national air quality forecasting capability: nationwide prediction and future enhancements ivanka...
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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 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 upgrades
Components 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/
6
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
7
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
0.99
0.97 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
0.99
0.990.97
0.96 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)
8
Seasonal ozone bias for CONUS
Julian Day0 50 100 150 200 250 300 350
Mar MayJan NovJuly Sep
Julian Day
Ozo
ne
Mean
Bia
s(p
pbv)
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
9
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/
10
Surface Smoke Surface Smoke Surface Smoke
Column Smoke Column Smoke Column Smoke
Smoke Prediction Example: Four Mile Canyon Fire
11
“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
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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
14
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
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
17
Bia
s ra
tio
Equ
itab
le t
hrea
t sc
ore
[%] 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
18
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
20
Target operational implementation of initial quantitative total PM2.5 forecasts for northeastern US in 2015
21
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 Ruth
NWS/OST Ken Carey, Kyle Wedmark Program SupportNESDIS/NCDC Alan Hall Product Archiving
NWS/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/ARL
Pius Lee, Rick Saylor, Daniel Tong , CMAQ development, adaptation of AQ simulations for AQF
Tianfeng 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 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
* 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
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):
24
• Very active fire season over Alaska in 2009
• More than 2,950,000 acres burned7/13/09, 17-18Z, Prediction:
GOES smoke product: Confirms areal extent of peak concentrations
FMS = 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