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1 Jim Crawford  1 , Ken Pickering 1 Jim Crawford  1 , Ken Pickering

1 Jim Crawford 1 , Ken Pickering - PowerPoint Presentation

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1 Jim Crawford 1 , Ken Pickering - PPT Presentation

1 Jim Crawford 1 Ken Pickering 2 Bruce Anderson 1 Andreas Beyersdorf 1 Gao Chen 1 Richard Clark 3 Ron Cohen 4 Glenn Diskin 1 Rich Ferrare 1 Alan Fried 5 Brent Holben 2 ID: 773421

tempo surface air ozone surface tempo ozone air variability quality diurnal aerosol correlations column validation models high nasa model

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1 Jim Crawford 1, Ken Pickering 2, Bruce Anderson 1, Andreas Beyersdorf 1, Gao Chen 1, Richard Clark 3, Ron Cohen 4, Glenn Diskin 1, Rich Ferrare 1, Alan Fried 5, Brent Holben 2, Jay Herman 6, Ray Hoff 6, Chris Hostetler 1, Scott Janz 2 , Mary Kleb 1, Jim Szykman 7, Anne Thompson 2, Andy Weinheimer 8, Armin Wisthaler 9, Melissa Yang 1 1 NASA Langley Research Center, 2 NASA Goddard Space Flight Center, 3 Millersville University, 4 University of California-Berkeley, 5 University of Colorado-Boulder, 6 University of Maryland-Baltimore County, 7 Environmental Protection Agency, 8 National Center for Atmospheric Research, 9 University of Innsbruck Lessons Learned from DISCOVER-AQ(GEO-CAPE 2015 Open Community Workshop) http://discover-aq.larc.nasa.gov/

Maryland Department of the Environment (MDE) San Joaquin Valley Air Pollution Control District (SJV APCD) California Air Resource Board (CARB)Bay Area Air Quality Management District (BAAQMD)Texas Commission on Environmental Quality (TCEQ)Colorado Department of Public Health and Environment (CDPHE)Environmental Protection Agency, Office of Res. and Dev.National Center for Atmospheric ResearchNational Science FoundationNational Oceanic and Atmospheric AdministrationNational Park ServiceUniversity of Maryland, College Park; Howard UniversityUniversity of California, Davis; University of California, IrvineUniversity of Houston; Rice University; University of Texas; Baylor University; PrincetonUniversity of Colorado-Boulder; Colorado State UniversityThanks to Partners

D eriving Information on Surface Conditions from Column and VERtically Resolved Observations Relevant to Air QualityA NASA Earth Venture campaign intended to improve the interpretation of satellite observations to diagnose near-surface conditions relating to air qualityObjectives: 1. Relate column observations to surface conditions for aerosols and key trace gases O3, NO2, and CH 2O2. Characterize differences in diurnal variation of surface and column observations for key trace gases and aerosols3. Examine horizontal scales of variability affecting satellites and model calculations Investigation Overview

Deployment Strategy Systematic and concurrent observation of column-integrated, surface, and vertically-resolved distributions of aerosols and trace gases relevant to air quality as they evolve throughout the day. NASA UC-12 (Remote sensing) Continuous mapping of aerosols with HSRL and trace gas columns with ACAM NASA P-3B (in situ meas.) In situ profiling of aerosols and trace gases over surface measurement sites Ground sites In situ trace gases and aerosols Remote sensing of trace gas and aerosol columns Ozonesondes Aerosol lidar observations Three major observational components:

Deployment Locations Maryland, July 2011 Houston, September 2013 California, Jan-Feb 2013 Colorado, Jul-Aug 2014

Flight Hours and Sampling Statistics Location P-3B Hours (Flight Days)King Air Hours (Flight days)Other aircraft participationBaltimore-Washington104 (14)101 (14)UMD CessnaCalifornia, Central Valley84 (10)10 (2) - PODEX70 (10)NASA ER-2 Alpha JetUC-Davis MooneyHouston71 (9)68 (9)23 (3) - GEO-CAPENASA LaRC FalconDenver, Northern Front Range93 (16)89 (17)NCAR C-130NASA LaRC FalconTotal for all campaigns352 (49)328 (50)LocationP-3B SpiralsKing Air OverflightsMissed ApproachesBaltimore-Washington2543420California, Central Valley170307166Houston195341105Denver, Northern Front Range214451108Total for all campaigns 8331441379

Value-Added Data Products Data Merges – These files place all aircraft measurements on a common time base and are fundamental to data analysis. Data Flags – These markers indicate when the aircraft was in a particular spiral, missed approach, or site overflight and can be used to effectively filter the data.BL Heights – BL estimates were derived from aircraft in situ profiles, airborne and ground-based lidars, sondesProfile Plots – These browse products help explore the data and evaluate horizontal versus vertical gradientsColumn Densities – These integrated column amounts from in situ aircraft profiles are critical for comparison with remote sensing observationsGridded Profiles – These binned profiles allow direct comparisons, averages, or combination in other ways to create profile statistics.

Data Archive doi:10.5067/Aircraft/DISCOVER-AQ/Aerosol- TraceGashttp://discover-aq.larc.nasa.gov/ http://www-air.larc.nasa.gov/missions /discover-aq/discover-aq.html

Key Scientific Outcomes Surface-Column correlations are a mixed bag, but there is indication that lower tropospheric ozone (especially in the afternoon) can sometimes be trusted to infer surface values. Spatial variability analyses show that TEMPO precision requirements are adequate to detect air quality violations.Model representation of diurnal evolution in profile structure is expected to be an important source of error for TEMPO retrievals.Pandora validation of TEMPO diurnal variability would appear to require no more than a couple of instruments in a given metropolitan area.Formaldehyde shows promise as a proxy for ozone production.Validation of AERONET AAOD reveals a high bias that may be responsible for overprediction of black carbon by global models.Strong correlations are observed between AOD and PM2.5, but the scaling factors vary widely based on BL depth, humidity, and aerosol composition (hygroscopicity).Reactive nitrogen chemistry in air quality models needs to be improved.Ozone can be highest over unmonitored areas downwind (e.g., Chesapeake Bay).A single lidar may be sufficient to characterize the vertical distribution of aerosols over distances of up to 100 km.

NO 2 O3P-3B in situLowHighP-3B + surfaceModerateHighPandoraModerateLowCMAQModerateModerate NO2O3P-3B in situLow Low P-3B + surface Moderate Low Pandora Low Low CMAQ Moderate Low NO 2 O 3 P-3B in situ Low Low P-3B + surface Moderate Low Pandora Low N.S. CMAQ High Low NO 2 O 3 P-3B in situ Low N.S. P-3B + surface Low N.S. Pandora High High CMAQ High Moderate Summary of Column vs. Surface Correlations for NO 2 and O 3 Low correlation: R < 0.4 ; Moderate: R=0.4-0.8; High: R=0.8-1.0; N.S. = not significant Maryland California Texas Colorado Clare Flynn, U. Maryland

Surface vs Column O3 at BAO (Erie, CO): All DAQ/FRAPPE days66% of 1500 m AGL column and surface O3 values agree within 10 ppbv, almost entirely after midday, showing lower tropospheric column O3 to be a good predictor of surface conditions.34% exhibit O3 column exceeding surface values due to shallow mixing in the morning, elevated plumes, and thunderstorm outflows leading to significant vertical gradients of ozone in the lower atmosphere Christoph Senff, NOAA TOPAZ Lidar 08 09 10 11 12 13 14 15 16 17 18MDTΔO3 ≤ 10 ppbv:66% of all cases N = 2294

Key Scientific Outcomes Surface-Column correlations are a mixed bag, but there is indication that lower tropospheric ozone (especially in the afternoon) can sometimes be trusted to infer surface values. Spatial variability analyses show that TEMPO precision requirements are adequate to detect air quality violations.Model representation of diurnal evolution in profile structure is expected to be an important source of error for TEMPO retrievals.Pandora validation of TEMPO diurnal variability would appear to require no more than a couple of instruments in a given metropolitan area.Formaldehyde shows promise as a proxy for ozone production.Validation of AERONET AAOD reveals a high bias that may be responsible for overprediction of black carbon by global models.Strong correlations are observed between AOD and PM2.5, but the scaling factors vary widely based on BL depth, humidity, and aerosol composition (hygroscopicity).Reactive nitrogen chemistry in air quality models needs to be improved.Ozone can be highest over unmonitored areas downwind (e.g., Chesapeake Bay).A single lidar may be sufficient to characterize the vertical distribution of aerosols over distances of up to 100 km.

Ozone Gradients versus TEMPO Precision Requirement MD CA TX COTEMPO PRMD, TX, and CO 75th percentile curves above the PR at distances of 8 (TX), 12 (CO), and 16 km (MD)All campaigns 95th percentile curves above the PRMelanie Follette-Cook, NASA GSFC

Melanie Follette-Cook, NASA GSFC NO 2 Gradients versus TEMPO Precision Requirement MD CA TX CO

Key Scientific Outcomes Surface-Column correlations are a mixed bag, but there is indication that lower tropospheric ozone (especially in the afternoon) can sometimes be trusted to infer surface values. Spatial variability analyses show that TEMPO precision requirements are adequate to detect air quality violations.Model representation of diurnal evolution in profile structure is expected to be an important source of error for TEMPO retrievals.Pandora validation of TEMPO diurnal variability would appear to require no more than a couple of instruments in a given metropolitan area.Formaldehyde shows promise as a proxy for ozone production.Validation of AERONET AAOD reveals a high bias that may be responsible for overprediction of black carbon by global models.Strong correlations are observed between AOD and PM2.5, but the scaling factors vary widely based on BL depth, humidity, and aerosol composition (hygroscopicity).Reactive nitrogen chemistry in air quality models needs to be improved.Ozone can be highest over unmonitored areas downwind (e.g., Chesapeake Bay).A single lidar may be sufficient to characterize the vertical distribution of aerosols over distances of up to 100 km.

Examples of Diurnal Changes in NO 2 Profiles

Sensitivity of NO 2 AMF to Profile Shape (Model versus Observations)Lok Lamsal, NASA GSFC

Key Scientific Outcomes Surface-Column correlations are a mixed bag, but there is indication that lower tropospheric ozone (especially in the afternoon) can sometimes be trusted to infer surface values. Spatial variability analyses show that TEMPO precision requirements are adequate to detect air quality violations.Model representation of diurnal evolution in profile structure is expected to be an important source of error for TEMPO retrievals.Pandora validation of TEMPO diurnal variability would appear to require no more than a couple of instruments in a given metropolitan area.Formaldehyde shows promise as a proxy for ozone production.Validation of AERONET AAOD reveals a high bias that may be responsible for overprediction of black carbon by global models.Strong correlations are observed between AOD and PM2.5, but the scaling factors vary widely based on BL depth, humidity, and aerosol composition (hygroscopicity).Reactive nitrogen chemistry in air quality models needs to be improved.Ozone can be highest over unmonitored areas downwind (e.g., Chesapeake Bay).A single lidar may be sufficient to characterize the vertical distribution of aerosols over distances of up to 100 km.

Diurnal Statistics for Pandora NO 2 Column Densities

Key Scientific Outcomes Surface-Column correlations are a mixed bag, but there is indication that lower tropospheric ozone (especially in the afternoon) can sometimes be trusted to infer surface values. Spatial variability analyses show that TEMPO precision requirements are adequate to detect air quality violations.Model representation of diurnal evolution in profile structure is expected to be an important source of error for TEMPO retrievals.Pandora validation of TEMPO diurnal variability would appear to require no more than a couple of instruments in a given metropolitan area.Formaldehyde shows promise as a proxy for ozone production.Validation of AERONET AAOD reveals a high bias that may be responsible for overprediction of black carbon by global models.Strong correlations are observed between AOD and PM2.5, but the scaling factors vary widely based on BL depth, humidity, and aerosol composition (hygroscopicity).Reactive nitrogen chemistry in air quality models needs to be improved.Ozone can be highest over unmonitored areas downwind (e.g., Chesapeake Bay).A single lidar may be sufficient to characterize the vertical distribution of aerosols over distances of up to 100 km.

Key Scientific Outcomes Anticipated TEMPO sensitivity for CH2O from geostationary orbit

Key Scientific Outcomes Surface-Column correlations are a mixed bag, but there is indication that lower tropospheric ozone (especially in the afternoon) can sometimes be trusted to infer surface values. Spatial variability analyses show that TEMPO precision requirements are adequate to detect air quality violations.Model representation of diurnal evolution in profile structure is expected to be an important source of error for TEMPO retrievals.Pandora validation of TEMPO diurnal variability would appear to require no more than a couple of instruments in a given metropolitan area.Formaldehyde shows promise as a proxy for ozone production.Validation of AERONET AAOD reveals a high bias that may be responsible for overprediction of black carbon by global models.Strong correlations are observed between AOD and PM2.5, but the scaling factors vary widely based on BL depth, humidity, and aerosol composition (hygroscopicity).Reactive nitrogen chemistry in air quality models needs to be improved.Ozone can be highest over unmonitored areas downwind (e.g., Chesapeake Bay).A single lidar may be sufficient to characterize the vertical distribution of aerosols over distances of up to 100 km.

LARGE and AERONET Teams Absorption aerosol optical depth (AAOD) was calculated from in-situ measurements for each vertical profileComparisons with AERONET yielded significantly lower in-situ AAOD values at each of the DISCOVER-AQ urban sitesThis discrepancy is compounded by model overestimates in remote regions by factors of 2-5 (Schwarz et al., 2013)No evidence was observed to support model up-scaling by Bond et al. (2013)Profiles of Aerosol Absorption Suggest Model/AERONET Over-predictions Luke Ziemba, NASA LaRC

Key Scientific Outcomes Surface-Column correlations are a mixed bag, but there is indication that lower tropospheric ozone (especially in the afternoon) can sometimes be trusted to infer surface values. Spatial variability analyses show that TEMPO precision requirements are adequate to detect air quality violations.Model representation of diurnal evolution in profile structure is expected to be an important source of error for TEMPO retrievals.Pandora validation of TEMPO diurnal variability would appear to require no more than a couple of instruments in a given metropolitan area.Formaldehyde shows promise as a proxy for ozone production.Validation of AERONET AAOD reveals a high bias that may be responsible for overprediction of black carbon by global models.Strong correlations are observed between AOD and PM2.5, but the scaling factors vary widely based on BL depth, humidity, and aerosol composition (hygroscopicity).Reactive nitrogen chemistry in air quality models needs to be improved.Ozone can be highest over unmonitored areas downwind (e.g., Chesapeake Bay).A single lidar may be sufficient to characterize the vertical distribution of aerosols over distances of up to 100 km.

Aerosol Variability in Baltimore Two Cases Extinctionambient varies by 10-18% (highest at Fairhill)Extinctiondry is a measure of aerosol loadingCase 1: variability in Extinctionambient is controlled by variability in aerosol loadingCase 2: variability in Extinctionambient is greater than variability in aerosol loadingRH is high and variable resulting in variable water uptakeAirborne measurements in the boundary layer (<1 km) over each site.Andreas Beyersdorf, NASA LaRC

Key Scientific Outcomes Surface-Column correlations are a mixed bag, but there is indication that lower tropospheric ozone (especially in the afternoon) can sometimes be trusted to infer surface values. Spatial variability analyses show that TEMPO precision requirements are adequate to detect air quality violations.Model representation of diurnal evolution in profile structure is expected to be an important source of error for TEMPO retrievals.Pandora validation of TEMPO diurnal variability would appear to require no more than a couple of instruments in a given metropolitan area.Formaldehyde shows promise as a proxy for ozone production.Validation of AERONET AAOD reveals a high bias that may be responsible for overprediction of black carbon by global models.Strong correlations are observed between AOD and PM2.5, but the scaling factors vary widely based on BL depth, humidity, and aerosol composition (hygroscopicity).Reactive nitrogen chemistry in air quality models needs to be improved.Ozone can be highest over unmonitored areas downwind (e.g., Chesapeake Bay).A single lidar may be sufficient to characterize the vertical distribution of aerosols over distances of up to 100 km.

D. LueckenAlkyl nitrates compared to DISCOVER-AQ measurements Updating organic nitrates improves comparison with measurements, but still high. Experiments adding hydrolysis and lowered nitrate yield bring model closer to observationsmeasurementsCMAQ v5.1CMAQ v5.02measurementsCMAQ v5.1CMAQ v5.1 w/hydrolysisCMAQ v5.1 with hydrolysis and lowered yieldCMAQ 5.02 containedone alkyl nitrate to represent the entire classof compoundsCMAQ 5.1 contains 7alkyl nitrates of variousreactivities, solubilities,and photolysis rates

Key Scientific Outcomes Surface-Column correlations are a mixed bag, but there is indication that lower tropospheric ozone (especially in the afternoon) can sometimes be trusted to infer surface values. Spatial variability analyses show that TEMPO precision requirements are adequate to detect air quality violations.Model representation of diurnal evolution in profile structure is expected to be an important source of error for TEMPO retrievals.Pandora validation of TEMPO diurnal variability would appear to require no more than a couple of instruments in a given metropolitan area.Formaldehyde shows promise as a proxy for ozone production.Validation of AERONET AAOD reveals a high bias that may be responsible for overprediction of black carbon by global models.Strong correlations are observed between AOD and PM2.5, but the scaling factors vary widely based on BL depth, humidity, and aerosol composition (hygroscopicity).Reactive nitrogen chemistry in air quality models needs to be improved.Ozone can be highest over unmonitored areas downwind (e.g., Chesapeake Bay).A single lidar may be sufficient to characterize the vertical distribution of aerosols over distances of up to 100 km.

Surface Ozone over Bay vs Land Possible reasons: Dan Goldberg, UMD

Key Scientific Outcomes Surface-Column correlations are a mixed bag, but there is indication that lower tropospheric ozone (especially in the afternoon) can sometimes be trusted to infer surface values. Spatial variability analyses show that TEMPO precision requirements are adequate to detect air quality violations.Model representation of diurnal evolution in profile structure is expected to be an important source of error for TEMPO retrievals.Pandora validation of TEMPO diurnal variability would appear to require no more than a couple of instruments in a given metropolitan area.Formaldehyde shows promise as a proxy for ozone production.Validation of AERONET AAOD reveals a high bias that may be responsible for overprediction of black carbon by global models.Strong correlations are observed between AOD and PM2.5, but the scaling factors vary widely based on BL depth, humidity, and aerosol composition (hygroscopicity).Reactive nitrogen chemistry in air quality models needs to be improved.Ozone can be highest over unmonitored areas downwind (e.g., Chesapeake Bay).A single lidar may be sufficient to characterize the vertical distribution of aerosols over distances of up to 100 km.

AOD Spatial Variability Chu et al., 2015 proposed the following relationship for predicting surface PM2.5 given AOD observations from satellite, HSRL, AERONET, etc.:ta :AOD at 0.55 mmf(RH): hydration factor (function of relative humidity) Aerosol extinction cross section per unit dry massHLH: Haze layer height = top of BuLD. Allen Chu, JCET/UMBC Max aerosol gradientPBL height

Please Consider Joining Us and Using Our Data doi:10.5067/Aircraft/DISCOVER-AQ/Aerosol- TraceGashttp://discover-aq.larc.nasa.gov/ http://www-air.larc.nasa.gov/missions /discover-aq/discover-aq.html