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 Service2 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

Overview

• Background• Recent Progress

• Ozone• Smoke• Dust• PM2.5

• Feedback and Outreach• Summary and Plans

2

Overview

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

• Improving the basis for AQ alerts• Providing AQ information for people at risk

3

Near-term Targets:• Higher resolution prediction

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 upgradesDust predictions

• Developmental testing: Ozone upgradesComponents for particulate matter (PM) forecasts

2005: O3

2007: O3,& smoke

6

2010smokeozone

2009: smoke2010: ozone

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

4

Model Components: Linked numerical prediction systemOperationally integrated on NCEP’s supercomputer• NCEP mesoscale NWP: WRF-NMM• NOAA/EPA community model for AQ: CMAQ • NOAA HYSPLIT model for smoke predictionObservational Input: • NWS weather observations; NESDIS fire locations• EPA emissions inventory/monitoring data

Gridded forecast guidance products• On NWS servers: www.weather.gov/aq 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

5

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

OZONE

Operational predictions at http://www.weather.gov/aq/

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1-Hr Average Ozone 1-Hr Average Ozone 1-Hr Average Ozone

8-Hr Average Ozone 8-Hr Average Ozone 8-Hr Average Ozone

                                                                                                                  

Near-real-time Ozone Verification

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4/1/09 5/1/09 5/31/09 6/30/09 7/30/09 8/29/090.8

0.85

0.9

0.95

1

0.99

0.990.97 0.97 0.95

4/1/10 5/1/10 5/31/10 6/30/10 7/30/10 8/29/100.8

0.85

0.9

0.95

1

0.990.99

0.970.96 0.95

Year 2009

Year 2010

skill target: fraction correct ≥ 0.9

• 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

Chemical mechanism sensitivity analysis

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)

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Seasonal ozone bias for CONUS

Julian Day0 50 100 150 200 250 300 350

Mar MayJan NovJuly Sep

Julian Day

Ozon

eM

ean

Bias

(ppb

v)

0 50 100 150 200 250 300 350

-10

-5

0

5

10

15

20

CBIVCB05

Mechanism differences Ozone production Precursor budget

Seasonal input differences Emissions Meteorological parameters

in 2009

Operational (CB-IV)

Experimental (CB05)

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

ppb

WRF/NMM-CMAQ NMMB-CMAQ

Impact of NMM-B meteorologyAugust 10, 2010 example

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

Overprediction case highlighted by Michael Giegert, CT DEP

SMOKE

Operational predictions at http://www.weather.gov/aq/

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Surface Smoke Surface Smoke Surface Smoke

Column Smoke Column Smoke Column Smoke

                                                                                                                  

Smoke Prediction Example: Four Mile Canyon Fire

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“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.”

September 7-8, 2010

DUST

Experimental predictions at http://www.weather.gov/aq-expr/

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Surface Dust Column Dust

                                                                                                                  

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Developmental 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)

CONUS Dust Predictions: Experimental Testing

Dust simulation compared with IMPROVE data

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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.

(Draxler et al., JGR, 2010)

June-July 2007 average dust concentration

PM2.5

Developmental predictions

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1h PM2.5

                                                                                                                  

Developmental predictions, Summer 2010

Aerosols over CONUS From National emissions inventory

(NEI) sources only· CMAQ:

CB05 gases, AERO-4 aerosols· sea salt emissions and

reactionsSeasonal bias:• winter, overprediction• summer, underprediction

Testing of real-time wildfire smoke emissions in CMAQ

1629-Mar 5-Apr 12-Apr 19-Apr 26-Apr 3-May 10-May 17-May 24-May 31-May 7-Jun 14-Jun 21-Jun 28-Jun 5-Jul 12-Jul 19-Jul 26-Jul 2-Aug 9-Aug 16-Aug 23-Aug 30-Aug

0.50

0.60

0.70

0.80

0.90

1.00

Fraction Correct, Aerosol Predictions, 0600 UTC Daily Maximum of 1-h avg, Full 5X Domain, Th=35 mg/m3

Fraction Cor-rect

Summer 2010

Assimilation of surface PM data• Assimilation

increases correlation between forecast and observed PM2.5

• Control is forecast without assimilation

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Bia

s ra

tio

Equ

itabl

e th

reat

sco

re [%

] Assimilation reduces bias and increases equitable threat score

Pagowski et al QJRMS, 2010

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

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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

http://www.epa.gov/airnow/airaware/

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

AQ Forecaster Feedback Examples

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

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

Summary

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

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

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Acknowledgments: AQF Implementation Team Members

Special thanks to Paula Davidson, OST chief scientist and former NAQFC ManagerNOAA/NWS/OST Tim McClung NAQFC ManagerNOAA/OAR Jim Meagher NOAA AQ Matrix ManagerNWS/OCWWS Jannie Ferrell Outreach, FeedbackNWS/OPS/TOC Cindy Cromwell, Bob Bunge Data CommunicationsNWS/OST/MDL Jerry Gorline, Marc Saccucci, Dev. Verification, NDGD Product Development

Tim Boyer, Dave RuthNWS/OST Ken Carey, Kyle Wedmark Program SupportNESDIS/NCDC Alan Hall Product ArchivingNWS/NCEP

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 ChuangGeoff Manikin Smoke Product testing and integrationDan Starosta, Chris Magee NCO transition and systems testingRobert Kelly, Bob Bodner, Andrew Orrison HPC coordination and AQF webdrawer

NOAA/OAR/ARLPius Lee, Rick Saylor, Daniel Tong , CMAQ development, adaptation of AQ simulations for AQFTianfeng Chai, Fantine Ngan, Yunsoo Choi,Hyun-Cheol Kim, Yunhee Kim, Mo Dan Roland Draxler, Glenn Rolph, Ariel Stein HYSPLIT adaptations

NESDIS/STAR Shobha Kondragunta, Jian Zeng Smoke Verification product developmentNESDIS/OSDPD Matt Seybold, Mark Ruminski HMS product integration with smoke forecast toolEPA/OAQPS partners:Chet Wayland, Phil Dickerson, Scott Jackson, Brad Johns AIRNow development, coordination with NAQFC

* Guest Contributors

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Operational AQ Forecast Guidancewww.weather.gov/aq

Further information: www.nws.noaa.gov/ost/air_quality

Smoke ProductsCONUS implemented in March 2007 AK implemented September 2009HI implemented in February 2010

CONUS Ozone Expansion Implemented in September 2007AK and HI implemented in September 2010

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Backup

Smoke forecast tool: Alaska example

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):

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• Very active fire season over Alaska in 2009• More than 2,950,000 acres burned

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

GOES smoke product: Confirms areal extent of peak concentrationsFMS = 35%, for column-averaged smoke > 1 ug/m3

July 2009

Target skill is 0.08

(Area Pred Area Obs) ---------------------------------- (Area Pred. U Area Obs)

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

APred

AObs