Jim Crawford1, Ken Pickering2, Lok Lamsal2, Bruce Anderson1, Andreas Beyersdorf1, Gao Chen1, Richard Clark3, Ron Cohen4, Glenn Diskin1, Rich Ferrare1, Alan Fried5, Brent Holben2, Jay Herman6, Ray Hoff6, Chris Hostetler1, - PowerPoint PPT Presentation

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PPT – Jim Crawford1, Ken Pickering2, Lok Lamsal2, Bruce Anderson1, Andreas Beyersdorf1, Gao Chen1, Richard Clark3, Ron Cohen4, Glenn Diskin1, Rich Ferrare1, Alan Fried5, Brent Holben2, Jay Herman6, Ray Hoff6, Chris Hostetler1, PowerPoint presentation | free to download - id: 7cf7bc-NzY4Z



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Jim Crawford1, Ken Pickering2, Lok Lamsal2, Bruce Anderson1, Andreas Beyersdorf1, Gao Chen1, Richard Clark3, Ron Cohen4, Glenn Diskin1, Rich Ferrare1, Alan Fried5, Brent Holben2, Jay Herman6, Ray Hoff6, Chris Hostetler1,

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Challenges and opportunities for remote sensing of air quality: Insights from DISCOVER-AQ Jim Crawford1, Ken Pickering2, Lok Lamsal2, Bruce Anderson1, Andreas ... – PowerPoint PPT presentation

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Title: Jim Crawford1, Ken Pickering2, Lok Lamsal2, Bruce Anderson1, Andreas Beyersdorf1, Gao Chen1, Richard Clark3, Ron Cohen4, Glenn Diskin1, Rich Ferrare1, Alan Fried5, Brent Holben2, Jay Herman6, Ray Hoff6, Chris Hostetler1,


1
Challenges and opportunities for remote sensing
of air quality Insights from DISCOVER-AQ
Jim Crawford1, Ken Pickering2, Lok Lamsal2, Bruce
Anderson1, Andreas Beyersdorf1, Gao Chen1,
Richard Clark3, Ron Cohen4, Glenn Diskin1, Rich
Ferrare1, Alan Fried5, Brent Holben2, Jay
Herman6, Ray Hoff6, Chris Hostetler1, Scott
Janz2 , Mary Kleb1, Jim Szykman7, Anne Thompson2,
Andy Weinheimer8, Armin Wisthaler9, Melissa
Yang1, Jay Al-Saadi1 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
http//discover-aq.larc.nasa.gov/
2
Thanks to Partners
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
Research National Science Foundation National
Oceanic and Atmospheric Administration National
Park Service University of Maryland, College
Park Howard University University of California,
Davis University of California,
Irvine University of Houston Rice University
University of Texas Baylor University
Princeton University of Colorado-Boulder
Colorado State University
3
Investigation Overview
Deriving Information on Surface Conditions from
Column and VERtically Resolved Observations
Relevant to Air Quality
A NASA Earth Venture campaign intended to improve
the interpretation of satellite observations to
diagnose near-surface conditions relating to air
quality
Objectives 1. Relate column observations to
surface conditions for aerosols and key trace
gases O3, NO2, and CH2O 2. Characterize
differences in diurnal variation of surface and
column observations for key trace gases and
aerosols 3. Examine horizontal scales of
variability affecting satellites and model
calculations
4
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.
Three major observational components
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
5
Deployment Locations
Maryland, July 2011
Houston, September 2013
California, Jan-Feb 2013
Colorado, Jul-Aug 2014
6
Predicted NO2 Column Behavior
Taken from Fishman et al., BAMS, 2008
7
Predicted NO2 Column Behavior
NO2NOx
NO2 Column
Photochemical Loss
Emissions
Taken from Boersma et al., JGR, 2008
8
Pandora Statistics-Maryland
1 x 1015 mol/cm2 0.037 DU
Median and inner quartile values plotted
9
P-3B Average Profiles-Maryland
10
Pandora Statistics-Houston
1 x 1015 mol/cm2 0.037 DU
Median and inner quartile values plotted
11
Pandora Statistics-California
1 x 1015 mol/cm2 0.037 DU
Median and inner quartile values plotted
12
P-3B Profile Statistics-California Urban sites
BakersfieldFresno
13
P-3B Profile Statistics-California Sub-Urban sites
14
Pandora Statistics-Colorado
1 x 1015 mol/cm2 0.037 DU
Median and inner quartile values plotted
15
Summary
  • 1. DISCOVER-AQ has collected a dataset of
    unprecedented detail on the diurnal trends in air
    quality as it is discerned from in situ and
    remote sensing methods.
  • 2. NO2 columns exhibit both unexpected and
    diverse diurnal trends that are consistent with
    vertically resolved profiles.
  • 3. NO2 tropospheric column retrievals are highly
    sensitive to diurnal variation in a-priori
    profile shapes.
  • 4. Next analysis steps include looking beyond
    median statistics.

16
Implications for TEMPO (1 of 2)
  • An airborne validation campaign is probably
    beyond TEMPOs budget
  • D-AQ core budget 30M for 5-year, 4-campaign
    study partner contributions added 10M
  • Typical RA-directed campaign budgets 15-20M
    (3-yr total)
  • 1-month GeoTASO/GCAS deployment 1M for flights
    data processing
  • Theres no guarantee that an RA airborne
    campaign will take place over North America
    during TEMPO prime mission
  • NASA RA led airborne campaigns for atmospheric
    composition historically occur every 2-4 years
    have to take turns with other science focus areas
  • Competing resource demands, within atmospheric
    composition and with EV-S

17
Implications for TEMPO (2 of 2)
  • So what do we do?
  • Science studies related to TEMPO observations are
    a logical priority for tropospheric chemistry
    programs post-launch
  • Analysis of DISCOVER-AQ, KORUS-AQ, TROPOMI and
    other data sets will help clarify priorities for
    pre- post-launch airborne campaigns related to
    TEMPO
  • Spatial representativeness, diurnal influences on
    products, vertical profile shapes,
  • Get in line and build advocacy NASA campaign
    concepts typically start as community grass-roots
    efforts leading to development of white papers
  • Consider Earth Venture proposal?
  • Continue preparatory activities as funding
    permits (GEO-CAPE, HQ RA, possibly HQ Applied
    Science, possibly TEMPO)

18
Thoughts from Jim Crawford on Possible Approaches
  • There are at least two initial approaches to
    take.  
  • One is to leverage what we have learned from
    DISCOVER-AQ, KORUS-AQ, etc. to define a field
    campaign to support TEMPO.  In this case, you
    would hope to be able to get airborne as soon as
    possible after TEMPO launches.  
  • Another approach would be to get the right
    surface measurements in place and use them to
    identify the locations where TEMPO needs the most
    help. This would delay a field campaign in favor
    of getting a chance to evaluate TEMPO performance
    using ground obs, sondes, Pandora, TROPOMI,
    etc.  Such a campaign would hopefully be more
    targeted on TEMPO performance, but would also
    hope to see TEMPO observations continue beyond
    the initial 2-years to take advantage of what is
    learned.

19
Backups
20
P-3B Profile Statistics-California Urban sites
BakersfieldFresno
21
Pandora vs Surface-Colorado
22
Observed NO2 Column Behavior
Taken from Tzortziou et al., JGR, 2014
23
CMAQ model Three simulations CMAQ model Three simulations CMAQ model Three simulations CMAQ model Three simulations
Horizontal resolution 4 km x 4 km 4 km x 4 km 4 km x 4 km
Vertical levels 45 (surface-100 hPa) 45 (surface-100 hPa) 45 (surface-100 hPa)
Domain Washington-Baltimore Washington-Baltimore Washington-Baltimore
Chemical mechanism CB05 CB05 CB05
Aerosols AE5 AE5 AE5
Dry deposition M3DRY M3DRY M3DRY
Vertical diffusion ACM2 ACM2 ACM2
Chemical and initial boundary condition RAQMS 12 km x 12 km RAQMS 12 km x 12 km RAQMS 12 km x 12 km
Biogenic emissions Calculated within CMAQ with BEIS Calculated within CMAQ with BEIS Calculated within CMAQ with BEIS
Biomass burning emissions FINNv1 FINNv1 FINNv1
Lightning emissions Calculated within CMAQ Calculated within CMAQ Calculated within CMAQ
Anthropogenic emissions NEI-2005 projected to 2012 NEI-2005 projected to 2012 NEI-2005 projected to 2012
1. ACM2 Base 2. ACM2 Mod 3. YSU Mod
PBL scheme ACM2 ACM2 YSU
Mobile emissions Standard Reduced 50 Reduced 50
Alkyl nitrate photolysis Standard 10 times faster 10 times faster
  • Two PBL schemes selected based on the study by
    Clare Flynn
  • Emissions and photolysis rate changed based on
    Anderson et al., 2014 and Canty et al., 2014

24
Remote Sensing Column Air Mass Factor
Sensitivity Observations and Methods
  • Location Padonia, Maryland
  • Observation period 3-4 spirals for 14 days in
    July 2011 (Hours covered 6 AM 5 PM, local time)
  • NO2 observations
  • Aircraft (P3B) measurements (200 m - 4 km) NCAR
    data (accuracy better than10)
  • Surface measurements by photolytic converter
    instrument (accuracy better than 10)
  • Spatial resolution comparable between model
    (4x4km) and spiral (radius 4km)
  • Observed PBL heights Estimation based on
    temperature, water vapor, O3 mixing ratios, and
    RH (Donald Lenschow)
  • Methods
  • Model and surface measurements sampled for the
    days and time of aircraft measurements
  • Spiral data sampled at model vertical grids

25
Comparison of NO2 profiles and shape factors
Filled circles observed grid average Open
circles linear interpolation in log space Error
bars standard deviation
fi? NO2 shape factors Oi? partial column wi?
scattering weights (VLIDORT)
AMF (Observation) 1.94 AMF (Base) 2.16 AMF
(ACM2 Mod) 2.3 AMF (YSU Mod) 1.8
26
Errors in AMFs/retrievals from a-priori NO2
profiles
  • AMF calculated for ACAM (air-borne spectrometer
    located at 8km) but is also relevant for
    tropospheric NO2 column retrievals

wi? scattering weights fi? NO2 shape factors
Surface reflectivities 0.1 to 0.14 at 0.01
steps Solar zenith angles 10 to 80 at 10
steps Aerosol optical depths 0.1 to 0.9 at 0.1
steps
27
NO2 profiles and AMFs (11 AM)
AMF (Observation) 1.94 AMF (Base) 2.16 AMF
(ACM2 Mod) 2.3 AMF (YSU Mod) 1.8
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