Expansion of the National Air Quality Forecast Capability Ivanka Stajner1, Jeff McQueen2, Pius Lee3, Roland Draxler3, Phil Dickerson4, Kyle Wedmark1,5,

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Presentation transcript:

Expansion of the National Air Quality Forecast Capability Ivanka Stajner1, Jeff McQueen2, Pius Lee3, Roland Draxler3, Phil Dickerson4, Kyle Wedmark1,5, Tim McClung1 1) NOAA NWS/OST, 2) NOAA NWS/NCEP, 3) NOAA ARL, 4) EPA, 5) Noblis - Background on NAQFC - Progress in 2011: Ozone, Smoke, Dust, PM2.5 - Looking Ahead CMAS, Special session on Air Quality Modeling Applications in memory of Daewon Byun Chapel Hill, October 24, 2011

National Air Quality Forecast Capability Current and Planned Capabilities, 10/11 Improving the basis for AQ alerts Providing AQ information for people at risk FY11 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 predictions Dust predictions over CONUS Developmental testing: Ozone upgrades Components for particulate matter (PM) forecasts 2005: O3 2007: O3,& smoke 6 2010 smoke ozone 2009: smoke 2010: ozone Near-term Operational Targets: Higher resolution prediction Longer range: Quantitative PM2.5 prediction

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-NMMB NOAA/EPA community model for AQ: CMAQ NOAA HYSPLIT model for smoke prediction Observational Input: NWS weather observations; NESDIS fire locations EPA emissions inventory Gridded forecast guidance products On NWS servers: airquality.weather.gov 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 AIRNow

Progress in 2011 Ozone, smoke nationwide; dust testing Ozone Upgrades: Nationwide predictions since 9/10 Operations: Updated emissions for 2011 (CEM point sources, DOE projection factors); Predictions driven by a new NMMB meteorological model (since 10/18/11) Experimental testing: CB-05 mechanism Developmental testing: Changing boundary conditions, dry deposition, PBL in CB-05; testing newer CMAQ 4.7.1 driven by NMMB meteorological model Smoke: Expanded Forecast Guidance Nationwide Developmental testing: Improvements to verification Aerosols: Developmental testing providing comprehensive dataset for diagnostic evaluations. (CONUS) Testing CMAQ 4.6 and 4.7.1 with 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 Experimental testing of dust prediction from CONUS sources Real-time testing of assimilation of surface PM2.5 measurements R&D efforts continuing in chemical data assimilation, real-time emissions sources, advanced chemical mechanisms Emission updates: 1) Updated CEM point source data from 2008 to 2009. 2) Updated DOE projection factors from 2010 to 2011.  3) Changed the number of electricity marketing module (EMMs) from 13 to 22, consistent with the changes made in DOE models. 4) Removed climatological forest fire emissions in experimental ozone predictions Updates in CMAQ 4.7: (a) updates to the heterogeneous N2O5 parameterization, (b) improvement in the treatment of secondary organic aerosol (SOA), (c) inclusion of dynamic mass transfer for coarse-mode aerosol, (d) revisions to the cloud model, and (e) new options for the calculation of photolysis rates.

Operational Nationwide Ozone Operational predictions at http://airquality.weather.gov/ 1-Hr Average Ozone 1-Hr Average Ozone 1-Hr Average Ozone 8-Hr Average Ozone 8-Hr Average Ozone 8-Hr Average Ozone

CONUS ozone prediction: Summary verification 2009 to 2011 CONUS, wrt 76ppb Threshold Operational 2010 CONUS, wrt 76ppb Threshold Operational 0.98 2011 CONUS, wrt 76ppb Threshold 0.99 Operational 0.96 0.95 0.94 See verification of experimental CB05 predictions (Jerry Gorline, Tuesday 2pm), and developmental predictions CMAQ 4.7.1 (Yunsoo Choi, Wednesday 9:30 am)

Chemical mechanism sensitivity analysis Experimental ozone prediction (with CB05 mechanism) shows larger biases than operational ozone prediction (CB-IV mechanism) Summertime, Eastern US. Sensitivity studies in progress: Box model studies, e.g. organic nitrate treatment (Saylor and Stein) Emission sensitivity (Poster by Yunsoo Choi) Seasonal bias of daily mean ozone for CONUS Operational (CB-IV) Experimental (CB05) Mechanism differences Ozone production Precursor budget Seasonal input differences Emissions Meteorological parameters in 2009

Impact of meteorological model Operational NAM driven ozone predictions Testing of NMMB driven ozone predictions Observed monitor values in the circles Impacts of NMMB meteorology and land use changes on air quality predictions discussed by Jeff McQueen, and see posters by Marina Tsidulko and Jianping Huang 8

Operational Nationwide Smoke Operational predictions at http://airquality.weather.gov/ Surface Smoke Vertical Smoke

Wallow Wildfire in Arizona June 6 (Luna, NM, AP) Wallow fire, June 3 1 Fire began May 29, 2011, consumed more then 500,000 acres and 32 homes3 16 injuries3; more then 9000 people evacuated 2 60mph winds contributed to its rapid spread 1 Code orange air quality forecast issued for Albuquerque for 4 days (June 6,8,9,13) - NWS prediction included high smoke concentrations Hazardous air quality predicted by NAQFC and observed in Springerville, AZ NAQFC joined coordination calls with state, local, federal agencies in AZ and NM, and USFS Fire Science Lab in Seattle Animation of NOAA’s prediction of smoke concentrations in column Eastern Arizona wildfire still rages, AP, June 5, 2011. http://www.latimes.com/news/la-na-arizona-wildfires-20110606-pictures,0,6897558.photogallery As Arizona wildfire rages, officials allow some to return home, CNN, June 12, 2011 http://www.cnn.com/2011/US/06/12/arizona.wildfires/ InciWeb, Incident information system, accessed on Oct. 23, 2011 http://www.inciweb.org/incident/article/2262/ 10

June 6, 2011 MODIS NWS smoke prediction Wallow North fire, Arizona : 2011/157 - 06/06 at 20:40 UTC Aqua 1km pixel size Vertical column smoke at 21 Z on 06/06 11

Albuquerque, NM Surface PM2.5 observations http://www.airnowtech.org/ NOAA’s predictions of surface smoke concentrations airquality.weather.gov 12 11 Z on 06/08/2011 04 Z on 06/09/2011

Verification of smoke predictions Daily time series of FMS for smoke concentrations larger than 1um/m3 APR AOV AOB June 2011 May 2011 May 2011 Figure of merit in space (FMS), which is a fraction of overlap between predicted and observed smoke plumes, exceeds 0.08 in the western US since May 29 when Wallow wildfire began. NESDIS GOES Aerosol/Smoke Product is used for verification 13

CONUS Dust Predictions: Experimental Testing Experimental predictions at http://airquality.weather.gov/expr/ 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) Emissions modulated by soil moisture in developmental testing. Developmental testing 14

Phoenix, AZ Haboob July 5, 2011 Massive dust storm hit Phoenix, AZ in the evening on July 5, 20112 Cloud was reported to be 5,000 feet when it hit, radar shows heights from 8,000-10,000 feet tall and 50 miles wide Originated from Tucson Stopped air traffic for over an hour Arizona DEQ reported a PM10 concentration of 6,348 ug/m3 during peak of storm at site in downtown Phoenix1 Storm moved through Phoenix at 30-40 mph1 Source: http://www.wrh.noaa.gov/psr/pns/2011/July/DustStorm.php “More Phoenix storms forecast after huge evening dust storm,” http://www.azcentral.com/community/phoenix/articles/2011/07/06/20110706phoenix-dust-storm-weather-abrk.html Phoenix Dust Storm: Arizona Hit with Monstrous ‘Haboob’, http://www.huffingtonpost.com/2011/07/06/phoenix-dust-storm-photos-video_n_891157.html Source: http://www.huffingtonpost.com/2011/07/06/phoenix-dust-storm-photos-video_n_891157.html 15

Phoenix Observed PM 2.5 Observations Based on observations, largest impact on Phoenix was between 8 PM and 10 PM LST Predicted surface dust concentrations: - 8PM 158 ug/m3 - 10PM 631 ug/m3 *Note* AZ three hours behind EDT in summer Timing of July 5 storm: beginning time of predicted storm is correct, but predicted storm extends past observed ending For dust storm on October 4 near Picacho, AZ developmental predictions (with more sources and emissions modulated by real time soil moisture performed better). This configuration is targeted for experimental testing on November 1. Peak values: 996 ug/m3 at South Phoenix station at 8PM and 506 ug/m3 at West Phoenix station at 8 PM (AIRNow Tech) 16

Developmental PM2.5 predictions for Summer 2011 Focus group access only, real-time as resources permit Aerosols over CONUS From NEI sources only CMAQ: CB05 gases, AERO-4 aerosols Sea salt emissions and reactions No climatological wildfire emissions Testing of real-time wildfire smoke emissions in CMAQ

Quantitative PM performance Forecast challenges Aerosol simulation using emission inventories: Seasonal bias: winter - overprediction; summer – underprediction Speciated PM2.5 component biases (poster by Yunhee Kim) Intermittent sources (contributions of wildfire smoke are in real time testing; also evaluation in poster by Hyun Cheol Kim) Chemical boundary conditions/trans-boundary inputs (Poster by Youhua Tang; Pius Lee’s talk at 3:20pm today)

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 19

Summary US national AQ forecasting capability status: Ozone prediction nationwide (AK and HI since September 2010; NMMB meteorology since October 18, 2011) 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 Development of higher resolution predictions (optimization of prediction code, I/O on NCEP operational computers; faster preparation of emission inputs – poster by Hyun Cheol Kim)

Plans for PM2.5 Development and integration of components for quantitative PM predictions: Integration of NEI, smoke and dust sources; inventory updates Data assimilation, bias correction; starting with surface PM monitor data Inclusion of lateral boundaries from global model predictions Testing advanced chemical mechanisms; evaluation of PM speciation Closer coupling of meteorological and chemical models Target operational implementation of initial quantitative total PM2.5 forecasts for northeastern US in FY16

Acknowledgments: AQF Implementation Team Members Special thanks to Paula Davidson, OST chief scientist and former NAQFC Manager NOAA/NWS/OST Ivanka Stajner NAQFC Manager NOAA/OAR Jim Meagher NOAA AQ Matrix Manager NWS/OCWWS Jannie 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 Kyle Wedmark, Ken Carey Program Support NESDIS/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 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 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 Liqun Ma, Mark Ruminski HMS product integration with smoke forecast tool EPA/OAQPS partners: Chet Wayland, Phil Dickerson, Brad Johns AIRNow development, coordination with NAQFC * Guest Contributors

Operational AQ forecast guidance airquality.weather.gov CONUS Ozone Expansion Implemented September, 2007 Smoke Products Implemented March, 2007 Further information: www.nws.noaa.gov/ost/air_quality

Backup

Real-time Verification Approach for Ozone Real-time verification of data Comparing model with observed values from AIRNow ground level observations Daily maximum of 8-hour ozone Metric is the fraction correct with respect to threshold commonly used for “code orange” AQ alerts in the US (currently 75 ppb) Skill target for fraction correct ≥ 0.9 Threshold Prediction Observations a c b d Fraction Correct = (a+c)/(a+b+c+d)

Progress from 2005 to 2008: Ozone Prediction Summary Verification Initial Operational Capability (IOC) Operational, NE US Domain EUS EUS 2006 Operational, Eastern US Operational CONUS 2007 Experimental, Contiguous US Approved 9/07 to replace Eastern US config in operations Experimental 2008 CONUS, wrt 85ppb Threshold Operational CONUS

Ozone sensitivity in the CB05 system: Experimental and developmental configuration Model-minus-AIRNow observations: mean for daytime in August 2009 Experimental test configuration Developmental test configuration with modified: - Lateral boundary conditions, - Minimum PBL height, - Aerodynamic resistance, forest canopy height ppbv In depth: Byun et al. in the Processes session

Smoke Forecast Tool: What is it? Overview Passive transport/dispersion computed with HYSPLIT & WRF-NAM (or GFS, OCONUS). 24-hr spin-up, 48-hour prediction made daily with 6Z cycle Fire Locations NESDIS/HMS: Filtered ABBA product (only fires with observed associated smoke) Emissions USFS’ BlueSky algorithm for emitted PM2.5 Smoke Transport/dispersion HYSPLIT (Lagrangian); plume rise based on combustion heat and meteorology Verification Based on satellite imagery for footprint of extent of observed smoke in atmospheric column exceeding threshold of detection 28

Smoke Forecast Tool Major Components NWP Model NAM/WRF-NMM NOAA/NWS Weather Observations NESDIS HMS Fire Locations USFS’s BlueSky Emissions Inventory: USFS HYSPLIT Module: NOAA/OAR Verification: NESDIS/GASP Smoke 29 29

Dust simulation compared with IMPROVE data June-July 2007 average dust concentration 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) 30

Developmental Aerosol Predictions: Summary Verification, 2011 April 1, 2011 July 14, 2011

AQ Forecaster Feedback Examples Michael Geigert, CT Department of Environmental Protection: Analyzed case on July 11 AIRNow data vs 12Z operational ozone model run Model still over-predicting in most cases, however not unreasonable Model does not yet have the resolution to pick up hourly fluctuations of plume location and intensity Still most problematic at coastal sites, but it still performed very well overall Modeled Examples of model overprediction 32