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Enhancing Air Quality Monitoring with Satellite Observations

Utilizing satellite observations to improve understanding and forecasting of air quality, verifying data accuracy, assessing health impacts, and enhancing operational models for pre-positioning. Collaborative efforts by several institutions have led to significant advancements in global air quality monitoring.

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Enhancing Air Quality Monitoring with Satellite Observations

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  1. Applications of satellite observations for understanding, verifying, improving and prepositioning NAQFC Pius Lee1, Jeff McQueen2, Ivanka Stajner3,Shobha Kondragunta4, Daniel Tong1,5,6, Li Pan1,5, Jianping Huang2,7, Youhua Tang1,5, Tianfeng Chai1,5, Ho-Chun Huang2,7, Greg Carmichael8, Brad Pierce9, Ted Russell10, Dick McNider11, Yang Liu12, Jim Szykman13, Ed Hyer14, and David Edwards15 1) NOAA ARL; 2) NOAA NCEP; 3) NOAA NWS/OSTI; 4) NOAA/NESDIS/STAR; 5) UMD CICS; 6) GMU CSISS; 7) IMSG, Rockville, MD; 8) U. of Iowa; 9) NOAA/NESDIS; 10) Georgia Tech.; 11) U. Alabama, Huntsville; 12) Emory U. 13) U.S. EPA Office R&D; 14). NRL, MMD; 15) NCAR, ACOM Air Resources Laboratory

  2. EPA NAAQS O3 standards at 70 ppb since Dec 28 2015 NAQFC vital service U.S. air quality has improved dramatically, but 138.5 M people still live in areas where “pollution levels are too often dangerous to breathe”

  3. Global Assimilation Satellite Products 2013 – 2016 : AQAST Tiger Team: Air Quality Reanalysis (Translating Research to Services) + AQ Assessments +State Implementation Plan Modeling + Rapid deployment of on-demand rapid-response forecasting; e.g., new fuel type,…, etc. + Health Impacts assessments + Demonstration of the impact of observations on AQ distributions + Ingestion of new AQAST products into operations Constrained http://acmg.seas.harvard.edu/aqast/projects.html 2011-2016 Air Resources Laboratory 3

  4. 12z July 2 2011 forecast valid for 18z on same day: NAQFC setup. Color-shading for Surface PM2.5 (μg m-3) Color bar for bias w.r.t. AIRNow (μg m-3) • Same as a) but with data assimilation adjusted Initialization. Large reduction in underestimation of PM2.5 w.r.t. a) Tang et al., 2015: Using optimal interpolation to assimilate surface measurements and satellite AOD for ozone and PM2.5: A case study for July 2011, JAWMA, 65, 1206-1216

  5. Campaign AQ Forecasting win win Win Win 4 DISCOVER-AQ Campaigns Pickering and Lee, EM 2014; Garner, Thompson & Lee Atm Chem 2013 O3 and PM…. AGU Session 13333 Conveners: Newchurch, Langford, Johnson, & Sullivan AGU Session 12929 Conveners: Frost, Kopacz, & Wang

  6. Campaign AQ Forecasting Win Win NOAA’s Southeastern Nexus Study (SENEX) - June-July 2013 4 km domain nested within the 12 km NAQFC SENEX and GOAMAZON…. AGU Session 12627 Conveners: Joost de Gouw (NOAA) et al. AGU Session: 12821 Conveners: Jesse Kroll (MIT); Jennifer Kaiser, Frank Keutsch (Harvard U.)

  7. Bases on GOES GOES Aerosol and Smoke Product (GASP)-based derived CSI for smoke-associated conc 6/1-7/5 2016 ( Operational vs Experimental) Verification – Satellite-based obs grid-2-grid Bases on AIRNow O3: NAQFC vs NAQFC-β PM2.5: NAQFC vs NAQFC-β Air Resources Laboratory

  8. Intermittent emissions by satellite-based obs Difference between two PM2.5 predictions: with-minus-without fire emissions NOAA NESDIS Hazard Mapping System Fire and Smoke Analysis Air Resources Laboratory

  9. Modulation of emissions by satellite-based obs Suppression emission of fugitive dust over Ice/snow in Jan 2015 Snow Implemented 2016/02 PM2.5 (μg m-3) Account for wind blown dust sources in May 2014 Implemented 2016/02 PM2.5 (μg m-3) dust Air Resources Laboratory

  10. Intruded dust plumes by satellite-based obs Sahara dust event May 9-11 2015 VIIRS AOD 12 UTC May 9 12 UTC May 10 12 UTC May 11 Air Resources Laboratory

  11. Satellite-obs enable rapid refresh for emission • Emission Indicator – Urban NOx in Summer • Short lifetime  proximity to emission sources • Urban NO2 dominated by local sources; • High emission density  low noise/signal ratio; • NOx Data sources • Satellite remote sensing (OMI-Aura NO2). • Ground monitoring (EPA AQS NOx); • Emission data ( NAQFC emissions); • Deriving the trend: (Y2–Y1)/Y1×100% • Selection of urban areas Monthly averaged CMAQ modeled NOx using NEI2011 Air Resources Laboratory TEMPO Workshop Huntsville July12-13 2016

  12. Satellite-obs considerations One of four sensors on the EOS-Aura platform (OMI, MLS, TES, HIRDLS) Launched on 07-15-04 Courtesy of OMAR Torres Instrument Characteristics -Nadir solar backscatter spectrometer -Spectral range 270-500 nm (resolution~0.6 nm ) • Spatial resolution: 13X24 km footprint • Swath width: 2600 km (global daily coverage) • 13:45 (+/- 15 min) Local equator crossing time (ascending node) Data Quality Control - VCD quality flag; - Cloud fraction; • Row Anomaly; • Outliners (5% at each end) Air Resources Laboratory TEMPO Workshop Huntsville July12-13 2016

  13. OMI Observed NOx Change (July) Courtesy: Lok Lamsal

  14. Inter-Comparison of OMI, AQS and NAQFC Atlanta Boston NOx Change from 2005 Level (%) Dallas Houston (Source: Tong et al., AE, 2015)

  15. Rapid Refresh of NOx Emissions Question: Can satellite and ground data be used to rapidly refresh NOx emissions? Fusing AQS & OMI State-level Projection Factors AQS stations (2005 to 2012) OMI weighs 1/100* AQS (Source: Tong et al., GRL, 2016)

  16. Pre-position NAQFC via NUOPC Chemical Analysis: homogeneously generated fields over multiple years NAQFC in finer resolutions: Chemically, spatially and temporally Tight linkage with global CTMs e.g. NGGPS Air chemistry as one of the NOAA Earth Modeling System Framework components – an integrated holistic earth modeling system example example example Air Resources Laboratory

  17. Summary Applications of satellite observations to: • UNDERSTAND AQ modeling through verification, chemical data assimilation; and corroboration with vertically-resolved AQ campaign intensives; • EXPLORE the efficacy of using observation to capture, modulate and quantify intermittent internal or external emissions for NAQFC; • QUANTIFY NO2 changes to allow rapidly adjusting NOx over EPA NEIs; • USE NEMS Global Aerosol Component (NGAC) as an global CTM to guide refinement of the vertical grid of NAQFC and linkage to large scale forcing; • LEVERAGE the NUOPC opportunity: CMAQ joins the ESMF world via the National Unified Operational Prediction Capability (NUOPC). ARL has succeeded to couple NAM per sync time-step (5 minutes) with CMAQ5.1 via NUOPC. The developed NUOPC modules can easily be adapted for a similar in-line NGGPS-CMAQ. This preposition NAQFC as an potential entity in an earth modeling system – a multiple disciplinary, multiple-feedback integrated earth modeling system. Air Resources Laboratory 17

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