability: Nationwide Prediction and Future Enhancements

Ivanka Stajner1,5, Daewon Byun2,†, Pius Lee2, Jeff McQueen3, Roland Draxler2, Phil Dickerson4, Kyle Wedmark1,5

1 National Oceanic and Atmospheric Administration (NOAA), National Weather Service

2 NOAA, Air Resources Laboratory (ARL)

3 NOAA, National Center for Environmental Prediction (NCEP)

4 Environmental Protection Agency (EPA)

5 Noblis, Inc

† Deceased

2011 National Air Quality Conference, San Diego, CA March 9, 2011




  • Background

  • Recent Progress

    • Ozone

    • Smoke

    • Dust

    • PM2.5

  • Feedback and Outreach

  • Summary and Plans

  • National air quality forecast capability current and planned capabilities 3 11

    2005: O3


    2007: O3,& smoke




    National Air Quality Forecast CapabilityCurrent and Planned Capabilities, 3/11

    • Improving the basis for AQ alerts

    • Providing AQ information for people at risk

    • Prediction Capabilities:

    • Operations:

      Ozone nationwide: expanded from EUS to CONUS (9/07), AK (9/10) and HI (9/10)

      Smoke nationwide: implemented over CONUS (3/07), AK (9/09), and HI (2/10)

    • Experimental testing:

      • Ozone upgrades

      • Dust predictions

    • Developmental testing:

      • Ozone upgrades

      • Components for particulate matter (PM) forecasts

    2009: smoke

    2010: ozone

    • Near-term Targets:

    • Higher resolution prediction

    • Longer range:

    • Quantitative PM2.5 prediction

    • Extend air quality forecast range to 48-72 hours

    • Include broader range of significant pollutants

    National air quality forecast capability end to end operational capability
    National Air Quality Forecast Capability End-to-End Operational Capability

    • Model Components: Linked numerical prediction system

    • Operationally integrated on NCEP’s supercomputer

      • NCEP mesoscale NWP: WRF-NMM

      • NOAA/EPA community model for AQ: CMAQ

      • NOAA HYSPLIT model for smoke prediction

    • Observational Input:

      • NWS weather observations; NESDIS fire locations

      • EPA emissions inventory/monitoring data

    • Gridded forecast guidance products

      • On NWS servers: and ftp-servers

      • On EPA servers

      • Updated 2x daily

    • Verification basis, near-real time:

      • Ground-level AIRNow observations

      • Satellite smoke observations

    • Customer outreach/feedback

      • State & Local AQ forecasters coordinated with EPA

      • Public and Private Sector AQ constituents

    Progress in 2010 ozone smoke operational nationwide dust testing
    Progress in 2010Ozone, Smoke Operational Nationwide; Dust Testing

    • Ozone: Expanded Forecast Guidance to Alaska and Hawaii domains in NWS operations (9/10)

      • Operations, 2010: Updates for CONUS (emissions), new 1, 8-hour daily maximum products

      • Developmental testing: changing boundary conditions, dry deposition, PBL in CB-05 system

  • Smoke: Expanded Forecast Guidance to Hawaii domain in NWS operations (2/10)

    • Operations:CONUS predictions operational since 2007, AK predictions since 2009

    • Developmental testing: Improvements to verification

  • Aerosols: Developmental testing providing comprehensive dataset for diagnostic evaluations. (CONUS)

    • CMAQ (aerosol option), testing CB-05 chemical mechanism with AERO-4 aerosol modules

      • Qualitative; summertime underprediction consistent with missing source inputs

    • Dust and smoke inputs: testing dust contributions to PM2.5 from global sources

      • Real-time testing of combining smoke inputs with CMAQ-aerosol

    • Testing experimental prediction of dust from CONUS sources

    • Developing prototype for assimilation of surface PM2.5 measurements

    • R&D efforts continuing in chemical data assimilation, real-time emissions sources, advanced chemical mechanisms

  • Near real time ozone verification
    Near-real-time OzoneVerification

    • Comparing model with observations from ground level monitors compiled and provided by EPA’ s AIRNow program

  • Fraction correct with

  • respect to 75ppb (code orange) thresholdfor operational prediction (using CMAQ CBIV mechanism)

  • over 48 contiguous states

  • Year 2009

    skill target:

    fraction correct ≥ 0.9

    Year 2010

    Chemical mechanism sensitivity analysis
    Chemical mechanism sensitivity analysis

    Experimental ozone prediction (with updated CB05 mechanism) shows larger biases than

    • operational ozone prediction (CB-IV mechanism)

      • Summertime,

      • Eastern US.

    • Sensitivity studies in progress:

      • Chemical speciation

      • Indicator reactions in box model studies (organic nitrate, NOx, PAN)

    Seasonal ozone bias for CONUS

    Operational (CB-IV)

    Experimental (CB05)

    • Mechanism differences

    • Ozone production

    • Precursor budget

    • Seasonal input differences

    • Emissions

    • Meteorological parameters

    in 2009

    Impact of nmm b meteorology august 10 2010 example
    Impact of NMM-B meteorologyAugust 10, 2010 example



    Overprediction case highlighted by Michael Giegert, CT DEP

    NMMB-CMAQ reduces overprediction of 8-hr maximum ozone in the coastal region in the northeastern US


    Smoke prediction example four mile canyon fire
    Smoke Prediction Example: Four Mile Canyon Fire

    September 7-8, 2010

    “The blaze broke out Monday morning in Four Mile Canyon northwest of Boulder and rapidly spread across roughly 1,400 hectares. Erratic wind gusts sometimes sent the fire in two directions at once.”

    “The 11-square-mile blaze had destroyed at least 92 structures and damaged at least eight others by Tuesday night, Boulder County sheriff's Cmdr. Rick Brough said.”


    CONUS Dust Predictions: Experimental Testing

    • Standalone prediction of airborne dust from dust storms:

    • Wind-driven dust emitted where surface winds exceed thresholds over source regions

  • Source regions with emission potential estimated from monthly MODIS deep blue climatology (2003-2006)

    • HYSPLIT model for transport, dispersion and deposition

    • Updated source map in experimental testing (Draxler et al., JGR, 2010)

  • Developmental testing

    Dust simulation compared with improve data
    Dust simulation compared with IMPROVE data

    • HYSPLIT simulated air concentrations are compared with IMPROVE data in southern California and western Arizona:

      • Model simulations: contours

      • IMPROVE dust concentration: numbers next to “+“

      • Variability in model simulation

      • Highest measured value in Phoenix

      • Model and IMPROVE comparable in 3-10 ug/m3 range

      • Spotty pattern indicates under-prediction. Considering ways for improving representation of smaller dust emission sources.

    June-July 2007 average dust concentration

    (Draxler et al., JGR, 2010)

    Developmental predictions summer 2010
    Developmental predictions, Summer 2010

    Aerosols over CONUS

    From National emissions inventory (NEI) sources only

    • CMAQ:

      CB05 gases, AERO-4 aerosols

    • sea salt emissions and reactions

      Seasonal bias:

  • winter, overprediction

  • summer, underprediction

    Testing of real-time wildfire smoke emissions in CMAQ

  • Summer 2010

    Assimilation of surface pm data
    Assimilation of surface PM data

    • Assimilation increases correlation between forecast and observed PM2.5

    • Control is forecast without assimilation

    Assimilation reduces bias and increases equitable threat score

    Bias ratio

    Equitable threat score [%]

    Pagowski et al

    QJRMS, 2010

    • Exploring bias correction approaches, e.g. Djalalova et al., Atmospheric Env., 2010

    Partnering with AQ Forecasters

    Focus group, State/local AQ forecasters:

    • Participate in real-time developmental testing of new capabilities, e.g. aerosol predictions

    • Provide feedback on reliability, utility of test products

    • Local episodes/case studies emphasis

    • Regular meetings; working together with EPA’s AIRNow and NOAA

    • Feedback is essential for refining/improving coordination

    This year’s Air Awareness week is May 2nd – May 6th 2011

    Aq forecaster feedback examples
    AQ Forecaster Feedback Examples

    Skill Scores (2010) for Code Orange O3 Threshold (≥ 76 ppbv, 8-hour)

    • William Ryan, Penn State:

    • Significant, and continuing, reductions in NOx emissions on a regional scale since 2003 and lowered O3 concentrations

    • The key relationship that powers statistical forecast models (O3 and Tmax) has weakened

    • Forecasters must now rely on numerical forecast models, updated with EGU emissions data yearly, for forecast guidance.

    • NAQC model guidance out-performed other forecast methods in 2010

    • Precursor emissions and O3 concentrations not static since 2003 and not likely to be in the near future

    • *from Bill Ryan’s 2011 AMS presentation, The Effect of Recent Regional Emissions Reductions on Air Quality Forecasting in the mid-Atlantic


    • US national AQ forecasting capability status:

    • Ozone prediction nationwide (AK and HI since September 2010)

    • Smoke prediction nationwide (HI since February 2010)

    • Experimental testing of dust prediction from CONUS sources (since June 2010)

    • Developmental testing of CMAQ aerosol predictions with NEI sources

    • Near-term plans for improvements and testing:

    • Operational dust predictions over CONUS

    • Development of satellite product for verification of dust predictions

    • Understanding/reduction of summertime ozone biases in the CB05 system

    • Transitions to meteorological inputs from NMM-B regional weather model

    • Development of higher resolution predictions

    Target operational implementation of initial quantitative total PM2.5 forecasts for northeastern US in 2015

    Acknowledgments aqf implementation team members
    Acknowledgments:AQF Implementation Team Members

    Special thanks to Paula Davidson, OST chief scientist and former NAQFC Manager

    NOAA/NWS/OST Tim McClung NAQFC Manager

    NOAA/OAR Jim Meagher NOAA AQ Matrix Manager

    NWS/OCWWSJannie Ferrell Outreach, Feedback

    NWS/OPS/TOC Cindy Cromwell, Bob Bunge Data Communications

    NWS/OST/MDL Jerry Gorline, Marc Saccucci, Dev. Verification, NDGD Product Development

    Tim Boyer, Dave Ruth

    NWS/OST Ken Carey, Kyle Wedmark Program Support

    NESDIS/NCDC Alan Hall Product Archiving


    Jeff McQueen, Youhua Tang, Marina Tsidulko, AQF model interface development, testing, & integration

    Jianping Huang

    *Sarah Lu , Ho-Chun Huang Global data assimilation and feedback testing

    *Brad Ferrier, *Dan Johnson, *Eric Rogers, WRF/NAM coordination

    *Hui-Ya Chuang

    Geoff Manikin Smoke Product testing and integration

    Dan Starosta, Chris Magee NCO transition and systems testing

    Robert Kelly, Bob Bodner, Andrew Orrison HPC coordination and AQF webdrawer


    Pius Lee, Rick Saylor, Daniel Tong , CMAQ development, adaptation of AQ simulations for AQF

    TianfengChai, FantineNgan, Yunsoo Choi,

    Hyun-Cheol Kim, Yunhee Kim, Mo Dan

    Roland Draxler, Glenn Rolph, Ariel Stein HYSPLIT adaptations

    NESDIS/STARShobhaKondragunta, JianZeng Smoke Verification product development

    NESDIS/OSDPD Matt Seybold, Mark Ruminski HMS product integration with smoke forecast tool

    EPA/OAQPS partners:

    Chet Wayland, Phil Dickerson, Scott Jackson, Brad Johns AIRNow development, coordination with NAQFC

    • * GuestContributors


    Operational aq forecast guidance www weather gov aq
    Operational AQ Forecast

    CONUS Ozone Expansion Implemented in September 2007

    AK and HI implemented in September 2010

    Smoke ProductsCONUS implemented in March 2007

    AK implemented September 2009

    HI implemented in February 2010

    Further information:

    Smoke forecast tool alaska example
    Smoke forecast tool: Alaska example

    • Very active fire season over Alaska in 2009

    • More than 2,950,000 acres burned

    GOES smoke product: Confirms areal extent of peak concentrations

    FMS = 35%, for column-averaged smoke > 1 ug/m3

    • 7/13/09, 17-18Z, Observation:

    7/13/09, 17-18Z, Prediction:

    • Real-time objective verification:

      • Based on NOAA/NESDIS satellite imagery:

        • GOES Aerosol Smoke Product

        • Smoke from identified fires only

      • Filtered for interference:

        • Clouds, surface reflectance, solar angle, other aerosol

      • “Footprint” comparison:

        • Threshold concentration (1 µg/m3) for average smoke in the column

        • Tracking Threat scores, or Figure-of-merit statistics (FMS):

    July 2009

    Target skill is 0.08


    • (Area Pred Area Obs)

    • ----------------------------------

    • (Area Pred. U Area Obs)